- 1. Introduction
- 2. Stability is a fundamental prerequisite for crystallization
- 3. Stability of the protein on a structural level
- 4. Protein stability during protein expression and purification
- 5. Key techniques for determining protein stability
- 6. Improving protein stability and the concept of crystallizability
- 7. Future outlook
- 1. Introduction
- 2. Stability is a fundamental prerequisite for crystallization
- 3. Stability of the protein on a structural level
- 4. Protein stability during protein expression and purification
- 5. Key techniques for determining protein stability
- 6. Improving protein stability and the concept of crystallizability
- 7. Future outlook
Protein stability: a crystallographer's perspective
aStanford ChEM-H, Macromolecular Structure Knowledge Center, Stanford University, Shriram Center, 443 Via Ortega, Room 097, MC5082, Stanford, CA 94305-4125, USA, bLaboratory of Cell and Molecular Biology, National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institutes of Health (NIH), Building 8, Room 1A03, 8 Center Drive, Bethesda, MD 20814, USA, cDepartment of Forensic Crystallography, k.-k. Hofkristallamt, 91 Audrey Place, Vista, CA 92084, USA, and dDepartment of Genetic Epidemiology, Medical University of Innsbruck, Schöpfstrasse 41, A-6020 Innsbruck, Austria
*Correspondence e-mail: email@example.com
Protein stability is a topic of major interest for the biotechnology, pharmaceutical and food industries, in addition to being a daily consideration for academic researchers studying proteins. An understanding of protein stability is essential for optimizing the expression, purification, formulation, storage and structural studies of proteins. In this review, discussion will focus on factors affecting protein stability, on a somewhat practical level, particularly from the view of a protein crystallographer. The differences between protein conformational stability and protein compositional stability will be discussed, along with a brief introduction to key methods useful for analyzing protein stability. Finally, tactics for addressing protein-stability issues during protein expression, purification and crystallization will be discussed.
The main purpose of this review is to introduce the reader to the concepts of protein stability from the viewpoint of a structural biologist, a structural biologist being defined as a scientist who determines the detailed molecular structure of a protein using methods such as crystallography, NMR spectroscopy or cryo-EM. Particular emphasis will be given to crystallographic techniques, as protein stability, or the lack thereof, represents a substantial challenge in the crystallization of many proteins. Protein stability is a wide-ranging topic including aspects of physical chemistry, thermodynamics, entropy, computational chemistry, protein folding and dynamics. For the purposes of this review, many of the computational and theoretical aspects are skipped over and the reader is referred to other excellent reviews on this topic (Compiani & Capriotti, 2013; Lazaridis & Karplus, 2002).
Stability is the potential of a pattern to survive over time, and therefore is integral to our understanding of biological systems and their evolution (Schrödinger, 1945). Clearly, the exact meaning of a `pattern' for a protein molecule is somewhat vague, but we know that processes such as protein unfolding, denaturation, degradation, conformational change, enzymatic modification and proteolytic cleavage may transform this `pattern'. These transformations are generally considered, or analyzed, with respect to the integrity of the primary and conformational structure of the fully folded protein. Additionally, protein stability means different things to different people. For example, a pharmacologist, biotechnologist or food scientist may primarily consider the half-life of a protein's activity as a measure of its stability. However, a protein chemist or a structural biologist may concern themselves with changes in the primary, secondary, tertiary or quaternary structure of a protein as a measure of its stability. Again, for the purposes of this review, we will focus on the structural aspects of protein stability and will refer the reader to other excellent reviews on protein stability from a pharmacological and biotechnological perspective (Hall, 2014).
We will first discuss protein stability as a fundamental prerequisite for crystallization (§2) and then some important aspects of stability on a higher, structural level (see §3). At this stage it is important to discuss the differences between thermodynamic protein stability and conformational protein disorder, especially given some of the unique parameters that structural biologists use to describe and analyze their structures. For example, NMR spectroscopists often report root-mean-squared deviation (r.m.s.d.) values between their ensemble structures, whereas crystallographers report B factors as a measure of the positional uncertainty in a given protein crystal structure model. Both of these parameters represent displacements and disorder within a structure and can be reflective of the level of conformational stability. We will then discuss some important factors to consider when expressing and purifying proteins for structural studies. Structural genomics efforts have alleviated many of the bottlenecks of a traditional structure-determination pipeline, but researchers are still all too aware of the difficulties of expressing and purifying challenging protein targets. Careful consideration of the primary structure, construct design, expression conditions and hosts cells can all be used to mitigate many of the protein-stability issues observed during expression and purification (see §4). We will then discuss some common methods used to analyze protein stability, with a focus on methods routinely used to asses protein stability, including protein melting temperature analysis (Tm), NMR and cryo-EM (see §5).
Biomolecular crystallization can be described as the self-organization of macromolecules into a translationally periodic arrangement with long-range order. In order to achieve this goal optimally, the moieties within each asymmetric unit of a crystal should be of the same kind and of the same shape. If a protein cannot form such stable entities per se, a fundamental and primary requirement for crystallization is not met, and no effort to find suitable thermodynamic and kinetic conditions will lead to crystals of such a protein construct (Fig. 1a).
It is important to note that from a crystallization perspective, there are at least two major flavors of protein stability: compositional stability and conformational stability (Table 1). The crystallographer must carefully assess both types of stability in order to enable crystallization of the target protein.
During the processes of crystallization it is essential to maintain the same species within the crystallization experiment; there needs to be some form of compositional stability. On a simple level this means that the protein molecules must have the same chemical makeup. The chemical homogeneity of a sample can often be determined using mass spectrometry or an SDS–PAGE gel. Compositional homogeneity is typically compromised by post-translational modifications, such as glycosylation and proteolysis, which can affect the primary structure of the protein molecules and generate compositional variability (see §3.1). Because protein crystallization takes time, the primary requirement for compositional stability must be maintained over a period of time, and preferably within a reasonable range of environmental conditions. It is important to note that there is no such thing as absolute stability. For example, a protein that is compositionally stable enough to produce a single band on an SDS–PAGE gel may still not be stable enough over the timeframe of a crystallization experiment.
Assuming that the protein sample has a degree of compositional homogeneity, it will still likely not crystallize unless it possesses conformational stability. A large number of proteins fall into the category of conformationally disordered proteins displaying little or no conformational order (Longhi et al., 2010). A protein with substantial disordered regions, or separate domains exhibiting dynamic variability, will be less likely to self-organize into a crystal. This can be the case even if the sample has perfect compositional stability. The strict requirement for limited conformational variability is a unique problem that a crystallographer faces when trying to crystallize a protein sample. The problem is confounded by the fact that the conformation of flexible regions of a protein is a context-driven property. For example, conformations may be quite different in the cellular context, in an NMR tube or in a macromolecular crystal. While structural methods can be used to probe conformational homogeneity, it is important to realise that the results are only meaningful within the context and conditions of that particular method (see §5). For example, analysis of conformational stability and dynamics is often limited using crystallographic methods as the crystal packing can hinder such movements. In these cases NMR solution methods can provide complementary information.
Structures determined using X-ray crystallography provide limited information regarding the dynamics of the protein structure. Nonetheless, some dynamics information is included in the atomic model in the form of the atomic displacement parameter (ADP) or B factor. The B factor is expressed in units of Å2 and is essentially a statistical measure describing the probability of finding an atom at that particular mean position in the structure (Willis & Pryor, 1975). If the B factor of a particular atom is high then it suggests that the certainty of finding the atom at that position in the structure is low. Atoms in regions of high B factor can be displaced as a result of dynamic disorder of the polypeptide chain or as a result of short-range or long-range disorder within the crystal. Such flexible or dynamic regions can often be identified in a crystal structure and engineered out at the cloning stage to produce protein samples with better conformational stability (see §§4.1 and 6.1). Not only do such modifications result in better protein stability during expression and purification, but they also increase the probability that the molecules will pack within the crystal lattice in a more orderly fashion. As a consequence, such efforts often result in better diffracting crystals and higher-resolution X-ray data.
Comparison of ensembles of structures, as typically generated by NMR spectroscopy, can be used to provide a measure analogous to the crystallographic B factor in the form of a root-mean-squared deviation (r.m.s.d.) between corresponding atoms of the ensemble members. This measure can be used to assess the flexibility, dynamics, disorder or stereochemical variability across a set of structural models. The r.m.s.d. value is complementary to the crystallographic B factor and because the structure is in solution it is not perturbed or influenced by crystal packing (Sikic & Carugo, 2009).
A large class of proteins, referred to as intrinsically disordered proteins (IDPs), contain significant levels of conformational disorder and, in some cases, have no discernable three-dimensional structure at all. It is estimated that ∼40% of all human proteins contain at least one disordered segment and ∼25% are completely disordered (Uversky & Dunker, 2010). These proteins have largely been avoided by the crystallographic community owing to the expected difficulties in crystallizing them. However, NMR techniques have been central to unraveling how these unstructured proteins function. Such studies have led to a paradigm shift in our understanding of protein structure and function (Wright & Dyson, 1999). Traditional theory dictates that proteins function by adopting a rigid, preformed structure that binds to a target ligand or protein in a fashion analogous to a lock and key. However, NMR studies on IDP proteins such as CREB, p53 and 14-3-3 have revealed that these disordered regions allow plasticity and flexibility and often only form structure upon binding of the partner protein (Oldfield et al., 2008; Sugase et al., 2007; Mujtaba et al., 2004). These so-called `hub proteins' are capable of interacting with many different protein partners in a context-sensitive manner, and this is only possible as a result of the plasticity and initial lack of conformational stability. High-resolution crystal structures of complexes of these vital `hub proteins' will be essential for understanding their role in human diseases such as Parkinson's disease and Alzheimer's disease (Wang et al., 2011). Although the poor conformational stability of these proteins poses challenges for the protein crystallographer, in a cellular context IDPs offer many advantages over more traditional single-function folded proteins, including the ability to bind to many different protein partners (Liu & Huang, 2014).
In addition to IDPs, many proteins contain aggregation-prone regions (APRs) that typically contain a run of 5–15 amino acids with a propensity for forming extended β-sheet structures. For example, APR segments are observed in β2-microglobulin and are responsible for aggregation into amyloid fibers in diseases such as amyloidosis (De Baets et al., 2014). Another group of proteins referred to as intrinsically insoluble proteins (IIPs) are completely insoluble and cannot be refolded in traditional buffer solutions (Goyal et al., 2015; Liu & Song, 2009). For example, naturally occurring mutants of SH3, such as V22-SH3, are insoluble in the presence of ions, but they can be resurrected and solubilized in pure water, allowing further study of the unstructured proteins in solution using NMR spectroscopy (Liu & Song, 2009).
One simple way of conceptualizing protein stability from a structural perspective is to consider stability at each level of protein structure: primary structure, secondary structure, tertiary structure and quaternary structure (Table 2). Protein stability with respect to each of the structural levels will now be discussed in turn. Wherever possible, we will emphasize aspects of particular importance to the structural biologist, with a particular focus on protein crystallization.
The primary structure of the protein, or the sequence of the amino acids in the polypeptide chain, can be modified in several ways by post-translational modifications (PTMs). PTMs result in alteration of the structure and function of a protein and for this reason are central to any discussion of protein stability. As discussed above (see §2), PTMs can affect both compositional stability, as the modifications may be non-uniform or incomplete, and also conformational stability, as the modifications may affect protein disorder and dynamics. This is illustrated by glycoproteins, which are often not uniformly glycosylated at all possible glycosylation sites, therefore leading to compositional heterogeneity. Furthermore, complex hydrocarbon chains tend to have a greater degree of conformational freedom. This conformational freedom results in an increase in disorder on the protein surface, while at the same time shielding polar or charged residues on the protein surface required for intermolecular crystal contact formation (see §6.6). Although the heterogeneity of glycosylation tends to impair crystallization, its variability can have important functional implications for a protein.
PTMs are the result of many different changes to the primary structure of a protein, including proteolytic processing, protein splicing and the addition of other functional groups to the amino acids. PTMs are often used for targeting of the protein to a specific region of the cell or modification of the activity or specificity of an enzyme. For example, functional groups such as myristate, palmitate, isoprenoid and glycosylphosphatidylinositol (GPI) are often attached to the protein and used for targeting of the protein to the membrane (Chatterjee & Mayor, 2001). Other functional groups such as carboxylate (Walker et al., 2001), ethanolamine phosphoglycerol (Whiteheart et al., 1989) and hypusine (Park et al., 2010) can be added to proteins to regulate their activity. Additionally, larger peptides and proteins can also be covalently added to proteins, including ubiquitin (Komander & Rape, 2012), SUMO (Hay, 2005), ISG15 (Malakhova et al., 2003), PUP (Striebel et al., 2014) and NEDD (Rabut & Peter, 2008). Of the 821 182 proteins that were experimentally analyzed by Khoury and coworkers, the top ten observed PTMs are phosphorylation (58383), acetylation (6751), N-linked glycosylation (5526), amidation (2844), hydroxylation (1619), methylation (1523), O-linked glycosylation (1133), ubiquitylation (878), pyrrolidone carboxylic acid (826) and sulfation (504) (https://bit.ly/1jdfXR8 ; Khoury et al., 2011). Key methods used to analyze and identify changes at the primary-structure level include mass spectrometry and Eastern and Western blots (Liu et al., 2014; Towbin et al., 1979; Table 2).
It is important to note that many PTMs play a role in stabilizing proteins, particular with respect to the half-life and turnover of the protein within the cell. For example, PTMs such as ubiquitination target proteins to the proteasome for degradation and recycling, therefore directly affect the half-life of the protein and its stability within the cell (Komander & Rape, 2012). A myriad of other PTMs exist, including acylation, alkylation, arginylation, butyrylation, malonylation, ADP-ribosylation, iodination, oxidation, succinylation, S-nitrosylation, S-glutathionylation and glycosylation. Currently, just under 500 PTMs have been identified in the SWISS-PROT and TrEMBL databases (for a full list, see https://bit.ly/1P6Rbj3 ). All of these modifications play a role in the structure and the function of the target protein. However, some PTMs, such as proteolytic cleavage and protein splicing, significantly influence protein structure at the primary level and can lead to drastic changes in compositional stability.
Protein splicing occurs in proteins called inteins (or protein introns), which are a large class of self-cleaving proteins found in all domains of life (Paulus, 2000; Novikova et al., 2014). One of the first examples identified was the VMA1 protein, a yeast vacuolar membrane H+-ATPase, which was shown to undergo protein splicing (Hirata et al., 1990). Protein splicing is a naturally occurring process analogous to the splicing of introns from RNA. A precursor polypeptide is processed into a mature and functional protein. The intein is autocatalytically excised from the precursor protein and the flanking exteins are ligated together, producing two new polypetides (Mills et al., 2014). Inteins are of great importance for the stability of proteins, but they are also of interest from a protein-engineering perspective (Aranko et al., 2014). For example, inteins can be used for the preparation of isotope-labeled proteins for NMR spectroscopy, for site-specific fluorescent labeling and as self-cleaving affinity purification tags such as cSAT and intein-CDB (commercially available as the IMPACT system from NEB; Chong et al., 1997; Volkmann & Iwaï, 2010; Lin et al., 2015; see §4.1).
Protein secondary structure is the localized three-dimensional structure of the polypeptide chain. Secondary structure can be described in terms of the pattern of hydrogen bonding between amide H atoms and carbonyl O atoms of the backbone (Pauling et al., 1951) or by the stereochemistry adopted by the polypeptide backbone (Ramachandran et al., 1963). On a somewhat simplified level, the primary driving forces behind the formation of secondary structure, and in turn tertiary structure, are hydrogen bonding and hydrophobic interaction (Pace, Scholtz et al., 2014; see §3.3).
The α-helix is the predominant type of secondary structure, accounting for approximately one-third of all secondary-structure elements (Stickle et al., 1992). Analysis of the first crystal structures suggested that certain residues including alanine, leucine and glutamate are found frequently in α-helices. In contrast, other residues such as proline, glycine and aspartic acid are found less frequently (Davies, 1964; Prothero, 1966; Guzzo, 1965). This information has been used to develop many algorithms for the prediction of protein secondary structure, including the popular Chou and Fasman method (Chou & Fasman, 1974). Secondary-structure propensity data have been expanded using mutagenesis data, and tables of α-helical (Pace & Scholtz, 1998) and β-sheet (Smith et al., 1994) propensity have been compiled.
One overwhelming consensus of the amino-acid propensity rules is the destabilizing effect that proline has on the α-helix (ΔΔG of 3.16 kcal mol−1 cf. alanine at 0 kcal mol−1; Pace & Scholtz, 1998; see §3.3). This destabilization is a result of the missing backbone amide H atom, which prevents proline from participating in stabilizing hydrogen bonding. Additionally, the bulky cyclic side chain of proline results in a ∼30% kink in the α-helix backbone as a result of steric hindrance (Richardson, 1981; Yun et al., 1991). Glycine has the next lowest propensity for forming α-helices as a result of enhanced conformational flexibility upon folding to form an α-helix (Hermans et al., 1992). It is important to note that many of these secondary-structure propensities are highly context-dependent. For example, proline occurs widely in the transmembrane helices of integral membrane proteins and has been shown to have a stabilizing effect on α-helices in such environments (Li et al., 1996).
Clearly, such findings are in support of the hypothesis that the stability of the folded protein is largely dictated by the amino-acid composition and, as such, the primary structure results in a unique, kinetic minimum of free energy as first suggested by Anfinsen (1973). These simple principles have been expanded into complex algorithms that can be used to design both stable α-helices and β-sheets (Jiménez, 2014; Yakimov et al., 2014). Furthermore, comparative modeling can be used to design proteins with a greater degree of thermal stability, and similar models can be used to predict the crystallizability of a protein (Olson et al., 2015; Smialowski & Frishman, 2010; see §6).
The tertiary structure of a protein is the overall shape, or fold, adopted by the polypeptide chain. Many factors affect the process of protein folding, including conformational and compositional stability, cellular environment including temperature and pH, primary and secondary structure, solvation, hydrogen bonding, salt bridges, hydrophobic effects, van der Waals (vdW) forces, ligand binding, cofactor binding, ion binding, chaperones and PTMs, to name just a few.
The conformational stability of the polypeptide chain results in a significant entropic penalty (−TΔS >> 0), and under normal cellular conditions a folded protein is only marginally stable (∼10 kcal mol−1 for a 10 kDa protein; Fig. 1b). In order to overcome this entropic penalty, all of the other factors influencing protein folding must outweigh the loss of conformational entropy (Dill, 1990). A series of studies by Pace and coworkers have recently quantified some of these influences (Pace et al., 2011; Pace, Fu et al., 2014; Pace, Scholtz et al., 2014). These studies suggest that the hydrophobic effect contributes ∼60% to the stability of the protein and hydrogen bonding contributes ∼40% (Pace et al., 2011). Specifically, the burial of a single methyl group contributes ∼1.1 kcal mol−1 to net protein stability and loss of its conformational entropy contributes ∼2.4 kcal mol−1 to net protein instability (Pace et al., 2011). The net contribution of hydrogen bonding to overall protein stability is also ∼1.1 kcal mol−1 and is largely independent of the size of the protein (Stickle et al., 1992; Pace, Scholtz et al., 2014). However, in contrast, hydrophobic interactions typically contribute less to the stability of small proteins (Pace et al., 2011; Pace, Fu et al., 2014).
The stability of the protein fold is of particular interest for the design of thermally stable proteins for industrial uses such as biofuel production and as proteases for laundry detergents. Thermophilic organisms such as Thermotoga maritima, which thrives in hot deep-sea vents in the Sargasso Sea, require proteins that maintain fold and structure under such extreme conditions. The study of these thermophilic proteins suggests that the protein structures are similar to their mesophilic counterparts and thermal stability is inferred by subtle changes in the amino-acid composition. On comparing thermophilic proteins with their mesophilic counterparts, certain patterns are observed including an increase in the number of salt bridges, an increase in hydrophobicity and an increase in the number of aromatic residues (Dekker et al., 1991; Tanner et al., 1996; Zhou et al., 2008; Fields et al., 2015; Somero, 2004).
In contrast to the IDPs discussed above (see §2.2), the stability of the protein tertiary structure is often considered to be essential for the maintenance of protein function. However, many proteins undergo an overall change of protein fold as part of their mechanism of action. For example, serine protease inhibitors (serpins) undergo a transformation from a long-term stable native form (S, stressed) into a more stable folded form (R, relaxed) upon interaction with the proteinase (Whisstock & Bottomley, 2006; Whisstock et al., 2000). These structural rearrangements include the insertion of a loop into the center of a core β-sheet or the insertion of a β-strand to form a domain-swapped dimer that can initiate polymerization (Mottonen et al., 1992; Yamasaki et al., 2008; see Fig. 2c). Large conformational changes such as this are commonplace and are observed in many proteins including influenza virus hemagglutinin, lymphotactin, Mad2 spindle checkpoint protein and chloride intracellular channel 1 (CLIC1; Bryan & Orban, 2010). Therefore, it is important that any discussion on stability carefully considers the mechanism of the protein under study, as some protein folds are designed to be inherently unstable.
Quaternary structure is the arrangement of the folded protein subunits into a multi-subunit complex. The stability of such complexes is of importance for the regulation of allostery and cooperativity, which often results from the conformational changes within individual polypeptide chains. One of the classic models used to describe allosteric transitions in proteins is the Monod–Wyman–Changeux (MWC) model (Monod et al., 1965). In this model proteins may exist in one of two states: tense (T) and relaxed (R). One of the key features of this model is that ligands may bind to either the T or R state with equal affinity, but if the R state is preferred then the affinity will be increased. However, if binding to the T state is favored then the affinity is decreased and the substance is described as an allosteric modulator. One of the best-studied examples is hemoglobin, with the R state representing deoxyhemoglobin and the T state representing oxyhemoglobin (Brunori, 2014; Ronda et al., 2013). As discussed above for IDPs and metastable proteins (see §§2.2 and 3.3), it is not sufficient to consider protein stability in isolation from function. Many proteins undergo large conformational changes involving both secondary and tertiary structure, and each state may have a different conformational or compositional stability (Fig. 2).
Modulation of the quaternary structure, and more specifically the protein–protein interactions responsible for quaternary structure, has long been a goal for the pharmaceutical industry. Such efforts present considerable challenges as a result of the large surface areas involved. Small molecules that destabilize protein–protein interactions have been demonstrated, but stabilizing examples are somewhat scarce (Giordanetto et al., 2014; Wells & McClendon, 2007). Destabilizing examples include the Abbott drug Navitoclax, which destabilizes the interaction between the anti-apoptotic protein Bcl-2 and Bad/Bid/Bak (Oltersdorf et al., 2005), and the Roche drug Nutlin-3, which inhibits the interaction between the tumor suppressor p53 and MDM2 (Secchiero et al., 2011). Examples of compounds that stabilize protein–protein interactions include natural products such as cyclosporin A, which stabilizes the interaction between calcineurin and cyclophin (Huai et al., 2002), and the drug Tafamidis, which binds to a pocket at the interface of the transthyretin dimer (Bulawa et al., 2012) (see Figs. 2a and 2b, respectively). In the case of Tafamidis, stabilization of the dimerized form of transthyretin prevents the aggregation and misfolding which has been shown to be the mechanism leading to transthyretin amyloidosis diseases such as peripheral neuropathy.
The stability of the protein during the course of expression and purification is often an issue. In order to obtain sufficient quantities of protein for crystallization screening, formulation, vaccine development or therapeutic use, it is essential that intact, stable and folded protein is produced. Many proteins are unstable, unfolded or proteolytically cleaved under the conditions used for protein expression; again it is important to emphasize that these factors can lead to poor stability on both the conformational and compositional levels (see §2 and Table 1). Factors giving rise to poor protein stability during expression and purification may include the primary structure of the protein, the construction of the recombinant expression plasmid, the temperature and the expression medium used and the toxicity of the protein to the host organism (Table 3). Therefore, there are many factors to test during expression and purification, and combinatorial design approaches are often used, in combination with high throughput methods to find the appropriate combination of conditions (Papaneophytou & Kontopidis, 2014). The use of advanced genetic engineering methods to modify both cells and expression plasmids is covered in more detail in reviews such as Sørensen & Mortensen (2005).
The design of the expression construct is one of the primary decisions that a structural biologist must make to ensure the efficient expression and purification of the target protein. For example, the compositional stability of a protein can be severely affected by the presence of protease cleavage sites within the target protein. These sites can result in cleavage of the target protein by endogenous proteases produced by the expression host. Proteolysis can be mitigated by the removal of cleavage sites from the expression construct during recombinant assembly. For example, such an approach was used to remove two protease sites from a malarial vaccine candidate that was proteolytically degraded by endogenous KEX2 protease during expression. Removal of these sites enabled the production of full-length protein on a large scale (Spiegel et al., 2015). Additionally, the primary structures of some proteins are inherently unstable, with unusually short half-lives. For example, proteins containing sequences rich in proline, glutamate, serine and threonine (PESTs) often have half-lives of less than 2 h (Rogers et al., 1986). It is suggested that PEST sequences target the protein for intracellular degradation via the proteasome machinery (Spencer et al., 2004) or via more traditional proteolysis pathways utilizing calpain (Shumway et al., 1999). Furthermore, the N-end rule is a strong predictor of protein half-life in vivo. For example, if the N-terminal residue of a protein is methionine, serine, alanine, glycine, threonine, valine or proline the half-life is stabilized (>20 h). In contrast, if the N-terminal residue is phenylalanine, aspartate, lysine or arginine the half-life of the protein is destabilized (<3 min; Bachmair et al., 1986). Proteins destabilized in this way are targeted for degradation via the ubiquitination pathway; therefore, the N-end rule is of no concern when using bacterial expression systems. It is important to note that the N-terminal residue is `masked' by the inclusion of an N-terminal affinity purification tag, as is typically used in most laboratories today.
In addition to the half-life stabilizing effects of affinity purification tags, they are also of considerable interest for improving the solubility of a target protein (Amarasinghe & Jin, 2015; Wood, 2014). This is particularly true for maltose-binding protein (MBP), which has a strong effect in solubilizing the protein to which it is attached. MBP has also been shown to promote the correct folding of the protein target to which it is attached, suggesting that it can act as a form of molecular chaperone (Kapust & Waugh, 1999). Other chaperones that can be used to assist protein stability and folding during expression include DnaK and GroEL (Kyratsous & Panagiotidis, 2012). Some proteins are simply not stable in the cytoplasm and they can be redirected to other compartments of the host cell using an affinity tag. For example, the pMal vector system (NEB) incorporates the malE signal sequence and can be used to direct the protein of interest across the plasma membrane and into the periplasm. This has the added advantage of keeping the target protein away from cytoplasmic proteases during subsequent purification steps, thus further enhancing the compositional stability.
Self-cleaving affinity purification tags can be applied to carefully control the compositional stability. Traditional affinity-tag removal procedures often use proteases, such as thrombin or factor Xa, which can result in nonspecific degradation of the target protein. However, the use of highly specific self-cleaving tags prevents this issue as exogenous protease addition is not required. In addition to the self-cleaving intein-based tags discussed above (see §3.1), it has also been reported that nickel ions can be used to cleave an affinity tag. In the example of the GmSPI-2 inhibitor structure the peptide bond preceding the serine or threonine residue in the (S/T)XHZ motif was cleaved by nickel ions (Kopera et al., 2014; Krężel et al., 2010).
In order to ensure proper protein folding and stability, it is essential that the expression host is provided with the necessary prosthetic groups, cofactors and ligands as required by the target protein. Many of these are provided by the expression medium and are scavenged by the host cells during the course of expression. However, some cofactors and prosthetic groups cannot be synthesized by the host cell and others are not available in sufficient quantities. For example, heme incorporation is often low when heme-containing proteins are expressed in Escherichia coli. In these cases the expression medium must be supplemented with δ-aminolevulinate in order to achieve satisfactory levels of heme incorporation (Kery et al., 1995). Additionally, the solubility of the protein can also be significantly improved during expression by the addition of additives such as trehalose, glycine betaine, mannitol, L-arginine, potassium citrate, CuCl2, proline, xylitol, NDSB 201, CTAB and K2PO4 (Leibly et al., 2012; see §6.2 for more on buffer screening).
Varying the temperature at which the expression is carried out can also be used to control protein stability and solubility. For example, cold-shock induction systems such as pCold (Takara/Clontech) can be used to improve the overall stability of the target protein (Qing et al., 2004). As an added benefit, at lower temperatures, cell proliferation is halted and the expression of endogenous proteins such as proteases is reduced. Therefore, the target protein is further protected from degradation and purity is improved. To assist in low-temperature expression, cold-adapted E. coli cells, for example ArcticExpress (Agilent Technologies), have been developed. These cells co-express the cold-adapted chaperonins Cpn10 and Cpn60 from the psychrophilic bacterium Oleispira antartica (see §4.3 for more on host-cell selection).
The choice of the host cells that are used for the expression of recombinant proteins has an important influence on protein stability. For example, the protein may be toxic to the host cell or the protein may be cleaved by endogenous proteases made by the cell. Clearly, such issues can be a primary source of compositional instability in proteins. Toxicity can be controlled by tight regulation of the expression level using promoters that respond in a concentration-dependent manner to the inducer. Examples of tightly controlled expression vectors include pBAD, which responds to the inducer L-arabinose (Guzman et al., 1995). The background expression of proteases (and proteins in general) can also be controlled using plasmids that express T7 lysozyme, such as pLysS. T7 lysozyme is a natural inhibitor of T7 RNA polymerase, the promoter utilized in the pET vector system, and can be used to reduce background levels of protein expression (Studier, 1991). Background levels of protease activity can also be reduced using OmpT− bacterial strains, which do not express the outer membrane aspartyl protease. Such systems are commercially available as BL21 Star strains of E. coli (Invitrogen/ThermoFisher Scientific).
Host-cell selection is particularly important when expressing mammalian proteins in bacterial cells, as the codon usage between the organisms is different. For example, the AGA codon for arginine is particularly rare in E. coli and can result in premature chain termination, frame-shifting and incorrect amino-acid insertion (Calderone et al., 1996, Kane, 1995). This issue can be addressed in a number of ways, including the generation of a synthetic gene reflecting the codon usage of the host organism or by co-transformation of the host with a plasmid that can provide the tRNA of the missing codons (e.g. CodonPlus, Stratagene and pRARE; EMD Millipore/Novagen; Dieci et al., 2000; Fu et al., 2007). Competent E. coli BL21 cells containing the pRARE plasmid are commercially available under the trade name Rosetta. These cells have been used to optimize the expression of many human proteins in E. coli. For example, the Swedish Human Protein Atlas project has been successful in improving both the level of expression and the purity of proteins using the Rosetta E. coli strain (Tegel et al., 2010).
Finally, mammalian expression systems such as Chinese hamster ovary (CHO) cells (Fischer et al., 2015) and human embryonic kidney cells (e.g. HEK 293T and 293F; Nettleship et al., 2015) are often essential for the expression of mammalian or human proteins (for a discussion of the merits of the various expression systems, see Brondyk, 2009). In addition to addressing the codon-usage issue, expression in mammalian cells is often required to ensure that PTMs are correctly added and the protein is correctly folded and active (see §6.6 for a discussion of GnTI and lec1 glycosylation-deficient mammalian cells). Alternatively, insect cells such as Spodoptera frugiperda (e.g. Sf9 and Sf21) and Trichoplusia ni can be used (Altmann et al., 1999; Jarvis, 2009).
The relative merits of the three main structural methods for assessing protein stability are shown in Table 2. Given the solid-state nature of protein crystallography it is often difficult to crystallize dynamic and disordered proteins, and for this reason NMR spectroscopy has been one of the main tools used to study IDPs such as p53 and CREB (Brutscher et al., 2015; Dunker & Oldfield, 2015; Mujtaba et al., 2004; Fig. 2d). NMR is extremely useful for assessing both secondary and tertiary structure in dynamic and disordered systems (see §5.1). The higher resolution of crystal structures make them a particularly attractive method for determining changes at the primary-structure level. For example, uncharacterized PTMs, such as glycosylation, can often be interpreted directly from the electron-density maps if the experimental data are of sufficiently high resolution. Similarly, given sufficiently high-resolution maps, cryo-EM can be a powerful technique for determining protein stability and dynamics at the quaternary level (see §5.2).
NMR spectroscopy is a powerful method for the determination of the stability of proteins in solution (Bieri et al., 2011; Kwan et al., 2011). The method is highly complementary to X-ray structure analysis, but given its ability to analyze structures in the solution state it is of tremendous value for assessing protein conformational stability (Krishnan & Rupp, 2012).
The fact that NMR can readily distinguish between folded and unfolded proteins, and detect the presence of disordered and unstructured regions, makes it inherently useful as a diagnostic tool for crystallization experiments (Fig. 3a). Modern instruments can extract this information with minimal sample requirement (∼10 nM) and a simple one-dimensional proton NMR spectrum can provide information on the conformational stability of the macromolecule. Specifically, as a result of the principal inverse relation between spin–spin relaxation time and the peak width, large soluble aggregates will not yield an interpretable high-resolution NMR spectrum. For non-aggregated protein samples that yield usable one-dimensional NMR spectra, good discrimination in the backbone amide region below 8.3 p.p.m., as well as peaks at around ∼1 p.p.m., are indicative of folded protein (Rehm et al., 2002). Furthermore, two-dimensional heteronuclear single-quantum coherence (HSQC) NMR spectra can be used to analyze the difference between folded and unstructured protein (Fig. 3a) and also to compare apoprotein and ligand-bound complexes (Figs. 3b and 3c). Such a two-dimensional spectrum maps the backbone amide groups according to their 1H and 15N resonance frequencies. This method necessitates the production of 15N-labeled protein and requires larger amounts of sample compared with the more qualitative one-dimensional spectral analysis (Zhao et al., 2004).
One of the main benefits of NMR methods is that the effect of environmental conditions, such as pH, temperature or ligand binding, can be readily varied and studied in a near-native solution state. Additionally, the nondestructive nature of NMR spectroscopy means that the samples can also be used for subsequent crystallization experiments, and high-throughput structure-determination facilities often combine NMR screening with crystallization experiments.
The high conformational heterogeneity of proteins, especially of large multi-domain protein–protein complexes, can often hinder crystallization efforts. Mutational variants and different combinations of protein partners may need to be screened for suitability for crystallization. To facilitate this, electron microscopy (EM) can be used to directly visualize the sample and assess the level of heterogeneity. In the best-case scenario three-dimensional cryo-EM reconstructions can be generated, but this can be time-consuming and often requires substantial efforts in screening for suitable data-collection parameters. However, the generation of raw images of negatively stained particles is straightforward and requires very little protein (typically <10 µg). This method is already routinely practiced by EM practitioners to screen for good samples to move forward for cryo-EM analysis, but has recently been adapted for crystallography. However, the negative stain (e.g. uranyl formate) may introduce artifacts or otherwise disrupt the protein sample. Additionally, the protein (or protein–protein complex) needs to be relatively large (>150 kDa) in order to be imaged. If these caveats can be overcome then simple negative staining can be a powerful technique for providing a low-resolution glimpse of the protein sample especially for rapid large-scale screening purposes. Furthermore, simple image analysis of the particles can be used to produce a two-dimensional class average and quantify the conformational classes within the protein sample. Thus, EM can guide the screening of protein constructs for further structural studies, such as crystallization or full three-dimensional cryo-EM reconstructions (Pugach et al., 2015; Julien et al., 2015). It is important to note that protein crystallography generally works well with protein complexes of <150 kDa, which makes these two techniques highly complementary.
With technological innovations pushing its data sets beyond 3 Å resolution, cryo-EM techniques are becoming an increasingly useful tool for screening of both protein conformational stability and compositional stability. Examples of recent studies using EM to assess protein stability and dynamics include the structural transitions of αβ-tubulin (Alushin et al., 2014) and the stability of the HBV capsid protein (Selzer et al., 2015).
Spectroscopic methods are primarily used to assess the stability of the protein at the level of secondary structure. Secondary structure can be analyzed using a variety of spectroscopic methods including circular dichroism (CD), Fourier transform infrared (FT-IR) and Raman spectroscopy (Pelton & McLean, 2000). CD spectroscopy, or spectropolarimetry, of proteins is carried out in the far-ultraviolet range (170–250 nm) and has seen a recent resurgence in usage as a result of the development of synchrotron-radiation circular dichroism (SRCD), which can operate at lower wavelengths (Whitmore & Wallace, 2008; Wallace & Janes, 2010). SRCD data are now publicly available in a central repository at the Protein Circular Dichroism Data Bank (PCDDB), which currently contains 529 entries (Whitmore et al., 2011; https://bit.ly/1OrNzrP ). Using this technique, a twin minimum in the ellipticity spectrum at 208 and 222 nm is produced by α-helical content, whereas a single minimum at 204 or 217 nm is suggestive of random-coil or β-sheet content, respectively. Example uses of UV-CD for assessing protein stability include a study of the plant membrane protein MBP-b6. The percentages of helix, sheet, turn and unordered secondary structure were determined and it was demonstrated that n-dodecyl-β-D-maltoside and Triton X-100 both preserved the correct secondary structure, whereas sodium dodecyl sulfate was shown to disrupt the secondary structure (Surma et al., 2014).
Infrared spectroscopy can also be used to identify secondary-structure content (Barth, 2007). This technique is used to analyze changes in the bond oscillation of amide groups which result from differences in the hydrogen-bonding pattern. Refinements of this technique include femtosecond two-dimensional infrared spectroscopy (2D-IR), which is more sensitive in detecting structural differences (Demirdöven et al., 2004).
Assessment of protein stability, particularly thermodynamic stability, is of tremendous value for the crystallization of both soluble and membrane proteins. The global thermal stability of a protein can be represented by its thermal denaturation midpoint or melting temperature (Tm). Methods used for the determination of Tm include Thermofluor (also known as differential scanning fluorimetry; DSF; Boivin et al., 2013; Ericsson et al., 2006; Reinhard et al., 2013) and differential scanning calorimetry (DSC; Sanchez-Ruiz, 1995; Privalov & Dragan, 2007; Bruylants et al., 2005).
The Tm is often determined using the Thermofluor technique using a reporter dye such as SYPRO Orange or 1-anilinonaphthalene-8-sulfonate (ANS). The reporter dye undergoes a change in fluorescence properties upon binding to the hydrophobic core of the unfolded protein (Semisotnov et al., 1991). A major advantage of the technique is that very little protein is required (typically <<1 mg) and the only equipment required is a readily available qPCR machine. One interesting application of this technique is the detection of ligand binding to proteins of unknown function. For example, by screening a library of 3000 compounds this technique was used to determine that an essential gene from Streptococcus pneumoniae was a nucleoside diphospho-keto-sugar aminotransferase (Carver et al., 2005). Additionally, Thermofluor techniques can be used for the quantitation of protein–protein interactions. This has been used to analyze the stabilizing effect of maltose-binding protein on ankyrin-repeat proteins via the production of a series of alanine mutants to probe the interaction (Layton & Hellinga, 2011). Using such techniques, it is possible to screen hundreds of protein truncations (see §6.1) and buffer conditions (see §6.2) on a high-throughput scale (Boivin et al., 2013; Ristic et al., 2015; Seabrook & Newman, 2013; Reinhard et al., 2013). Melting-temperature methods are also applicable for the assessment of the thermodynamic stability of membrane proteins in detergents and have been successfully applied to the analysis of the stability of the acetylcholine receptor in Brij-35 (Yeh et al., 2006).
Potential caveats of the Thermofluor technique include the possibility of protein–dye interactions that many adversely affect the protein stability, a phenomenon that was observed in the study of GroEL when using the ANS dye (Smoot et al., 2001). Additionally, aberrant or false-positive thermal shifts are fairly common, and careful analysis of the melt-curve data must be carried out; tools such as Meltdown are available to assist in this effort (Rosa et al., 2015). It is also important to note that not all proteins unfold in a well-defined sigmoid melting curve, including proteins with high disulfide-bond content (e.g. albumin) and proteins from thermophilic organisms such as T. maritima. Finally, a negative Thermofluor result (i.e. no change in Tm) does not necessarily indicate a lack of binding for small molecules, but could simply mean that the small molecule does not stabilize the protein further. This is of particular relevance for proteins which have high melting temperatures in the absence of ligands.
Many techniques are available for increasing the crystallizability of a protein, and the central theme of these techniques is the improvement of both the compositional and the conformational stability of the protein sample (Table 4). These methods include truncations, buffers, ligands, purification tags, reductive methylation, surface-entropy reduction (SER), in situ proteolysis, Thermofluor, deuterium-exchange mass spectrometry (DXMS) and disulfide engineering. A complementary review of protein-engineering approaches for improving the properties of proteins for crystallization studies is provided by Ruggiero et al. (2012).
Several software packages and algorithms have been developed to assess the so-called crystallizability of a protein (Smialowski & Frishman, 2010; Derewenda, 2010; Ruggiero et al., 2012). These include DisMeta (Huang et al., 2014), XtalPred (Jahandideh et al., 2014; Slabinski et al., 2007), POODLE (Shimizu, 2014), MFDp2 (Mizianty et al., 2014), MoRFpred (Disfani et al., 2012), RFCRYS (Jahandideh & Mahdavi, 2012), XANNpred (Overton et al., 2011), SVMCRYS (Kandaswamy et al., 2010), SCMCRYS (Charoenkwan et al., 2013), CRYSTALP2 (Kurgan et al., 2009), MetaPPCP (Mizianty & Kurgan, 2009) and ParCrys (Overton et al., 2008). Many of these packages use a template-based approach to analyze the propensity of a protein to crystallize by comparison with known crystal structures. However, some of these packages, including XtalPred, POODLE and DisMeta, utilize sequence-based predictions to identify regions of low complexity, disorder, transmembrane and signal peptides.
It is important to note that the propensity for disorder calculated by most of these methods, and in turn the propensity for crystallization, is largely predicted on the basis of a single polypeptide chain in isolation. Clearly, protein–protein complexes can often result in stabilization of the constituent proteins, as is commonly observed for IDPs (see §2.2). Therefore, particularly for crystallographic studies, it is often essential to study the protein–protein complex as a whole; the individual proteins are often too disordered or unstable when uncomplexed.
Selection of the shortest possible domain is often preferable and computational tools such as Expression of Soluble Proteins by Random Incremental Truncation (ESPRIT) and combinatorial domain hunting (CDH) are available to assist in this effort (Reich et al., 2006; Yumerefendi et al., 2010). Truncation of a protein to the shortest possible fragment can often be a key factor in successful structure solution. For example, amyloid fibers are of tremendous medical importance in diseases such Alzheimer's and prion diseases and their partially disordered structure has traditionally hindered structural analysis using crystallographic techniques. However, shorter fragments of only 6–7 amino acids in length, which also form fibrils, were used to produce microcrystals and to determine the structure (Moshe et al., 2016; Sawaya et al., 2007). Another example is the production of structured truncation arrays of a target protein generated using polymerase incomplete primer extension cloning methods (Klock & Lesley, 2009). Using this technique, structural genomics consortia such as the Joint Center for Structural Genomics (JCSG) and the Structural Genomics Institute, Karolinska Institutet have been able to generate several thousands of truncations for targets recalcitrant to crystallization (Klock et al., 2008; Gräslund et al., 2008).
In addition to truncations of the protein, it is also important to consider other mutations of the protein. Stability-enhancing mutations in membrane proteins are surprisingly common and some estimates suggest that ∼10% of random mutations will confer some level of stability on the protein (Bowie, 2001). For example, two valine-to-alanine substitutions in the transmembrane portion of the M13 coat protein were found to enhance thermal stability (Deber et al., 1993). The reasons for the stability-enhancing effects of mutations are not always immediately obvious from analysis of the structure. It has been suggested that membrane proteins are required to be inherently flexible, and therefore conformationally unstable, in order to maintain function in the restricted environment of the membrane (Bowie, 2001).
The buffer in which a protein is solubilized exerts an influence on its stability (Davis-Searles et al., 2001). Therefore, buffer screening is a powerful method for stabilizing proteins for crystallographic applications and also for the formulation of biologics. One of the primary methods used for high-throughput buffer screening is Thermofluor (see §5.4). Using such approaches it is possible to screen libraries of hundreds of different buffers and pH combinations, and the stabilizing effect can be easily inferred from the change in Tm (ΔTm; Reinhard et al., 2013; Ristic et al., 2015). Using these approaches, interesting protein-stabilizing buffers have been identified. For example, citrate, bis-tris and N-(2-acetamido)iminodiacetic acid (ADA) have all been identified as having statistically significant stabilizing effects on the proteins tested (Ristic et al., 2015). As a more extreme example of buffer screening, IIPs (see §2.2) such as V22-SH3 are insoluble in traditional buffer systems and can only be solubilized in pure water (Liu & Song, 2009).
Ligand binding can also significantly help to stabilize the protein, particularly from the perspective of conformational stability. Co-crystal structures of proteins bound to cofactors, prosthetic groups, substrates, drugs and inhibitors are often the holy grail of structural biology; somewhat fortunately for the structural biologist, ligands often have a stabilizing effect on the protein and can increase the chances of successful crystallization. It is important to remember that although soaking of compounds through the crystal lattice to the active site is often possible, it may also bring about conformational changes in the protein on binding of the ligand. Therefore, ab initio crystal screening in the presence of the ligand may be required in order to obtain crystals (Hassell et al., 2007). The judicial use of bioanalytical techniques, such as Thermofluor and NMR, is key for guiding the successful production of a ligand-bound crystal structure (see §5).
In addition to the stabilizing effects of small-molecule ligands, it is also possible to identify ions and other organic additives that stabilize the protein or even the crystal. For example, Thermofluor was used to identify magnesium ions as a stabilizing influence on the enzyme DapD from Mycobacterium tuberculosis, and the subsequent addition of MgCl2 to the crystallization solution resulted in larger crystals (Reinhard et al., 2013; Schuldt et al., 2009). In this case, stabilization of the quaternary structure results from the addition of magnesium ions, and two tightly coordinated Mg2+ ions were identified in the homotrimer interface of the crystal structure (Schuldt et al., 2009). Other examples of stabilizing additives include the commonly used precipitants polyethylene glycol (PEG) and 2-methyl-2,4-pentanediol (MPD), which are both often observed bound to crystal structures. In the case of MPD it has been proposed that it stabilizes the protein by promoting the hydration of the protein surface by binding to exposed hydrophobic surface residues such as leucine (Anand et al., 2002). Building on this theme, additive screens such as `Silver Bullets' (Hampton Research) have been assembled that can stabilize or initiate cross-linking between proteins further promoting crystal lattice formation (McPherson & Cudney, 2006).
As discussed in §4.1, affinity purification tags such as MBP are often used to aid in both protein stability and solubility during the course of protein expression and purification. Larger tags such as MBP are usually removed prior to crystallization trials, as the flexibility of the tag can interfere with crystal packing. However, in some cases smaller tags such as His can often be left on without unduly affecting crystal packing or protein function (Bucher et al., 2002). The primary reason for removing large tags is the reduced conformational stability resulting from the flexible linker between the target protein and its affinity tag. Several groups have successfully `engineered out' this linker flexibility by inserting a string of alanine residues in place of the usual protease cleavage site in the linker (Smyth et al., 2003). This concept has resulting in several crystal structures of proteins fused to MBP, including gp21 (Kobe et al., 1999), SarR (Liu et al., 2001), MATa1 (Ke & Wolberger, 2003) and MCL1 (Clifton et al., 2015). This concept has led to the idea of crystallization chaperones (Bukowska & Grütter, 2013). Example uses of crystallization chaperones include the application of Fab antibody fragments to study the neurotransmitter sodium symporter LeuT in various conformational states (Krishnamurthy & Gouaux, 2012) and the fusion of T4 lysozyme to G-protein-coupled receptor (Zou et al., 2012). These approaches are proving to be useful for the stabilization of membrane proteins and as aids in their structure determination (Lieberman et al., 2011).
Modification of the protein surface is a well established strategy for enhancing protein crystallization and can be achieved using site-directed mutagenesis (see §6.6) or chemical modification (Derewenda, 2004). A common method of chemical modification is the reductive methylation of the ∊-amino groups of solvent-exposed lysine residues. This is performed using the reducing agents dimethylamine–borane and formaldehyde (Means, 1977). In recent years, this technique has become one of the workhorse `salvage' techniques of structural genomics consortia (Tan et al., 2014; Walter et al., 2006; Sledz et al., 2010). Reductive methylation is believed to function via the introduction of new surface contacts, therefore promoting crystal lattice formation (Sledz et al., 2010).
Recent developments of the technique include the use of ethylation and isopropylation, although fewer targets are available to assess the performance of such techniques (Tan et al., 2014). Another important development of the method is the use of cysteine alkylation for the structure determination of membrane proteins. This method was first used for the determination of the β1-adrenergic G-protein-coupled receptor structure (Warne et al., 2008) and is in common use for many GPCR studies (Columbus, 2015). Cysteine alkylation stabilizes the protein by preventing the formation of disulfide bonds, and in the case of the β1-adrenergic receptor functions by stabilizing the monomers and preventing oligomers forming (Mathiasen et al., 2014).
In addition to alkylation, other chemical modifications such as fluorination can be carried out. Fluorine is all but absent from biological systems, but stabilizes proteins as a result of the `fluorous effect' (Buer & Marsh, 2014; Marsh, 2014). This effect results in an unusual propensity to undergo phase separation and causes an increase in the buried surface area in the hydrophobic core of fluorinated proteins. Fluorinated proteins can be generated using the highly fluorinated amino acid hexafluoroleucine. The crystal structure of a designed four-helical bundle protein, α4H, reveals that the fluorinated residues pack well into the hydrophobic core of the protein with little perturbation of the structure (Buer et al., 2012). This method would clearly perturb the structure and the function of some proteins, but may be a method worthy of further investigation for enabling structural studies of very unstable proteins.
Mutagenesis of surface-exposed amino acids is a proven method for engineering proteins with improved stability and chance of crystallization (Derewenda, 2004, 2010; Derewenda & Vekilov, 2006). Amongst the first uses of mutagenesis to enhance crystallizability was the transplant of key crystal contacts from the rat ferritin structure onto the human ortholog, which was previously unsolved (Lawson et al., 1991). In this example, Lys86 of the human protein was mutated to Glu to mimic the Ca2+-binding site observed in the crystal contacts of the rat ortholog. Clearly, this technique is useful if an orthologous structure is available. However, this is often not the case and one is shooting in the dark when choosing surface residues to mutate.
To address this issue, Derewenda and coworkers made a series of mutations to RhoGDI targeting glutamate and lysine residues on the surface of the protein (Longenecker et al., 2001; Mateja et al., 2002; Czepas et al., 2004). These residues have high conformational entropy and rarely participate in protein–protein interfaces within the crystal (Lo Conte et al., 1999; Baud & Karlin, 1999). Therefore, they represent attractive targets for modification of crystal contacts. In an attempt to reduce the conformational entropy on the protein surface, these residues were mutated to either alanine, arginine or aspartate. The original set of mutable residues has more recently been expanded to include glutamine. The SERp server is an excellent resource for identifying such residues (Goldschmidt et al., 2007, 2014; https://bit.ly/1LFjdyk ). This site evaluates the solvent exposure, secondary structure, surface entropy and evolutionary conservation for sets of glutamate, glutamine or lysine residues. Analysis of the evolutionary conservation is used as a guide to avoid the mutation of residues that may be structurally or functionally significant. Finally, the site suggests appropriate clusters of residues matching these criteria as suitable for mutagenesis. These techniques have been used in conjunction with the fused affinity-tag and molecular-chaperone approach discussed above (see §6.4) to determine the structure of a RACK1A-MBP fusion protein (Moon et al., 2010).
Finally, the presence of PTMs (see §3.1) on the surface of the protein, in particular glycosylation, must be carefully considered. N- and O-linked sugars occur frequently on the surface of eukaryotic proteins and their chemical heterogeneity and conformational freedom can result in significant conformational variability. Strategies to deal with glycosylation on the protein surface include expression using a deglycosylation-deficient CHO cell strains such as lec1 (Puthalakath et al., 1996) or an HEK293S strain such as GnTI (Reeves et al., 2002). Another strategy is the mutagenesis of consensus glycosylation sites, such as the Asn-X-Ser/Thr sequon, which is targeted for N-linked glycosylation (Mellquist et al., 1998), and Ser or Thr residues, which are targeted for O-linked glycosylation. Alternatively, endoglycosylases such as endoglycosidase H (Endo H) and PNGase F can be used. In the case of PNGase F the glycosylated asparagine residue is converted to an aspartic acid, thus removing the sugar completely. Another approach is the use of N-glycosylation inhibitors such as swainsonine and kifunensine (Elbein, 1987). An effective combination approach uses glycosylation inhibitors during expression, followed by treatment with Endo H. Such an approach has been used to generate diffraction-quality crystals of sRPTPμ and s19A (Chang et al., 2007). Also, prudent selection of the expression system can be deployed to vary the glycosylation patterns. For example, Sf insect cells generally produce simpler glycosylation patterns of the GlucNAcMan5 type and often glycosylate at fewer sites, whereas yeast cells can often hyperglycosylate proteins.
It must be noted that the presence of glycans on the protein surface can sometimes aid in crystallization by mediating important crystal contacts. For example, a complex of the densely glycosylated Hepatitis C virus E2 (HCV E2) glycoprotein bound to a broadly neutralizing antibody failed to crystallize using numerous deglycosylation strategies, including Endo H treatment or N-linked glycan-sequon mutagenesis. In this example, only the fully glycosylated HCV E2 protein crystallized, and the resulting structure revealed a key crystal contact mediated by an N-linked glycan interacting nonspecifically with a neighboring symmetry mate within the crystal (Kong et al., 2013). Full or partial glycosylation may also be necessary to crystallize complexes involving glycan-dependent protein interactions (Kong et al., 2014). These examples illustrate that glycosylation may also have a stabilizing effect on proteins, especially for crystallization, and the presence of glycans is not always deleterious.
The presence of proteases in a protein sample submitted for crystallization trials is clearly of significance and can lead to significant composition instability over the time course of the experiment. For example, it was serendipitously discovered that a penicillium fungus growing in a crystallization drop was responsible for cleaving ∼200 residues off the yeast CPSF-100 protein and was essential for successful crystallization (Mandel et al., 2006; Bai et al., 2007). This finding initiated so-called in situ proteolysis, in which protein samples are crystallized in the presence of a panel of proteases such as trypsin and chymotrypsin (Dong et al., 2007; Wernimont & Edwards, 2009). More recently, these techniques have been combined with mass-spectrometric analysis to help identify peptide fragments that are stable over the time frame of a crystallization experiment (Gheyi et al., 2010). In a similar fashion, limited proteolysis can be used to identify stable domains of membrane and globular proteins. The membrane protects the transmembrane portions of the protein from protease cleavage and more compact forms of the membrane protein can be produced. Such an approach was used to generate a stable 55 kDa construct of the P pilus PapC that was subsequently used for crystal structure determination (Remaut et al., 2008).
The thermal stability of a protein displays a good level of correlation with the crystallizability of a protein. For example, a large-scale Thermofluor study of 657 protein samples showed that only 23% of the proteins with a Tm of <43°C yielded crystals. In contrast, 49% of the proteins with a Tm of >45°C yielded crystals (Dupeux et al., 2011; see §5.4 and Fig. 4). However, beyond 45°C the Tm does not appear to be particularly predictive of crystallizability; there is no significant increase in crystallization frequency for proteins with a Tm of between 45 and 96°C (Dupeux et al., 2011). Furthermore, there are no known structural features that correlate well with Tm (Kumar et al., 2000), and the Tm appears to be highly solvent-dependent (Faria et al., 2004). It is important to note that other features of the melting curve, aside from the Tm, may be useful to the crystallographer. For example, a high initial signal generated by a Thermofluor assay may indicate that an exposed hydrophobic surface of the native protein may be interacting with the dye used in the assay. Proteins displaying such profiles appear to have a lower rate of crystallization (36.6%; Dupeux et al., 2011). Similarly, a wide thermal denaturation peak can be indicative of noncooperative unfolding behavior, which might be a useful consideration for construct modification (Morar-Mitrica et al., 2013). Ultimately, measures of thermal stability may be useful as a broad, qualitative assessment of the suitability of a protein for crystallization.
6.9. Deuterium-exchange mass spectrometry (DXMS)
Prior to crystallization, it is often desirable to remove protein termini and low-complexity regions owing to their inherent flexibility (Derewenda, 2010). Disordered regions of the protein often hamper crystallization and the design of protein constructs trimmed of such regions can be guided using deuterium-exchange mass spectrometry (DXMS; Englander, 2006; Konermann et al., 2011; Spraggon et al., 2004; Figs. 5a and 5b).
Using this technique, proteins are exposed to deuterated solvent and the deuterium is allowed to exchange with the backbone amide protons of the protein. The exchange rates are dependent on the degree of solvent exposure and the integrity of the local secondary structure. Therefore, this method gives a measure of the conformational flexibility of the protein in solution (Englander & Kallenbach, 1983). These rates can be obtained by measuring the change in the mass of the deuterated peptides using proteolytic mass spectrometry. DXMS has been successfully applied to structural genomics targets, guiding the deletion of flexible regions that previously hindered the growth of diffraction-quality crystals (Spraggon et al., 2004; Pantazatos et al., 2004; Fig. 5). Although the utility of DXMS is often limited by the lengths of the proteolytic fragments that can be produced, sometimes restricting the area of investigation to less than 50% of the protein sequence, it can often provide a key insight for the design of crystallizable constructs. Furthermore, when coupled with structure, DXMS can be mapped onto the protein structure to provide a three-dimensional map of flexibility that can further guide protein-engineering efforts and mechanistic studies (Guttman et al., 2012; Kong et al., 2010; Zhang et al., 2012; Chung et al., 2011).
Disulfide engineering is a well established method for stabilizing proteins and for studying and modifying protein function and dynamics (Dombkowski et al., 2014). Intermolecular disulfide bonds between proteins in the crystal lattice have been observed and have led to the coining of the phrase `spontaneously polymerizing protein crystals' (Quistgaard, 2014). Disulfide engineering can be used to introduce these intermolecular disulfides into the protein in an attempt to stabilize the lattice and promote crystallization. Furthermore, studies have shown that symmetrical proteins, such as homodimers, tend to crystallize more readily (Wukovitz & Yeates, 1995). Using T4 lysozyme as a model system, it has been demonstrated that the introduction of disulfide bonds can be used to make monomeric proteins dimerize and increasing the chance of lattice formation (Banatao et al., 2006; Heinz & Matthews, 1994). This has been termed `synthetic symmetrization' and can be a useful tool for assisting in the crystallization of monomeric proteins and protein–protein complexes which display asymmetry. Proteins that have been successfully crystallized using this approach include CelA from Thermotoga maritima (Forse et al., 2011). Tools for identifying residues suitable for disulfide engineering are available, including Disulfide by Design 2.0 (Craig & Dombkowski, 2013; https://bit.ly/1NbV2tQ ). One caveat of this approach is the potential of disulfide bonds to adopt different conformations that may promote conformational flexibility. For example, the disulfide bond connecting the I-EGF1 and I-EGF2 domains of β2 integrin is able to accommodate a >20 Å hinge motion between the domains (Shi et al., 2007; Smagghe et al., 2010).
Protein stability does mean many different things to many different scientists. However, on a global level, it can be considered as the ability of a protein to maintain structure and function in a particular environment. If the environment of interest is normal physiological conditions then the net summation of all contributing forces must add up to provide a small negative ΔG, therefore favoring a stable folded protein. However, not all proteins operate in standard physiological environments and many other factors must be considered. For example, many proteins, such as FeFe hydrogenase, are sensitive to oxygen and changes to the structure must be considered under an oxygen-free environment (Mulder et al., 2011). However, the growth of such enzymes under anaerobic conditions is both costly and difficult to achieve on a large scale, and considerable effort is being made to generate oxygen-tolerant hydrogenases for use on an industrial scale (Fritsch et al., 2013). The stability of such enzymes is of great interest to the biofuels industry, with the potential for biological hydrogen-gas production (Kim & Kim, 2011). Similarly, light-sensitive proteins such as opsins, photolyase and photosystems I and II all have unique structural features that enable them to utilize the energy of photons to carry out biological functions. Other interesting examples include the light-, oxygen- and voltage-sensitive domains (LOVs) found in plants and algae that undergo conformational changes and covalent binding of an FMN moiety under illumination with blue light (Briggs, 2007; Kottke et al., 2006). These examples illustrate how nonstandard environments must be considered in any discussion of protein stability.
Traditionally, discussions of protein stability have focused on soluble, folded and more classical globular proteins. However, large percentages of the proteome are predicted to contain unstructured, insoluble and aggregated proteins in the form of IDPs, IIPs and APRs (see §2.2). Such proteins, and their unwieldy conformational stability, represent a challenge for protein crystallographers, who usually simply remove such regions during the cloning stage. However, these families of proteins are of tremendous medical importance and deletion of these regions at the cloning stage is no longer meaningful. Crystallographic techniques are evolving to help to deal with such proteins, particularly in the areas of microcrystallography, next-generation synchrotron sources and X-ray free-electron lasers (XFELs) (Neutze & Moffat, 2012; Spence et al., 2012; Gruner & Lattman, 2015; Weckert, 2015; Moukhametzianov et al., 2008; Igarashi et al., 2008; Fischetti et al., 2009).
Recent developments in XFEL light sources are enabling higher resolution studies of protein dynamics on a femtosecond timescale (Uervirojnangkoorn et al., 2015). From a crystallization perspective, such light sources present a unique set of challenges to the crystallographer that are somewhat orthogonal to the traditional problems faced when using more traditional diffraction methods. One such requirement is the need for microcrystalline sample material that can be injected into the laser path. Additionally, such techniques also allow larger and more dynamic protein structures to be determined, particularly those which possess conformational disorder and thus hinder the formation of larger, more ordered crystals. Recent examples using XFEL techniques include structures of the complex between synaptotagmin 1 and the neuronal SNARE (Zhou et al., 2015). Clearly, XFEL technology, especially when coupled with associated hybrid methods, such as cryo-EM, ultrafast electron diffraction (UED) and double electron–electron resonance (DEER), will help mitigate many of the problems associated with protein conformation stability and its effect on protein crystallization (Wakatsuki, 2016). Next-generation synchrotron sources will be capable of providing smaller and brighter X-ray beams, and most importantly with a higher coherence fraction (Weckert, 2015). Such coherent beams will provide exciting opportunities for the study of protein dynamics on ever smaller timescales and will be essential for the study of unstable proteins that are currently inaccessible using traditional crystallographic techniques.
In summary, we will end this review with the title of an excellent paper by the late Fred Richards: Protein stability: still an unsolved problem (Richards, 1997). Although the problem is still largely unsolved, considerable progress has been made towards the study of protein stability, disorder and dynamics. Structural methods such as crystallography, NMR and cryo-EM are central to this endeavor and the exploration of innovative hybrid methods will be vital.
This work was sponsored in part by contributions from k.-k. Hofkristallamt, Vista, California, USA and Austrian Science Fund (FWF) project P28395-B26. We would like to thank members of the Joint Center for Structural Genomics for fruitful discussions and contribution of the Thermofluor and DXMS data.
Altmann, F., Staudacher, E., Wilson, I. B. & März, L. (1999). Insect cells as hosts for the expression of recombinant glycoproteins. Glycoconj. J. 16, 109–123. CrossRef PubMed CAS Google Scholar
Alushin, G. M., Lander, G. C., Kellogg, E. H., Zhang, R., Baker, D. & Nogales, E. (2014). High-resolution microtubule structures reveal the structural transitions in αβ-tubulin upon GTP hydrolysis. Cell, 157, 1117–1129. CrossRef CAS PubMed Google Scholar
Amarasinghe, C. & Jin, J.-P. (2015). The use of affinity tags to overcome obstacles in recombinant protein expression and purification. Protein Pept. Lett. 22, 885–892. CrossRef CAS PubMed Google Scholar
Anand, K., Pal, D. & Hilgenfeld, R. (2002). An overview on 2-methyl-2,4-pentanediol in crystallization and in crystals of biological macromolecules. Acta Cryst. D58, 1722–1728. Web of Science CrossRef CAS IUCr Journals Google Scholar
Anfinsen, C. B. (1973). Principles that govern the folding of protein chains. Science, 181, 223–230. CrossRef CAS PubMed Web of Science Google Scholar
Aranko, A. S., Wlodawer, A. & Iwai, H. (2014). Nature's recipe for splitting inteins. Protein Eng. Des. Sel. 27, 263–271. CrossRef CAS PubMed Google Scholar
Bachmair, A., Finley, D. & Varshavsky, A. (1986). In vivo half-life of a protein is a function of its amino-terminal residue. Science, 234, 179–186. CrossRef CAS PubMed Web of Science Google Scholar
Bai, Y., Auperin, T. C. & Tong, L. (2007). The use of in situ proteolysis in the crystallization of murine CstF-77. Acta Cryst. F63, 135–138. Web of Science CrossRef CAS IUCr Journals Google Scholar
Banatao, D. R., Cascio, D., Crowley, C. S., Fleissner, M. R., Tienson, H. L. & Yeates, T. O. (2006). An approach to crystallizing proteins by synthetic symmetrization. Proc. Natl Acad. Sci. USA, 103, 16230–16235. Web of Science CrossRef PubMed CAS Google Scholar
Barth, A. (2007). Infrared spectroscopy of proteins. Biochim. Biophys. Acta, 1767, 1073–1101. CrossRef PubMed CAS Google Scholar
Baud, F. & Karlin, S. (1999). Measures of residue density in protein structures. Proc. Natl Acad. Sci. USA, 96, 12494–12499. Web of Science CrossRef PubMed CAS Google Scholar
Bieri, M., Kwan, A. H., Mobli, M., King, G. F., Mackay, J. P. & Gooley, P. R. (2011). Macromolecular NMR spectroscopy for the non-spectroscopist: beyond macromolecular solution structure determination. FEBS J. 278, 704–715. CrossRef CAS PubMed Google Scholar
Boivin, S., Kozak, S. & Meijers, R. (2013). Optimization of protein purification and characterization using Thermofluor screens. Protein Expr. Purif. 91, 192–206. Web of Science CrossRef CAS PubMed Google Scholar
Bowie, J. U. (2001). Stabilizing membrane proteins. Curr. Opin. Struct. Biol. 11, 397–402. Web of Science CrossRef PubMed CAS Google Scholar
Briggs, W. R. (2007). The LOV domain: a chromophore module servicing multiple photoreceptors. J. Biomed. Sci. 14, 499–504. CrossRef PubMed CAS Google Scholar
Brondyk, W. H. (2009). Selecting an appropriate method for expressing a recombinant protein. Methods Enzymol. 463, 131–147. CrossRef PubMed CAS Google Scholar
Brunori, M. (2014). Variations on the theme: allosteric control in hemoglobin. FEBS J. 281, 633–643. CrossRef CAS PubMed Google Scholar
Brutscher, B., Felli, I. C., Gil-Caballero, S., Hošek, T., Kümmerle, R., Piai, A., Pierattelli, R. & Sólyom, Z. (2015). NMR methods for the study of instrinsically disordered proteins structure, dynamics, and interactions: general overview and practical guidelines. Adv. Exp. Med. Biol. 870, 49–122. CrossRef PubMed Google Scholar
Bruylants, G., Wouters, J. & Michaux, C. (2005). Differential scanning calorimetry in life science: thermodynamics, stability, molecular recognition and application in drug design. Curr. Med. Chem. 12, 2011–2020. Web of Science CrossRef PubMed CAS Google Scholar
Bryan, P. N. & Orban, J. (2010). Proteins that switch folds. Curr. Opin. Struct. Biol. 20, 482–488. CrossRef CAS PubMed Google Scholar
Bucher, M. H., Evdokimov, A. G. & Waugh, D. S. (2002). Differential effects of short affinity tags on the crystallization of Pyrococcus furiosus maltodextrin-binding protein. Acta Cryst. D58, 392–397. Web of Science CrossRef CAS IUCr Journals Google Scholar
Buer, B. C. & Marsh, E. N. (2014). Design, synthesis, and study of fluorinated proteins. Methods Mol. Biol. 1216, 89–116. CrossRef CAS PubMed Google Scholar
Buer, B. C., Meagher, J. L., Stuckey, J. A. & Marsh, E. N. (2012). Structural basis for the enhanced stability of highly fluorinated proteins. Proc. Natl Acad. Sci. USA, 109, 4810–4815. CrossRef CAS PubMed Google Scholar
Bukowska, M. A. & Grütter, M. G. (2013). New concepts and aids to facilitate crystallization. Curr. Opin. Struct. Biol. 23, 409–416. Web of Science CrossRef CAS PubMed Google Scholar
Bulawa, C. E., Connelly, S., Devit, M., Wang, L., Weigel, C., Fleming, J. A., Packman, J., Powers, E. T., Wiseman, R. L., Foss, T. R., Wilson, I. A., Kelly, J. W. & Labaudinière, R. (2012). Tafamidis, a potent and selective transthyretin kinetic stabilizer that inhibits the amyloid cascade. Proc. Natl Acad. Sci. 109, 9629–9634. CrossRef CAS PubMed Google Scholar
Calderone, T. L., Stevens, R. D. & Oas, T. G. (1996). High-level misincorporation of lysine for arginine at AGA codons in a fusion protein expressed in Escherichia coli. J. Mol. Biol. 262, 407–412. CrossRef CAS PubMed Google Scholar
Carver, T. E. et al. (2005). Decrypting the biochemical function of an essential gene from Streptococcus pneumoniae using Thermofluor technology. J. Biol. Chem. 280, 11704–11712. Web of Science CrossRef PubMed CAS Google Scholar
Chang, V. T., Crispin, M., Aricescu, A. R., Harvey, D. J., Nettleship, J. E., Fennelly, J. A., Yu, C., Boles, K. S., Evans, E. J., Stuart, D. I., Dwek, R. A., Jones, E. Y., Owens, R. J. & Davis, S. J. (2007). Glycoprotein structural genomics: solving the glycosylation problem. Structure, 15, 267–273. Web of Science CrossRef PubMed CAS Google Scholar
Charoenkwan, P., Shoombuatong, W., Lee, H.-C., Chaijaruwanich, J., Huang, H.-L. & Ho, S.-Y. (2013). SCMCRYS: predicting protein crystallization using an ensemble scoring card method with estimating propensity scores of P-collocated amino acid pairs. PLoS One, 8, e72368. Web of Science CrossRef PubMed Google Scholar
Chatterjee, S. & Mayor, S. (2001). The GPI-anchor and protein sorting. Cell. Mol. Life Sci. 58, 1969–1987. CrossRef PubMed CAS Google Scholar
Chong, S., Mersha, F. B., Comb, D. G., Scott, M. E., Landry, D., Vence, L. M., Perler, F. B., Benner, J., Kucera, R. B., Hirvonen, C. A., Pelletier, J. J., Paulus, H. & Xu, M.-Q. (1997). Single-column purification of free recombinant proteins using a self-cleavable affinity tag derived from a protein splicing element. Gene, 192, 271–281. CrossRef CAS PubMed Web of Science Google Scholar
Chou, P. Y. & Fasman, G. D. (1974). Conformational parameters for amino acids in helical, β-sheet, and random coil regions calculated from proteins. Biochemistry, 13, 211–222. CrossRef CAS PubMed Web of Science Google Scholar
Chung, C. (2007). The use of biophysical methods increases success in obtaining liganded crystal structures. Acta Cryst. D63, 62–71. CrossRef IUCr Journals Google Scholar
Chung, K. Y., Rasmussen, S. G. F., Liu, T., Li, S., DeVree, B. T., Chae, P. S., Calinski, D., Kobilka, B. K., Woods, V. L. Jr & Sunahara, R. K. (2011). Conformational changes in the G protein Gs induced by the β2 adrenergic receptor. Nature (London), 477, 611–615. CrossRef CAS PubMed Google Scholar
Clifton, M. C. et al. (2015). A maltose-binding protein fusion construct yields a robust crystallography platform for MCL1. PLoS One, 10, e0125010. CrossRef PubMed Google Scholar
Columbus, L. (2015). Post-expression strategies for structural investigations of membrane proteins. Curr. Opin. Struct. Biol. 32, 131–138. CrossRef CAS PubMed Google Scholar
Compiani, M. & Capriotti, E. (2013). Computational and theoretical methods for protein folding. Biochemistry, 52, 8601–8624. CrossRef CAS PubMed Google Scholar
Conte, L. L., Chothia, C. & Janin, J. (1999). The atomic structure of protein–protein recognition sites. J. Mol. Biol. 285, 2177–2198. CrossRef CAS PubMed Google Scholar
Cooper, D. R., Boczek, T., Grelewska, K., Pinkowska, M., Sikorska, M., Zawadzki, M. & Derewenda, Z. (2007). Protein crystallization by surface entropy reduction: optimization of the SER strategy. Acta Cryst. D63, 636–645. Web of Science CrossRef CAS IUCr Journals Google Scholar
Craig, D. B. & Dombkowski, A. A. (2013). Disulfide by Design 2.0: a web-based tool for disulfide engineering in proteins. BMC Bioinformatics, 14, 346. Google Scholar
Czepas, J., Devedjiev, Y., Krowarsch, D., Derewenda, U., Otlewski, J. & Derewenda, Z. S. (2004). The impact of Lys→Arg surface mutations on the crystallization of the globular domain of RhoGDI. Acta Cryst. D60, 275–280. Web of Science CrossRef CAS IUCr Journals Google Scholar
Daniel, E., Onwukwe, G. U., Wierenga, R. K., Quaggin, S. E., Vainio, S. J. & Krause, M. (2015). ATGme: open-source web application for rare codon identification and custom DNA sequence optimization. BMC Bioinformatics, 16, 303. Google Scholar
Davies, D. R. (1964). A correlation between amino acid composition and protein structure. J. Mol. Biol. 9, 605–609. CrossRef PubMed CAS Web of Science Google Scholar
Davis-Searles, P. R., Saunders, A. J., Erie, D. A., Winzor, D. J. & Pielak, G. J. (2001). Interpreting the effects of small uncharged solutes on protein-folding equilibria. Annu. Rev. Biophys. Biomol. Struct. 30, 271–306. PubMed CAS Google Scholar
De Baets, G., Schymkowitz, J. & Rousseau, F. (2014). Predicting aggregation-prone sequences in proteins. Essays Biochem. 56, 41–52. CrossRef PubMed Google Scholar
Deber, C. M., Khan, A. R., Li, Z., Joensson, C., Glibowicka, M. & Wang, J. (1993). Val→Ala mutations selectively alter helix–helix packing in the transmembrane segment of phage M13 coat protein. Proc. Natl Acad. Sci. USA, 90, 11648–11652. CrossRef CAS PubMed Google Scholar
Dekker, K., Yamagata, H., Sakaguchi, K. & Udaka, S. (1991). Xylose (glucose) isomerase gene from the thermophile Thermus thermophilus: cloning, sequencing, and comparison with other thermostable xylose isomerases. J. Bacteriol. 173, 3078–3083. CAS PubMed Google Scholar
Demirdöven, N., Cheatum, C. M., Chung, H. S., Khalil, M., Knoester, J. & Tokmakoff, A. (2004). Two-dimensional infrared spectroscopy of antiparallel β-sheet secondary structure. J. Am. Chem. Soc. 126, 7981–7990. PubMed Google Scholar
Derewenda, Z. S. (2004). Rational protein crystallization by mutational surface engineering. Structure, 12, 529–535. Web of Science CrossRef PubMed CAS Google Scholar
Derewenda, Z. S. (2010). Application of protein engineering to enhance crystallizability and improve crystal properties. Acta Cryst. D66, 604–615. Web of Science CrossRef CAS IUCr Journals Google Scholar
Derewenda, Z. S. & Vekilov, P. G. (2006). Entropy and surface engineering in protein crystallization. Acta Cryst. D62, 116–124. Web of Science CrossRef CAS IUCr Journals Google Scholar
Dieci, G., Bottarelli, L., Ballabeni, A. & Ottonello, S. (2000). tRNA-assisted overproduction of eukaryotic ribosomal proteins. Protein Expr. Purif. 18, 346–354. CrossRef PubMed CAS Google Scholar
Dill, K. A. (1990). Dominant forces in protein folding. Biochemistry, 29, 7133–7155. CrossRef CAS PubMed Web of Science Google Scholar
Disfani, F. M., Hsu, W.-L., Mizianty, M. J., Oldfield, C. J., Xue, B., Dunker, A. K., Uversky, V. N. & Kurgan, L. (2012). MoRFpred, a computational tool for sequence-based prediction and characterization of short disorder-to-order transitioning binding regions in proteins. Bioinformatics, 28, i75–i83. CrossRef CAS PubMed Google Scholar
Dombkowski, A. A., Sultana, K. Z. & Craig, D. B. (2014). Protein disulfide engineering. FEBS Lett. 588, 206–212. CrossRef CAS PubMed Google Scholar
Dong, A. et al. (2007). In situ proteolysis for protein crystallization and structure determination. Nature Methods, 4, 1019–1021. Web of Science CrossRef PubMed CAS Google Scholar
Dunker, A. K. & Oldfield, C. J. (2015). Back to the future: nuclear magnetic resonance and bioinformatics studies on intrinsically disordered proteins. Adv. Exp. Med. Biol. 870, 1–34. CrossRef PubMed Google Scholar
Dupeux, F., Röwer, M., Seroul, G., Blot, D. & Márquez, J. A. (2011). A thermal stability assay can help to estimate the crystallization likelihood of biological samples. Acta Cryst. D67, 915–919. Web of Science CrossRef CAS IUCr Journals Google Scholar
Elbein, A. D. (1987). Inhibitors of the biosynthesis and processing of N-linked oligosaccharide chains. Annu. Rev. Biochem. 56, 497–534. CrossRef CAS PubMed Google Scholar
Englander, S. W. (2006). Hydrogen exchange and mass spectrometry: a historical perspective. J. Am. Soc. Mass Spectrom. 17, 1481–1489. CrossRef PubMed CAS Google Scholar
Englander, S. W. & Kallenbach, N. R. (1983). Hydrogen exchange and structural dynamics of proteins and nucleic acids. Q. Rev. Biophys. 16, 521–655. CrossRef CAS PubMed Google Scholar
Ericsson, U. B., Hallberg, B. M., DeTitta, G. T., Dekker, N. & Nordlund, P. (2006). Thermofluor-based high-throughput stability optimization of proteins for structural studies. Anal. Biochem. 357, 289–298. Web of Science CrossRef PubMed CAS Google Scholar
Evdokimov, A. G. et al. (2007). Serendipitous discovery of novel bacterial methionine aminopeptidase inhibitors. Proteins, 66, 538–546. CrossRef PubMed CAS Google Scholar
Faria, T. Q., Lima, J. C., Bastos, M., Maçanita, A. L. & Santos, H. (2004). Protein stabilization by osmolytes from hyperthermophiles: effect of mannosylglycerate on the thermal unfolding of recombinant nuclease A from Staphylococcus aureus studied by picosecond time-resolved fluorescence and calorimetry. J. Biol. Chem. 279, 48680–48691. CrossRef PubMed CAS Google Scholar
Fields, P. A., Dong, Y., Meng, X. & Somero, G. N. (2015). Adaptations of protein structure and function to temperature: there is more than one way to `skin a cat'. J. Exp. Biol. 218, 1801–1811. CrossRef PubMed Google Scholar
Fischer, S., Handrick, R. & Otte, K. (2015). The art of CHO cell engineering: a comprehensive retrospect and future perspectives. Biotechnol. Adv. 33, 1878–1896. CrossRef CAS PubMed Google Scholar
Fischetti, R. F., Xu, S., Yoder, D. W., Becker, M., Nagarajan, V., Sanishvili, R., Hilgart, M. C., Stepanov, S., Makarov, O. & Smith, J. L. (2009). Mini-beam collimator enables microcrystallography experiments on standard beamlines. J. Synchrotron Rad. 16, 217–225. Web of Science CrossRef CAS IUCr Journals Google Scholar
Forse, G. J., Ram, N., Banatao, D. R., Cascio, D., Sawaya, M. R., Klock, H. E., Lesley, S. A. & Yeates, T. O. (2011). Synthetic symmetrization in the crystallization and structure determination of CelA from Thermotoga maritima. Protein Sci. 20, 168–178. Web of Science CrossRef CAS PubMed Google Scholar
Fritsch, J., Lenz, O. & Friedrich, B. (2013). Structure, function and biosynthesis of O2-tolerant hydrogenases. Nature Rev. Microbiol. 11, 106–114. CrossRef CAS Google Scholar
Fu, W., Lin, J. & Cen, P. (2007). 5-Aminolevulinate production with recombinant Escherichia coli using a rare codon optimizer host strain. Appl. Microbiol. Biotechnol. 75, 777–782. CrossRef PubMed CAS Google Scholar
Gheyi, T., Rodgers, L., Romero, R., Sauder, J. M. & Burley, S. K. (2010). Mass spectrometry guided in situ proteolysis to obtain crystals for X-ray structure determination. J. Am. Soc. Mass Spectrom. 21, 1795–1801. Web of Science CrossRef CAS PubMed Google Scholar
Giordanetto, F., Schäfer, A. & Ottmann, C. (2014). Stabilization of protein–protein interactions by small molecules. Drug Discov. Today, 19, 1812–1821. CrossRef CAS PubMed Google Scholar
Goldschmidt, L., Cooper, D. R., Derewenda, Z. S. & Eisenberg, D. (2007). Toward rational protein crystallization: a web server for the design of crystallizable protein variants. Protein Sci. 16, 1569–1576. Web of Science CrossRef PubMed CAS Google Scholar
Goldschmidt, L., Eisenberg, D. & Derewenda, Z. S. (2014). Salvage or recovery of failed targets by mutagenesis to reduce surface entropy. Methods Mol. Biol. 1140, 201–209. CrossRef CAS PubMed Google Scholar
Goyal, S., Qin, H., Lim, L. & Song, J. (2015). Insoluble protein characterization by circular dichroism (CD) spectroscopy and nuclear magnetic resonance (NMR). Methods Mol. Biol. 1258, 371–385. CrossRef PubMed Google Scholar
Gräslund, S., Sagemark, J., Berglund, H., Dahlgren, L. G., Flores, A., Hammarström, M., Johansson, I., Kotenyova, T., Nilsson, M., Nordlund, P. & Weigelt, J. (2008). The use of systematic N- and C-terminal deletions to promote production and structural studies of recombinant proteins. Protein Expr. Purif. 58, 210–221. Web of Science PubMed Google Scholar
Gruner, S. M. & Lattman, E. E. (2015). Biostructural science inspired by next-generation X-ray sources. Annu. Rev. Biophys. 44, 33–51. Web of Science CrossRef CAS PubMed Google Scholar
Guttman, M., Kahn, M., Garcia, N. K., Hu, S.-L. & Lee, K. K. (2012). Solution structure, conformational dynamics, and CD4-induced activation in full-length, glycosylated, monomeric HIV gp120. J. Virol. 86, 8750–8764. CrossRef CAS PubMed Google Scholar
Guzman, L. M., Belin, D., Carson, M. J. & Beckwith, J. (1995). Tight regulation, modulation, and high-level expression by vectors containing the arabinose PBAD promoter. J. Bacteriol. 177, 4121–4130. CAS PubMed Web of Science Google Scholar
Guzzo, A. V. (1965). The influence of amino acid sequence on protein structure. Biophys. J. 5, 809–822. CrossRef CAS PubMed Google Scholar
Hall, M. P. (2014). Biotransformation and in vivo stability of protein biotherapeutics: impact on candidate selection and pharmacokinetic profiling. Drug Metab. Dispos. 42, 1873–1880. CrossRef PubMed Google Scholar
Hassell, A. M. et al. (2007). Crystallization of protein–ligand complexes. Acta Cryst. D63, 72–79. Web of Science CrossRef CAS IUCr Journals Google Scholar
Hay, R. T. (2005). SUMO. Mol. Cell, 18, 1–12. CrossRef PubMed CAS Google Scholar
Heinz, D. W. & Matthews, B. W. (1994). Rapid crystallization of T4 lysozyme by intermolecular disulfide cross-linking. Protein Eng. Des. Sel. 7, 301–307. CrossRef CAS Web of Science Google Scholar
Hermans, J., Anderson, A. G. & Yun, R. H. (1992). Differential helix propensity of small apolar side chains studied by molecular dynamics simulations. Biochemistry, 31, 5646–5653. CrossRef PubMed CAS Google Scholar
Hirata, R., Ohsumk, Y., Nakano, A., Kawasaki, H., Suzuki, K. & Anraku, Y. (1990). Molecular structure of a gene, VMA1, encoding the catalytic subunit of H+-translocating adenosine triphosphatase from vacuolar membranes of Saccharomyces cerevisiae. J. Biol. Chem. 265, 6726–6733. CAS PubMed Google Scholar
Huai, Q., Kim, H.-Y., Liu, Y., Zhao, Y., Mondragon, A., Liu, J. O. & Ke, H. (2002). Crystal structure of calcineurin–cyclophilin–cyclosporin shows common but distinct recognition of immunophilin–drug complexes. Proc. Natl Acad. Sci. USA, 99, 12037–12042. Web of Science CrossRef PubMed CAS Google Scholar
Huang, Y. J., Acton, T. B. & Montelione, G. T. (2014). DisMeta: a meta server for construct design and optimization. Methods Mol. Biol. 1091, 3–16. CrossRef CAS PubMed Google Scholar
Igarashi, N., Ikuta, K., Miyoshi, T., Matsugaki, N., Yamada, Y., Yousef, M. S. & Wakatsuki, S. (2008). X-ray beam stabilization at BL-17A, the protein microcrystallography beamline of the Photon Factory. J. Synchrotron Rad. 15, 292–295. Web of Science CrossRef CAS IUCr Journals Google Scholar
Jahandideh, S., Jaroszewski, L. & Godzik, A. (2014). Improving the chances of successful protein structure determination with a random forest classifier. Acta Cryst. D70, 627–635. Web of Science CrossRef IUCr Journals Google Scholar
Jahandideh, S. & Mahdavi, A. (2012). RFCRYS: sequence-based protein crystallization propensity prediction by means of random forest. J. Theor. Biol. 306, 115–119. Web of Science CrossRef CAS PubMed Google Scholar
Jarvis, D. L. (2009). Baculovirus–insect cell expression systems. Methods Enzymol. 463, 191–222. CrossRef PubMed CAS Google Scholar
Jiménez, M. A. (2014). Design of monomeric water-soluble β-hairpin and β-sheet peptides. Methods Mol. Biol. 1216, 15–52. PubMed Google Scholar
Julien, J.-P. et al. (2015). Design and structure of two HIV-1 clade C SOSIP.664 trimers that increase the arsenal of native-like Env immunogens. Proc. Natl Acad. Sci. USA, 112, 11947–11952. CrossRef CAS PubMed Google Scholar
Kandaswamy, K. K., Pugalenthi, G., Suganthan, P. N. & Gangal, R. (2010). SVMCRYS: an SVM approach for the prediction of protein crystallization propensity from protein sequence. Protein Pept. Lett. 17, 423–430. CrossRef CAS PubMed Google Scholar
Kane, J. F. (1995). Effects of rare codon clusters on high-level expression of heterologous proteins in Escherichia coli. Curr. Opin. Biotechnol. 6, 494–500. CrossRef CAS PubMed Web of Science Google Scholar
Kapust, R. B. & Waugh, D. S. (1999). Escherichia coli maltose-binding protein is uncommonly effective at promoting the solubility of polypeptides to which it is fused. Protein Sci. 8, 1668–1674. Web of Science CrossRef PubMed CAS Google Scholar
Ke, A. & Wolberger, C. (2003). Insights into binding cooperativity of MATa1/MATα2 from the crystal structure of a MATa1 homeodomain-maltose binding protein chimera. Protein Sci. 12, 306–312. Web of Science CrossRef PubMed CAS Google Scholar
Kery, V., Elleder, D. & Kraus, J. P. (1995). δ-Aminolevulinate increases heme saturation and yield of human cystathionine β-synthase expressed in Escherichia coli. Arch. Biochem. Biophys. 316, 24–29. CrossRef CAS PubMed Google Scholar
Khoury, G. A., Baliban, R. C. & Floudas, C. A. (2011). Proteome-wide post-translational modification statistics: frequency analysis and curation of the Swiss-Prot database. Sci. Rep. 1, 90. CrossRef Google Scholar
Kim, D.-H. & Kim, M.-S. (2011). Hydrogenases for biological hydrogen production. Bioresour. Technol. 102, 8423–8431. CrossRef CAS PubMed Google Scholar
Klock, H. E., Koesema, E. J., Knuth, M. W. & Lesley, S. A. (2008). Combining the polymerase incomplete primer extension method for cloning and mutagenesis with microscreening to accelerate structural genomics efforts. Proteins, 71, 982–994. Web of Science CrossRef PubMed CAS Google Scholar
Klock, H. E. & Lesley, S. A. (2009). The polymerase incomplete primer extension (PIPE) method applied to high-throughput cloning and site-directed mutagenesis. Methods Mol. Biol. 498, 91–103. CrossRef PubMed CAS Google Scholar
Kobe, B., Center, R. J., Kemp, B. E. & Poumbourios, P. (1999). Crystal structure of human T cell leukemia virus type 1 gp21 ectodomain crystallized as a maltose-binding protein chimera reveals structural evolution of retroviral transmembrane proteins. Proc. Natl Acad. Sci. USA, 96, 4319–4324. Web of Science CrossRef PubMed CAS Google Scholar
Komander, D. & Rape, M. (2012). The ubiquitin code. Annu. Rev. Biochem. 81, 203–229. Web of Science CrossRef CAS PubMed Google Scholar
Konermann, L., Pan, J. & Liu, Y.-H. (2011). Hydrogen exchange mass spectrometry for studying protein structure and dynamics. Chem. Soc. Rev. 40, 1224–1234. CrossRef CAS PubMed Google Scholar
Kong, L., Giang, E., Nieusma, T., Kadam, R. U., Cogburn, K. E., Hua, Y., Dai, X., Stanfield, R. L., Burton, D. R., Ward, A. B., Wilson, I. A. & Law, M. (2013). Hepatitis C virus E2 envelope glycoprotein core structure. Science, 342, 1090–1094. CrossRef CAS PubMed Google Scholar
Kong, L., Huang, C.-C., Coales, S. J., Molnar, K. S., Skinner, J., Hamuro, Y. & Kwong, P. D. (2010). Local conformational stability of HIV-1 gp120 in unliganded and CD4-bound states as defined by amide hydrogen/deuterium exchange. J. Virol. 84, 10311–10321. CrossRef CAS PubMed Google Scholar
Kong, L., Stanfield, R. & Wilson, I. (2014). HIV Glycans in Infection and Immunity, edited by R. Pantophlet, pp. 117–141. New York: Springer. Google Scholar
Kopera, E., Bal, W., Lenarčič Živkovič, M., Dvornyk, A., Kludkiewicz, B., Grzelak, K., Zhukov, I., Zagórski-Ostoja, W., Jaskolski, M. & Krzywda, S. (2014). Atomic resolution structure of a protein prepared by non-enzymatic His-tag removal. Crystallographic and NMR study of GmSPI-2 inhibitor. PLoS One, 9, e106936. CrossRef PubMed Google Scholar
Kottke, T., Hegemann, P., Dick, B. & Heberle, J. (2006). The photochemistry of the light-, oxygen-, and voltage-sensitive domains in the algal blue light receptor phot. Biopolymers, 82, 373–378. CrossRef PubMed CAS Google Scholar
Krężel, A., Kopera, E., Protas, A. M., Poznański, J., Wysłouch-Cieszyńska, A. & Bal, W. (2010). Sequence-specific NiII-dependent peptide bond hydrolysis for protein engineering. Combinatorial library determination of optimal sequences. J. Am. Chem. Soc. 132, 3355–3366. PubMed Google Scholar
Krishnamurthy, H. & Gouaux, E. (2012). X-ray structures of LeuT in substrate-free outward-open and apo inward-open states. Nature (London), 481, 469–474. CrossRef CAS PubMed Google Scholar
Krishnan, V. V. & Rupp, B. (2012). Macromolecular structure determination: comparison of X-ray crystallography and NMR spectroscopy. eLS, doi:10.1002/9780470015902.a0002716.pub2. Google Scholar
Kumar, S., Tsai, C.-J. & Nussinov, R. (2000). Factors enhancing protein thermostability. Protein Eng. Des. Sel. 13, 179–191. CrossRef CAS Google Scholar
Kurgan, L., Razib, A. A., Aghakhani, S., Dick, S., Mizianty, M. & Jahandideh, S. (2009). CRYSTALP2: sequence-based protein crystallization propensity prediction. BMC Struct. Biol. 9, 50. Google Scholar
Kwan, A. H., Mobli, M., Gooley, P. R., King, G. F. & Mackay, J. P. (2011). Macromolecular NMR spectroscopy for the non-spectroscopist. FEBS J. 278, 687–703. CrossRef CAS PubMed Google Scholar
Kyratsous, C. A. & Panagiotidis, C. A. (2012). Heat-shock protein fusion vectors for improved expression of soluble recombinant proteins in Escherichia coli. Methods Mol. Biol. 824, 109–129. CrossRef CAS PubMed Google Scholar
Kyratsous, C. A., Silverstein, S. J., DeLong, C. R. & Panagiotidis, C. A. (2009). Chaperone-fusion expression plasmid vectors for improved solubility of recombinant proteins in Escherichia coli. Gene, 440, 9–15. CrossRef PubMed CAS Google Scholar
Lawson, D. M., Artymiuk, P. J., Yewdall, S. J., Smith, J. M., Livingstone, J. C., Treffry, A., Luzzago, A., Levi, S., Arosio, P., Cesareni, G., Thomas, C. D., Shaw, W. V. & Harrison, P. M. (1991). Solving the structure of human H ferritin by genetically engineering intermolecular crystal contacts. Nature (London), 349, 541–544. CrossRef PubMed CAS Web of Science Google Scholar
Layton, C. J. & Hellinga, H. W. (2011). Quantitation of protein–protein interactions by thermal stability shift analysis. Protein Sci. 20, 1439–1450. CrossRef CAS PubMed Google Scholar
Lazaridis, T. & Karplus, M. (2002). Thermodynamics of protein folding: a microscopic view. Biophys. Chem. 100, 367–395. CrossRef Google Scholar
Leibly, D. J., Nguyen, T. N., Kao, L. T., Hewitt, S. N., Barrett, L. K. & Van Voorhis, W. C. (2012). Stabilizing additives added during cell lysis aid in the solubilization of recombinant proteins. PLoS One, 7, e52482. Web of Science CrossRef PubMed Google Scholar
Li, S.-C., Goto, N. K., Williams, K. A. & Deber, C. M. (1996). α-Helical, but not β-sheet, propensity of proline is determined by peptide environment. Proc. Natl Acad. Sci. USA, 93, 6676–6681. CrossRef CAS PubMed Google Scholar
Lieberman, R. L., Culver, J. A., Entzminger, K. C., Pai, J. C. & Maynard, J. A. (2011). Crystallization chaperone strategies for membrane proteins. Methods, 55, 293–302. Web of Science CrossRef CAS PubMed Google Scholar
Lin, Z., Zhao, Q., Zhou, B., Xing, L. & Xu, W. (2015). Cleavable self-aggregating tags (cSAT) for protein expression and purification. Methods Mol. Biol. 1258, 65–78. CrossRef PubMed Google Scholar
Liu, J. & Song, J. (2009). Insights into protein aggregation by NMR characterization of insoluble SH3 mutants solubilized in salt-free water. PLoS One, 4, e7805. CrossRef PubMed Google Scholar
Liu, Y., Manna, A., Li, R., Martin, W. E., Murphy, R. C., Cheung, A. L. & Zhang, G. (2001). Crystal structure of the SarR protein from Staphylococcus aureus. Proc. Natl Acad. Sci. USA, 98, 6877–6882. Web of Science CrossRef PubMed CAS Google Scholar
Liu, Z. & Huang, Y. (2014). Advantages of proteins being disordered. Protein Sci. 23, 539–550. CrossRef CAS PubMed Google Scholar
Liu, Z.-Q., Mahmood, T. & Yang, P.-C. (2014). Western blot: technique, theory and trouble shooting. N. Am. J. Med. Sci. 6, 160. PubMed Google Scholar
Longenecker, K. L., Garrard, S. M., Sheffield, P. J. & Derewenda, Z. S. (2001). Protein crystallization by rational mutagenesis of surface residues: Lys to Ala mutations promote crystallization of RhoGDI. Acta Cryst. D57, 679–688. Web of Science CrossRef CAS IUCr Journals Google Scholar
Longhi, S., Lieutaud, P. & Canard, B. (2010). Conformational disorder. Methods Mol. Biol. 609, 307–325. CrossRef CAS PubMed Google Scholar
Malakhova, O. A., Yan, M., Malakhov, M. P., Yuan, Y., Ritchie, K. J., Kim, K. I., Peterson, L. F., Shuai, K. & Zhang, D.-E. (2003). Protein ISGylation modulates the JAK-Stat signaling pathway. Genes Dev. 17, 455–460. CrossRef PubMed CAS Google Scholar
Mandel, C. R., Gebauer, D., Zhang, H. & Tong, L. (2006). A serendipitous discovery that in situ proteolysis is essential for the crystallization of yeast CPSF-100 (Ydh1p). Acta Cryst. F62, 1041–1045. Web of Science CrossRef CAS IUCr Journals Google Scholar
Marsh, E. N. (2014). Fluorinated proteins: from design and synthesis to structure and stability. Acc. Chem. Res. 47, 2878–2886. CrossRef CAS PubMed Google Scholar
Mateja, A., Devedjiev, Y., Krowarsch, D., Longenecker, K., Dauter, Z., Otlewski, J. & Derewenda, Z. S. (2002). The impact of Glu→Ala and Glu→Asp mutations on the crystallization properties of RhoGDI: the structure of RhoGDI at 1.3 Å resolution. Acta Cryst. D58, 1983–1991. Web of Science CrossRef CAS IUCr Journals Google Scholar
Mathiasen, S., Christensen, S. M., Fung, J. J., Rasmussen, S. G. F., Fay, J. F., Jorgensen, S. K., Veshaguri, S., Farrens, D. L., Kiskowski, M., Kobilka, B. & Stamou, D. (2014). Nanoscale high-content analysis using compositional heterogeneities of single proteoliposomes. Nature Methods, 11, 931–934. CrossRef CAS PubMed Google Scholar
McPherson, A. & Cudney, B. (2006). Searching for silver bullets: an alternative strategy for crystallizing macromolecules. J. Struct. Biol. 156, 387–406. Web of Science CrossRef PubMed CAS Google Scholar
Means, G. E. (1977). Reductive alkylation of amino groups. Methods Enzymol. 47, 469–478. CrossRef CAS PubMed Google Scholar
Mellquist, J. L., Kasturi, L., Spitalnik, S. L. & Shakin-Eshleman, S. H. (1998). The amino acid following an Asn-X-Ser/Thr sequon is an important determinant of N-linked core glycosylation efficiency. Biochemistry, 37, 6833–6837. CrossRef CAS PubMed Google Scholar
Mills, K. V., Johnson, M. A. & Perler, F. B. (2014). Protein splicing: how inteins escape from precursor proteins. J. Biol. Chem. 289, 14498–14505. CrossRef CAS PubMed Google Scholar
Mizianty, M. J. & Kurgan, L. (2009). Meta prediction of protein crystallization propensity. Biochem. Biophys. Res. Commun. 390, 10–15. Web of Science CrossRef PubMed CAS Google Scholar
Mizianty, M. J., Uversky, V. & Kurgan, L. (2014). Prediction of intrinsic disorder in proteins using MFDp2. Methods Mol. Biol. 1137, 147–162. CrossRef CAS PubMed Google Scholar
Monod, J., Wyman, J. & Changeux, J.-P. (1965). On the nature of allosteric transitions: a plausible model. J. Mol. Biol. 12, 88–118. CrossRef PubMed CAS Web of Science Google Scholar
Moon, A. F., Mueller, G. A., Zhong, X. & Pedersen, L. C. (2010). A synergistic approach to protein crystallization: combination of a fixed-arm carrier with surface entropy reduction. Protein Sci. 19, 901–913. Web of Science CAS PubMed Google Scholar
Morar-Mitrica, S., Nesta, D. & Crotts, G. (2013). Differential scanning calorimetry (DSC) for biopharmaceutical development: old concepts, new applications. BioPharma Asia, 2(4), 44–55. Google Scholar
Moshe, A., Landau, M. & Eisenberg, D. (2016). Preparation of crystalline samples of amyloid fibrils and oligomers. Methods Mol. Biol. 1345, 201–210. CrossRef PubMed Google Scholar
Mottonen, J., Strand, A., Symersky, J., Sweet, R. M., Danley, D. E., Geoghegan, K. F., Gerard, R. D. & Goldsmith, E. J. (1992). Structural basis of latency in plasminogen activator inhibitor-1. Nature (London), 355, 270–273. CrossRef PubMed CAS Web of Science Google Scholar
Moukhametzianov, R., Burghammer, M., Edwards, P. C., Petitdemange, S., Popov, D., Fransen, M., McMullan, G., Schertler, G. F. X. & Riekel, C. (2008). Protein crystallography with a micrometre-sized synchrotron-radiation beam. Acta Cryst. D64, 158–166. Web of Science CrossRef CAS IUCr Journals Google Scholar
Mujtaba, S., He, Y., Zeng, L., Yan, S., Plotnikova, O., Sachchidanand, Sanchez, R., Zeleznik-Le, N. J., Ronai, Z. & Zhou, M.-M. (2004). Structural mechanism of the bromodomain of the coactivator CBP in p53 transcriptional activation. Mol. Cell, 13, 251–263. CrossRef PubMed CAS Google Scholar
Mulder, D. W., Shepard, E. M., Meuser, J. E., Joshi, N., King, P. W., Posewitz, M. C., Broderick, J. B. & Peters, J. W. (2011). Insights into [FeFe]-hydrogenase structure, mechanism, and maturation. Structure, 19, 1038–1052. CrossRef CAS PubMed Google Scholar
Nettleship, J. E., Watson, P. J., Rahman-Huq, N., Fairall, L., Posner, M. G., Upadhyay, A., Reddivari, Y., Chamberlain, J. M., Kolstoe, S. E., Bagby, S., Schwabe, J. W. & Owens, R. J. (2015). Transient expression in HEK 293 cells: an alternative to E. coli for the production of secreted and intracellular mammalian proteins. Methods Mol. Biol. 1258, 209–222. CrossRef PubMed Google Scholar
Neutze, R. & Moffat, K. (2012). Time-resolved structural studies at synchrotrons and X-ray free electron lasers: opportunities and challenges. Curr. Opin. Struct. Biol. 22, 651–659. Web of Science CrossRef CAS PubMed Google Scholar
Novikova, O., Topilina, N. & Belfort, M. (2014). Enigmatic distribution, evolution, and function of inteins. J. Biol. Chem. 289, 14490–14497. CrossRef CAS PubMed Google Scholar
Oldfield, C. J., Meng, J., Yang, J. Y., Yang, M. Q., Uversky, V. N. & Dunker, A. K. (2008). Flexible nets: disorder and induced fit in the associations of p53 and 14-3-3 with their partners. BMC Genomics, 9, S1. Google Scholar
Olson, M. A., Zabetakis, D., Legler, P. M., Turner, K. B., Anderson, G. P. & Goldman, E. R. (2015). Fusion to a highly stable consensus albumin binding domain allows for tunable pharmacokinetics. Protein Eng. Des. Sel. 28, 395–402. CrossRef CAS PubMed Google Scholar
Oltersdorf, T. et al. (2005). An inhibitor of Bcl-2 family proteins induces regression of solid tumours. Nature (London), 435, 677–681. CrossRef PubMed CAS Google Scholar
Overton, I. M., Padovani, G., Girolami, M. A. & Barton, G. J. (2008). ParCrys: a Parzen window density estimation approach to protein crystallization propensity prediction. Bioinformatics, 24, 901–907. Web of Science CrossRef PubMed CAS Google Scholar
Overton, I. M., van Niekerk, C. A. & Barton, G. J. (2011). XANNpred: neural nets that predict the propensity of a protein to yield diffraction-quality crystals. Proteins, 79, 1027–1033. CrossRef CAS PubMed Google Scholar
Pace, C. N., Fu, H., Fryar, K. L., Landua, J., Trevino, S. R., Shirley, B. A., Hendricks, M. M., Iimura, S., Gajiwala, K., Scholtz, J. M. & Grimsley, G. R. (2011). Contribution of hydrophobic interactions to protein stability. J. Mol. Biol. 408, 514–528. CrossRef CAS PubMed Google Scholar
Pace, C. N., Fu, H. et al. (2014). Contribution of hydrogen bonds to protein stability. Protein Sci. 23, 652–661. CrossRef CAS PubMed Google Scholar
Pace, C. N. & Scholtz, J. M. (1998). Biophys. J. 75, 422–427. Web of Science CAS PubMed Google Scholar
Pace, C. N., Scholtz, J. M. & Grimsley, G. R. (2014). Forces stabilizing proteins. FEBS Lett. 588, 2177–2184. PubMed Google Scholar
Pantazatos, D., Kim, J. S., Klock, H. E., Stevens, R. C., Wilson, I. A., Lesley, S. A. & Woods, V. L. Jr (2004). Rapid refinement of crystallographic protein construct definition employing enhanced hydrogen/deuterium exchange MS. Proc. Natl Acad. Sci. USA, 101, 751–756. Web of Science CrossRef PubMed CAS Google Scholar
Papaneophytou, C. P. & Kontopidis, G. (2014). Statistical approaches to maximize recombinant protein expression in Escherichia coli: a general review. Protein Expr. Purif. 94, 22–32. CrossRef CAS PubMed Google Scholar
Park, M. H., Nishimura, K., Zanelli, C. F. & Valentini, S. R. (2010). Functional significance of eIF5A and its hypusine modification in eukaryotes. Amino Acids, 38, 491–500. CrossRef PubMed CAS Google Scholar
Pauling, L., Corey, R. B. & Branson, H. R. (1951). The structure of proteins: two hydrogen-bonded helical configurations of the polypeptide chain. Proc. Natl Acad. Sci. USA, 37, 205–211. CrossRef PubMed CAS Web of Science Google Scholar
Paulus, H. (2000). Protein splicing and related forms of protein autoprocessing. Annu. Rev. Biochem. 69, 447–496. Web of Science CrossRef PubMed CAS Google Scholar
Pelton, J. T. & McLean, L. R. (2000). Spectroscopic methods for analysis of protein secondary structure. Anal. Biochem. 277, 167–176. CrossRef PubMed CAS Google Scholar
Privalov, P. L. & Dragan, A. I. (2007). Microcalorimetry of biological macromolecules. Biophys. Chem. 126, 16–24. CrossRef PubMed CAS Google Scholar
Prothero, J. W. (1966). Correlation between the distribution of amino acids and alpha helices. Biophys. J. 6, 367–370. CrossRef CAS PubMed Google Scholar
Pugach, P. et al. (2015). A native-like SOSIP.664 trimer based on an HIV-1 subtype B env gene. J. Virol. 89, 3380–3395. CrossRef CAS PubMed Google Scholar
Puthalakath, H., Burke, J. & Gleeson, P. A. (1996). Glycosylation defect in Lec1 Chinese hamster ovary mutant is due to a point mutation in N-acetylglucosaminyltransferase I gene. J. Biol. Chem. 271, 27818–27822. CrossRef CAS PubMed Google Scholar
Qing, G., Ma, L.-C., Khorchid, A., Swapna, G. V. T., Mal, T. K., Takayama, M. M., Xia, B., Phadtare, S., Ke, H., Acton, T., Montelione, G. T., Ikura, M. & Inouye, M. (2004). Cold-shock induced high-yield protein production in Escherichia coli. Nature Biotechnol. 22, 877–882. CrossRef CAS Google Scholar
Quistgaard, E. M. (2014). A disulfide polymerized protein crystal. Chem. Commun. 50, 14995–14997. CrossRef CAS Google Scholar
Rabut, G. & Peter, M. (2008). Function and regulation of protein neddylation. EMBO Rep. 9, 969–976. Web of Science CrossRef PubMed CAS Google Scholar
Ramachandran, G. N., Ramakrishnan, C. & Sasisekharan, V. (1963). Stereochemistry of polypeptide chain configurations. J. Mol. Biol. 7, 95–99. CrossRef PubMed CAS Web of Science Google Scholar
Reeves, P. J., Callewaert, N., Contreras, R. & Khorana, H. G. (2002). Structure and function in rhodopsin: high-level expression of rhodopsin with restricted and homogeneous N-glycosylation by a tetracycline-inducible N-acetylglucosaminyltransferase I-negative HEK293S stable mammalian cell line. Proc. Natl Acad. Sci. USA, 99, 13419–13424. Web of Science CrossRef PubMed CAS Google Scholar
Rehm, T., Huber, R. & Holak, T. A. (2002). Application of NMR in structural proteomics: screening for proteins amenable to structural analysis. Structure, 10, 1613–1618. CrossRef PubMed CAS Google Scholar
Reich, S., Puckey, L. H., Cheetham, C. L., Harris, R., Ali, A. A. E., Bhattacharyya, U., Maclagan, K., Powell, K. A., Prodromou, C., Pearl, L. H., Driscoll, P. C. & Savva, R. (2006). Combinatorial domain hunting: an effective approach for the identification of soluble protein domains adaptable to high-throughput applications. Protein Sci. 15, 2356–2365. CrossRef PubMed CAS Google Scholar
Reinhard, L., Mayerhofer, H., Geerlof, A., Mueller-Dieckmann, J. & Weiss, M. S. (2013). Optimization of protein buffer cocktails using Thermofluor. Acta Cryst. F69, 209–214. Web of Science CrossRef IUCr Journals Google Scholar
Remaut, H., Tang, C., Henderson, N. S., Pinkner, J. S., Wang, T., Hultgren, S. J., Thanassi, D. G., Waksman, G. & Li, H. (2008). Fiber formation across the bacterial outer membrane by the chaperone/usher pathway. Cell, 133, 640–652. CrossRef PubMed CAS Google Scholar
Rice, R. H., Means, G. E. & Brown, W. D. (1977). Stabilization of bovine trypsin by reductive methylation. Biochim. Biophys. Acta, 492, 316–321. CrossRef CAS PubMed Google Scholar
Richards, F. M. (1997). Protein stability: still an unsolved problem. Cell. Mol. Life Sci. 53, 790–802. CrossRef CAS PubMed Google Scholar
Richardson, J. S. (1981). The anatomy and taxonomy of protein structure. Adv. Protein Chem. 34, 167–339. CrossRef CAS PubMed Google Scholar
Ristic, M., Rosa, N., Seabrook, S. A. & Newman, J. (2015). Formulation screening by differential scanning fluorimetry: how often does it work? Acta Cryst. F71, 1359–1364. Web of Science CrossRef IUCr Journals Google Scholar
Rogers, S., Wells, R. & Rechsteiner, M. (1986). Amino acid sequences common to rapidly degraded proteins: the PEST hypothesis. Science, 234, 364–368. CrossRef CAS PubMed Google Scholar
Ronda, L., Bruno, S. & Bettati, S. (2013). Tertiary and quaternary effects in the allosteric regulation of animal hemoglobins. Biochim. Biophys. Acta, 1834, 1860–1872. CrossRef CAS PubMed Google Scholar
Rosa, N., Ristic, M., Seabrook, S. A., Lovell, D., Lucent, D. & Newman, J. (2015). Meltdown: a tool to help in the interpretation of thermal melt curves acquired by differential scanning fluorimetry. J. Biomol. Screen. 20, 898–905. CrossRef CAS PubMed Google Scholar
Ruggiero, A., Smaldone, G., Squeglia, F. & Berisio, R. (2012). Enhanced crystallizability by protein engineering approaches: a general overview. Protein Pept. Lett. 19, 732–742. CrossRef CAS PubMed Google Scholar
Rupp, B. (2015). Origin and use of crystallization phase diagrams. Acta Cryst. F71, 247–260. Web of Science CrossRef IUCr Journals Google Scholar
Sanchez-Ruiz, J. M. (1995). Differential scanning calorimetry of proteins. Subcell. Biochem. 24, 133–176. CAS PubMed Google Scholar
Sawaya, M. R., Sambashivan, S., Nelson, R., Ivanova, M. I., Sievers, S. A., Apostol, M. I., Thompson, M. J., Balbirnie, M., Wiltzius, J. J., McFarlane, H. T., Madsen, A. O., Riekel, C. & Eisenberg, D. (2007). Atomic structures of amyloid cross-β spines reveal varied steric zippers. Nature (London), 447, 453–457. Web of Science CrossRef PubMed CAS Google Scholar
Schrödinger, E. (1945). What is Life? The Physical Aspect of the Living Cell. New York: McMillan. Google Scholar
Schuldt, L., Weyand, S., Kefala, G. & Weiss, M. S. (2009). The three-dimensional structure of a mycobacterial DapD provides insights into DapD diversity and reveals unexpected particulars about the enzymatic mechanism. J. Mol. Biol. 389, 863–879. Web of Science CrossRef PubMed CAS Google Scholar
Seabrook, S. A. & Newman, J. (2013). High-throughput thermal scanning for protein stability: making a good technique more robust. ACS Comb. Sci. 15, 387–392. Web of Science CrossRef CAS PubMed Google Scholar
Secchiero, P., Bosco, R., Celeghini, C. & Zauli, G. (2011). Recent advances in the therapeutic perspectives of nutlin-3. Curr. Pharm. Des. 17, 569–577. CrossRef CAS PubMed Google Scholar
Selzer, L., Kant, R., Wang, J. C., Bothner, B. & Zlotnick, A. (2015). Hepatitis B virus core protein phosphorylation sites affect capsid stability and transient exposure of the C-terminal domain. J. Biol. Chem. 290, 28584–28593 CrossRef CAS PubMed Google Scholar
Semisotnov, G. V., Rodionova, N. A., Razgulyaev, O. I., Uversky, V. N., Gripas', A. F. & Gilmanshin, R. I. (1991). Study of the `molten globule' intermediate state in protein folding by a hydrophobic fluorescent probe. Biopolymers, 31, 119–128. CrossRef CAS PubMed Web of Science Google Scholar
Shi, M., Foo, S. Y., Tan, S.-M., Mitchell, E. P., Law, S. K. A. & Lescar, J. (2007). A structural hypothesis for the transition between bent and extended conformations of the leukocyte β2 integrins. J. Biol. Chem. 282, 30198–30206. CrossRef PubMed CAS Google Scholar
Shimizu, K. (2014). POODLE: tools predicting intrinsically disordered regions of amino acid sequence. Methods Mol. Biol. 1137, 131–145. CrossRef CAS PubMed Google Scholar
Shumway, S. D., Maki, M. & Miyamoto, S. (1999). The PEST domain of IκBα is necessary and sufficient for in vitro degradation by μ-calpain. J. Biol. Chem. 274, 30874–30881. CrossRef PubMed CAS Google Scholar
Sikic, K. & Carugo, O. (2009). CARON – average RMSD of NMR structure ensembles. Bioinformation, 4, 132–133. CrossRef PubMed Google Scholar
Slabinski, L., Jaroszewski, L., Rychlewski, L., Wilson, I. A., Lesley, S. A. & Godzik, A. (2007). XtalPred: a web server for prediction of protein crystallizability. Bioinformatics, 23, 3403–3405. Web of Science CrossRef PubMed CAS Google Scholar
Sledz, P., Zheng, H., Murzyn, K., Chruszcz, M., Zimmerman, M. D., Chordia, M. D., Joachimiak, A. & Minor, W. (2010). New surface contacts formed upon reductive lysine methylation: improving the probability of protein crystallization. Protein Sci. 19, 1395–1404. Web of Science CrossRef CAS PubMed Google Scholar
Smagghe, B. J., Huang, P.-S., Ban, Y.-E. A., Baker, D. & Springer, T. A. (2010). Modulation of integrin activation by an entropic spring in the β-knee. J. Biol. Chem. 285, 32954–32966. CrossRef CAS PubMed Google Scholar
Smialowski, P. & Frishman, D. (2010). Protein crystallizability. Methods Mol. Biol. 609, 385–400. CrossRef CAS PubMed Google Scholar
Smith, C. K., Withka, J. M. & Regan, L. (1994). A thermodynamic scale for the β-sheet forming tendencies of the amino acids. Biochemistry, 33, 5510–5517. CrossRef CAS PubMed Web of Science Google Scholar
Smoot, A. L., Panda, M., Brazil, B. T., Buckle, A. M., Fersht, A. R. & Horowitz, P. M. (2001). The binding of bis-ANS to the isolated GroEL apical domain fragment induces the formation of a folding intermediate with increased hydrophobic surface not observed in tetradecameric GroEL. Biochemistry, 40, 4484–4492. Web of Science CrossRef PubMed CAS Google Scholar
Smyth, D. R., Mrozkiewicz, M. K., McGrath, W. J., Listwan, P. & Kobe, B. (2003). Crystal structures of fusion proteins with large affinity tags. Protein Sci. 12, 1313–1322. Web of Science CrossRef PubMed CAS Google Scholar
Somero, G. N. (2004). Adaptation of enzymes to temperature: searching for basic `strategies'. Comp. Biochem. Physiol. B Biochem. Mol. Biol. 139, 321–333. CrossRef PubMed Google Scholar
Sørensen, H. P. & Mortensen, K. K. (2005). Advanced genetic strategies for recombinant protein expression in Escherichia coli. J. Biotechnol. 115, 113–128. PubMed Google Scholar
Spence, J. C., Weierstall, U. & Chapman, H. N. (2012). X-ray lasers for structural and dynamic biology. Rep. Prog. Phys. 75, 102601. Web of Science CrossRef PubMed Google Scholar
Spencer, M. L., Theodosiou, M. & Noonan, D. J. (2004). NPDC-1, a novel regulator of neuronal proliferation, is degraded by the ubiquitin/proteasome system through a PEST degradation motif. J. Biol. Chem. 279, 37069–37078. CrossRef PubMed CAS Google Scholar
Spiegel, H., Schinkel, H., Kastilan, R., Dahm, P., Boes, A., Scheuermayer, M., Chudobová, I., Maskus, D., Fendel, R., Schillberg, S., Reimann, A. & Fischer, R. (2015). Optimization of a multi-stage, multi-subunit malaria vaccine candidate for the production in Pichia pastoris by the identification and removal of protease cleavage sites. Biotechnol. Bioeng. 112, 659–667. CrossRef CAS PubMed Google Scholar
Spraggon, G., Pantazatos, D., Klock, H. E., Wilson, I. A., Woods, V. L. Jr & Lesley, S. A. (2004). On the use of DXMS to produce more crystallizable proteins: structures of the T. maritima proteins TM0160 and TM1171. Protein Sci. 13, 3187–3199. CrossRef PubMed CAS Google Scholar
Stickle, D. F., Presta, L. G., Dill, K. A. & Rose, G. D. (1992). Hydrogen bonding in globular proteins. J. Mol. Biol. 226, 1143–1159. PubMed CAS Web of Science Google Scholar
Striebel, F., Imkamp, F., Özcelik, D. & Weber-Ban, E. (2014). Pupylation as a signal for proteasomal degradation in bacteria. Biochim. Biophys. Acta, 1843, 103–113. CrossRef CAS PubMed Google Scholar
Studier, F. W. (1991). Use of bacteriophage T7 lysozyme to improve an inducible T7 expression system. J. Mol. Biol. 219, 37–44. CrossRef CAS PubMed Web of Science Google Scholar
Sugase, K., Dyson, H. J. & Wright, P. E. (2007). Mechanism of coupled folding and binding of an intrinsically disordered protein. Nature (London), 447, 1021–1025. CrossRef PubMed CAS Google Scholar
Surma, M. A., Szczepaniak, A. & Króliczewski, J. (2014). Comparative studies on detergent-assisted apocytochrome b6 reconstitution into liposomal bilayers monitored by zetasizer instruments. PLoS One, 9, e111341. CrossRef PubMed Google Scholar
Tan, K. et al. (2014). Salvage of failed protein targets by reductive alkylation. Methods Mol. Biol. 1140, 189–200. CrossRef CAS PubMed Google Scholar
Tanner, J. J., Hecht, R. M. & Krause, K. L. (1996). Determinants of enzyme thermostability observed in the molecular structure of Thermus aquaticus D-glyceraldehyde-3-phosphate dehydrogenase at 2.5 Å resolution. Biochemistry, 35, 2597–2609. CrossRef CAS PubMed Web of Science Google Scholar
Tegel, H., Tourle, S., Ottosson, J. & Persson, A. (2010). Increased levels of recombinant human proteins with the Escherichia coli strain Rosetta(DE3). Protein Expr. Purif. 69, 159–167. CrossRef PubMed CAS Google Scholar
Towbin, H., Staehelin, T. & Gordon, J. (1979). Electrophoretic transfer of proteins from polyacrylamide gels to nitrocellulose sheets: procedure and some applications. Proc. Natl Acad. Sci. USA, 76, 4350–4354. CrossRef CAS PubMed Web of Science Google Scholar
Uervirojnangkoorn, M., Zeldin, O. B., Lyubimov, A. Y., Hattne, J., Brewster, A. S., Sauter, N. K., Brunger, A. T. & Weis, W. I. (2015). Enabling X-ray free electron laser crystallography for challenging biological systems from a limited number of crystals. Elife, 4, e05421. Web of Science CrossRef Google Scholar
Uversky, V. N. & Dunker, A. K. (2010). Understanding protein non-folding. Biochim. Biophys. Acta, 1804, 1231–1264. Web of Science CrossRef CAS PubMed Google Scholar
Vasina, J. A. & Baneyx, F. (1997). Expression of aggregation-prone recombinant proteins at low temperatures: a comparative study of the Escherichia coli cspA and tac promoter systems. Protein Expr. Purif. 9, 211–218. CrossRef CAS PubMed Google Scholar
Volkmann, G. & Iwaï, H. (2010). Protein trans-splicing and its use in structural biology: opportunities and limitations. Mol. Biosyst. 6, 2110–2121. Web of Science CrossRef CAS PubMed Google Scholar
Wakatsuki, S. (2016). In Synchrotron Light Sources and Free-Electron Lasers, edited by E. Jaeschke, S. Khan, J. R. Schneider & J. B. Hastings. New York: Springer. In the press. Google Scholar
Walker, C. S., Shetty, R. P., Clark, K., Kazuko, S. G., Letsou, A., Olivera, B. M. & Bandyopadhyay, P. K. (2001). On a potential global role for vitamin K-dependent γ-carboxylation in animal systems: evidence for a γ-glutamyl carboxylase in Drosophila. J. Biol. Chem. 276, 7769–7774. CrossRef PubMed CAS Google Scholar
Wallace, B. A. & Janes, R. W. (2010). Synchrotron radiation circular dichroism (SRCD) spectroscopy: an enhanced method for examining protein conformations and protein interactions. Biochem. Soc. Trans. 38, 861–873. Web of Science CrossRef CAS PubMed Google Scholar
Walls, D. & Loughran, S. T. (2011). Tagging recombinant proteins to enhance solubility and aid purification. Methods Mol. Biol. 681, 151–175. CrossRef CAS PubMed Google Scholar
Walter, T. S., Meier, C., Assenberg, R., Au, K. F., Ren, J., Verma, A., Nettleship, J. E., Owens, R. J., Stuart, D. I. & Grimes, J. M. (2006). Lysine methylation as a routine rescue strategy for protein crystallization. Structure, 14, 1617–1622. Web of Science CrossRef PubMed CAS Google Scholar
Wang, J., Cao, Z., Zhao, L. & Li, S. (2011). Novel strategies for drug discovery based on intrinsically disordered proteins (IDPs). Int. J. Mol. Sci. 12, 3205–3219. CrossRef CAS PubMed Google Scholar
Warne, T., Serrano-Vega, M. J., Baker, J. G., Moukhametzianov, R., Edwards, P. C., Henderson, R., Leslie, A. G. W., Tate, C. G. & Schertler, G. F. X. (2008). Structure of a β1-adrenergic G-protein-coupled receptor. Nature (London), 454, 486–491. Web of Science CrossRef PubMed CAS Google Scholar
Weckert, E. (2015). The potential of future light sources to explore the structure and function of matter. IUCrJ, 2, 230–245. Web of Science CrossRef CAS PubMed IUCr Journals Google Scholar
Wells, J. A. & McClendon, C. L. (2007). Reaching for high-hanging fruit in drug discovery at protein–protein interfaces. Nature (London), 450, 1001–1009. CrossRef PubMed CAS Google Scholar
Wernimont, A. & Edwards, A. (2009). In situ proteolysis to generate crystals for structure determination: an update. PLoS One, 4, e5094. Web of Science CrossRef PubMed Google Scholar
Whisstock, J. C. & Bottomley, S. P. (2006). Molecular gymnastics: serpin structure, folding and misfolding. Curr. Opin. Struct. Biol. 16, 761–768. CrossRef PubMed CAS Google Scholar
Whisstock, J. C., Skinner, R., Carrell, R. W. & Lesk, A. M. (2000). Conformational changes in serpins. I. The native and cleaved conformations of α1-antitrypsin. J. Mol. Biol. 296, 685–699. CrossRef PubMed CAS Google Scholar
Whiteheart, S. W., Shenbagamurthi, P., Chen, L., Cotter, R. J. & Hart, G. W. (1989). Murine elongation factor 1 alpha (EF-1 alpha) is posttranslationally modified by novel amide-linked ethanolamine-phosphoglycerol moieties. Addition of ethanolamine-phosphoglycerol to specific glutamic acid residues on EF-1 alpha. J. Biol. Chem. 264, 14334–14341. CAS PubMed Google Scholar
Whitmore, L. & Wallace, B. A. (2008). Protein secondary structure analyses from circular dichroism spectroscopy: methods and reference databases. Biopolymers, 89, 392–400. Web of Science CrossRef PubMed CAS Google Scholar
Whitmore, L., Woollett, B., Miles, A. J., Klose, D. P., Janes, R. W. & Wallace, B. A. (2011). PCDDB: the Protein Circular Dichroism Data Bank, a repository for circular dichroism spectral and metadata. Nucleic Acids Res. 39, D480–D486. CrossRef CAS PubMed Google Scholar
Willis, B. T. M. & Pryor, A. W. (1975). Thermal Vibrations in Crystallography. Cambridge University Press. Google Scholar
Wood, D. W. (2014). New trends and affinity tag designs for recombinant protein purification. Curr. Opin. Struct. Biol. 26, 54–61. CrossRef CAS PubMed Google Scholar
Wright, P. E. & Dyson, H. J. (1999). Intrinsically unstructured proteins: re-assessing the protein structure–function paradigm. J. Mol. Biol. 293, 321–331. Web of Science CrossRef PubMed CAS Google Scholar
Wukovitz, S. W. & Yeates, T. O. (1995). Why protein crystals favour some space groups over others. Nature Struct. Mol. Biol. 2, 1062–1067. CrossRef CAS Web of Science Google Scholar
Yakimov, A., Rychkov, G. & Petukhov, M. (2014). De novo design of stable α-helices. Methods Mol. Biol. 1216, 1–14. CrossRef CAS PubMed Google Scholar
Yamasaki, M., Li, W., Johnson, D. J. & Huntington, J. A. (2008). Crystal structure of a stable dimer reveals the molecular basis of serpin polymerization. Nature (London), 455, 1255–1258. Web of Science CrossRef PubMed CAS Google Scholar
Yeh, A. P., McMillan, A. & Stowell, M. H. B. (2006). Rapid and simple protein-stability screens: application to membrane proteins. Acta Cryst. D62, 451–457. Web of Science CrossRef CAS IUCr Journals Google Scholar
Yumerefendi, H., Tarendeau, F., Mas, P. J. & Hart, D. J. (2010). ESPRIT: an automated, library-based method for mapping and soluble expression of protein domains from challenging targets. J. Struct. Biol. 172, 66–74. Web of Science CrossRef CAS PubMed Google Scholar
Yun, R. H., Anderson, A. & Hermans, J. (1991). Proline in α-helix: stability and conformation studied by dynamics simulation. Proteins, 10, 219–228. CrossRef PubMed CAS Google Scholar
Zhang, A. P., Bornholdt, Z. A., Liu, T., Abelson, D. M., Lee, D. E., Li, S., Woods, V. L. Jr & Saphire, E. O. (2012). The ebola virus interferon antagonist VP24 directly binds STAT1 and has a novel, pyramidal fold. PLoS Pathog. 8, e1002550. CrossRef PubMed Google Scholar
Zhao, Q., Frederick, R., Seder, K., Thao, S., Sreenath, H., Peterson, F., Volkman, B. F., Markley, J. L. & Fox, B. G. (2004). Production in two-liter beverage bottles of proteins for NMR structure determination labeled with either 15N- or 13C–15N. J. Struct. Funct. Genomics, 5, 87–93. CrossRef PubMed CAS Google Scholar
Zhou, Q. et al. (2015). Architecture of the synaptotagmin–SNARE machinery for neuronal exocytosis. Nature (London), 525, 62–67. CrossRef CAS PubMed Google Scholar
Zhou, X.-X., Wang, Y.-B., Pan, Y.-J. & Li, W.-F. (2008). Differences in amino acids composition and coupling patterns between mesophilic and thermophilic proteins. Amino Acids, 34, 25–33. Web of Science CrossRef PubMed CAS Google Scholar
Zou, Y., Weis, W. I. & Kobilka, B. K. (2012). N-Terminal T4 lysozyme fusion facilitates crystallization of a G protein coupled receptor. PLoS One, 7, e46039. Web of Science CrossRef PubMed Google Scholar
This is an open-access article distributed under the terms of the Creative Commons Attribution (CC-BY) Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.