topical reviews\(\def\hfill{\hskip 5em}\def\hfil{\hskip 3em}\def\eqno#1{\hfil {#1}}\)

IUCrJ
Volume 4| Part 3| May 2017| Pages 291-300
ISSN: 2052-2525

Applications of contact predictions to structural biology

aInstitute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England, bDepartment of Biochemistry, University of Washington, Seattle, WA 98195, USA, cInstitute for Protein Design, University of Washington, Seattle, WA 98195, USA, and dHoward Hughes Medical Institute, University of Washington, Box 357370, Seattle, WA 98195, USA
*Correspondence e-mail: drigden@liverpool.ac.uk

Edited by E. N. Baker, University of Auckland, New Zealand (Received 12 December 2016; accepted 3 April 2017; online 18 April 2017)

Evolutionary pressure on residue interactions, intramolecular or intermolecular, that are important for protein structure or function can lead to covariance between the two positions. Recent methodological advances allow much more accurate contact predictions to be derived from this evolutionary covariance signal. The practical application of contact predictions has largely been confined to structural bioinformatics, yet, as this work seeks to demonstrate, the data can be of enormous value to the structural biologist working in X-ray crystallo­graphy, cryo-EM or NMR. Integrative structural bioinformatics packages such as Rosetta can already exploit contact predictions in a variety of ways. The contribution of contact predictions begins at construct design, where structural domains may need to be expressed separately and contact predictions can help to predict domain limits. Structure solution by molecular replacement (MR) benefits from contact predictions in diverse ways: in difficult cases, more accurate search models can be constructed using ab initio modelling when predictions are available, while intermolecular contact predictions can allow the construction of larger, oligomeric search models. Furthermore, MR using supersecondary motifs or large-scale screens against the PDB can exploit information, such as the parallel or antiparallel nature of any β-strand pairing in the target, that can be inferred from contact predictions. Contact information will be particularly valuable in the determination of lower resolution structures by helping to assign sequence register. In large complexes, contact information may allow the identity of a protein responsible for a certain region of density to be determined and then assist in the orientation of an available model within that density. In NMR, predicted contacts can provide long-range information to extend the upper size limit of the technique in a manner analogous but complementary to experimental methods. Finally, predicted contacts can distinguish between biologically relevant interfaces and mere lattice contacts in a final crystal structure, and have potential in the identification of functionally important regions and in foreseeing the consequences of mutations.

1. Introduction: contact predictions, their potential and their limitations

The accurate prediction of residue contacts in proteins is a long-lasting challenge faced by the scientific community. Today, the prediction of residue contacts is usually performed using programs employing one or both of two algorithms: evolutionary coupling (EC) analysis and supervised machine learning (SML).

EC methods use sequence information alone to identify the coordinated changes of residue pairs in protein families. These coordinated changes between participating residues are typically driven by the evolutionary pressure to preserve the structure and function of the protein. For many years, the prediction of contacting residue pairs by analysis of the coevolutionary pattern of amino acids in a protein family (Fig. 1[link]) was limited by the statistical model applied. The so-called local statistical model was unable to distinguish accurate contact predictions, i.e. direct (AB and BC) covarying pairs of contacting residues, from indirect (AC) pairs of residues that covary but are not in contact. A few years later, but largely forgotten until recently, Lapedes and coworkers were the first to apply a global statistical model to successfully overcome this hurdle (Lapedes et al., 1999[Lapedes, A. S., Giraud, B. G., Liu, L. & Stormo, G. D. (1999). Statistics in Molecular Biology and Genetics, pp. 236-256. Hayward: Institute of Mathematical Statistics. https://doi.org/10.1214/lnms/1215455556.]). More recently, various research groups revisited this concept and through different learning procedures of the same global model were able to improve the precision of the contact predictions dramatically. Whilst some rely on the principle of inverse covariance matrix estimation (Morcos et al., 2011[Morcos, F., Pagnani, A., Lunt, B., Bertolino, A., Marks, D. S., Sander, C., Zecchina, R., Onuchic, J. N., Hwa, T. & Weigt, M. (2011). Proc. Natl Acad. Sci. USA, 108, E1293-E1301.]; Marks et al., 2011[Marks, D. S., Colwell, L. J., Sheridan, R., Hopf, T. A., Pagnani, A., Zecchina, R. & Sander, C. (2011). PLoS One, 6, e28766.]; Jones et al., 2012[Jones, D. T., Buchan, D. W. A., Cozzetto, D. & Pontil, M. (2012). Bioinformatics, 28, 184-190.]), it has been shown that pseudo-likelihood-based approaches result in the most accurate predictions (Balakrishnan et al., 2011[Balakrishnan, S., Kamisetty, H., Carbonell, J. G., Lee, S. & Langmead, C. J. (2011). Proteins, 79, 1061-1078.]; Ekeberg et al., 2013[Ekeberg, M., Lövkvist, C., Lan, Y., Weigt, M. & Aurell, E. (2013). Phys. Rev. E, 87, 012707.]; Kamisetty et al., 2013[Kamisetty, H., Ovchinnikov, S. & Baker, D. (2013). Proc. Natl Acad. Sci. USA, 110, 15674-15679.]). However, these methods strongly rely on the availability of sufficient sequence homologues to be effective (see, for example, Morcos et al., 2011[Morcos, F., Pagnani, A., Lunt, B., Bertolino, A., Marks, D. S., Sander, C., Zecchina, R., Onuchic, J. N., Hwa, T. & Weigt, M. (2011). Proc. Natl Acad. Sci. USA, 108, E1293-E1301.]; Kamisetty et al., 2013[Kamisetty, H., Ovchinnikov, S. & Baker, D. (2013). Proc. Natl Acad. Sci. USA, 110, 15674-15679.]; Jones et al., 2015[Jones, D. T., Singh, T., Kosciolek, T. & Tetchner, S. (2015). Bioinformatics, 31, 999-1006.]; Marks et al., 2012[Marks, D. S., Hopf, T. A. & Sander, C. (2012). Nat. Biotechnol. 30, 1072-1080.]; Ovchinnikov, Kim et al., 2015[Ovchinnikov, S., Kim, D. E., Wang, R. Y., Liu, Y., DiMaio, F. & Baker, D. (2015). Proteins, 84, Suppl. 1, 67-75.]; Skwark et al., 2014[Skwark, M. J., Raimondi, D., Michel, M. & Elofsson, A. (2014). PLoS Comput. Biol. 10, e1003889.]). Nevertheless, together with the accelerating increases in the size of protein sequence databases (UniProt Consortium, 2015[UniProt Consortium (2015). Nucleic Acids Res. 43, D204-D212.]), these have made accurate contact prediction a reality (Marks et al., 2012[Marks, D. S., Hopf, T. A. & Sander, C. (2012). Nat. Biotechnol. 30, 1072-1080.]; de Juan et al., 2013[Juan, D. de, Pazos, F. & Valencia, A. (2013). Nat. Rev. Genet. 14, 249-261.]). Prominent contact-prediction methods and ancillary software, especially those available as servers, are listed in Table 1[link].

Table 1
Key methods in contact prediction or its application available as servers or downloads

Name of method Description Availability URL Citation
HHblits Sequence-alignment generation by database search Web server and local installation https://toolkit.tuebingen.mpg.de/hhblits Remmert et al. (2011[Remmert, M., Biegert, A., Hauser, A. & Söding, J. (2011). Nat. Methods, 9, 173-175.])
Jackhmmer Sequence alignment generation by database search Web server and local installation https://www.ebi.ac.uk/Tools/hmmer/search/jackhmmer Johnson et al. (2010[Johnson, L. S., Eddy, S. R. & Portugaly, E. (2010). BMC Bioinformatics, 11, 431.])
CCMpred Contact-prediction application Local installation https://github.com/soedinglab/CCMpred Seemayer et al. (2014[Seemayer, S., Gruber, M. & Söding, J. (2014). Bioinformatics, 30, 3128-3130.])
MetaPSICOV Intramolecular contact-prediction server Web server and local installation http://bioinf.cs.ucl.ac.uk/MetaPSICOV Jones et al. (2015[Jones, D. T., Singh, T., Kosciolek, T. & Tetchner, S. (2015). Bioinformatics, 31, 999-1006.])
GREMLIN Intramolecular and intermolecular contact-prediction server Web server and local installation http://gremlin.bakerlab.org Ovchinnikov, Kinch et al. (2015[Ovchinnikov, S., Kinch, L., Park, H., Liao, Y., Pei, J., Kim, D. E., Kamisetty, H., Grishin, N. V. & Baker, D. (2015). eLife, 4, e09248.])
RaptorX-Contact Applies an ultradeep learning model to predict contacts: one of the best methods in CASP12 Web server and local installation http://raptorx.uchicago.edu/ContactMap/ Wang et al. (2017[Wang, S., Sun, S., Li, Z., Zhang, R. & Xu, J. (2017). PLoS Comput. Biol. 13, e1005324.])
EVfold Intramolecular and intermolecular contact-prediction server with optional ab initio structure prediction Web server http://evfold.org/evfold-web/evfold.do Marks et al. (2011[Marks, D. S., Colwell, L. J., Sheridan, R., Hopf, T. A., Pagnani, A., Zecchina, R. & Sander, C. (2011). PLoS One, 6, e28766.])
CONFOLD Ab initio structure-prediction server that takes input contacts Web server http://protein.rnet.missouri.edu/confold/ Adhikari et al. (2015[Adhikari, B., Bhattacharya, D., Cao, R. & Cheng, J. (2015). Proteins, 83, 1436-1449.])
ConKit Python interface to contact prediction, visualization and evaluation with command-line scripts available Local installation http://www.conkit.org Simkovic et al. (2017[Simkovic, F., Thomas, J. M. & Rigden, D. J. (2017). Bioinformatics, https://doi.org/10.1093/bioinformatics/btx148.])
ConEVA Contact-prediction evaluation server Web server http://cactus.rnet.missouri.edu/coneva/ Adhikari et al. (2016[Adhikari, B., Nowotny, J., Bhattacharya, D., Hou, J. & Cheng, J. (2016). BMC Bioinformatics, 17, 517.])
MSAVOLVE MATLAB toolbox that includes numerous contact-prediction and related algorithms Local installation http://146.9.23.191/~gatti/coevolution/msavolve---simulation-and.html Gatti (2015[Gatti, L. (2015). Curr. Biotechnol. 4, 16-25.])
Domainpred Perl scripts using kernel density estimation to parse domains from a list of predicted contacts Local installation Not currently available, but similar functionality is available in ConKit Sadowski (2013[Sadowski, M. I. (2013). Proteins, 81, 253-260.])
i-COMS Interprotein COrrelated Mutations Server: a webserver to calculate correlated mutations between proteins Web server http://i-coms.leloir.org.ar/index.php Iserte et al. (2015[Iserte, J., Simonetti, F. L., Zea, D. J., Teppa, E. & Marino-Buslje, C. (2015). Nucleic Acids Res. 43, W320-W325.])
InterEvDock Protein–protein binding mode prediction server that uses contact predictions to help score poses Web server http://mobyle.rpbs.univ-paris-diderot.fr/cgi-bin/portal.py#forms::InterEvDock Yu et al. (2016[Yu, J., Vavrusa, M., Andreani, J., Rey, J., Tufféry, P. & Guerois, R. (2016). Nucleic Acids Res. 44, W542-W549.])
[Figure 1]
Figure 1
A schematic representation of the various points at which contact predictions, derived from multiple protein-sequence alignments (centre), are of use in the course (left to right) of structure determination by X-ray crystallography or cryo-EM. Applications to solution scattering data and NMR experiments are shown at the lower right.

SML methods use a variety of sequence-dependent and sequence-independent information to predict contacting residue pairs (Cheng & Baldi, 2005[Cheng, J. & Baldi, P. (2005). Bioinformatics, 21, i75-i84.]; Shackelford & Karplus, 2007[Shackelford, G. & Karplus, K. (2007). Proteins, 69, Suppl. 8, 159-164.]; González et al., 2013[González, A. J., Liao, L. & Wu, C. H. (2013). Bioinformatics, 29, 1018-1025.]; Wang & Xu, 2013[Wang, Z. & Xu, J. (2013). Bioinformatics, 29, 266-273.]; Zhang et al., 2016[Zhang, H., Huang, Q., Bei, Z., Wei, Y. & Floudas, C. A. (2016). Proteins, 84, 332-348.]; Du et al., 2016[Du, T., Liao, L., Wu, C. & Sun, B. (2016). Methods, 110, 97-105.]). These methods derive contacts by analysing protein features, sequence profiles and mutual information. Although generally inferior to EC methods, SML algorithms can outperform EC algorithms for families with fewer homologous sequences (Skwark et al., 2014[Skwark, M. J., Raimondi, D., Michel, M. & Elofsson, A. (2014). PLoS Comput. Biol. 10, e1003889.]; Wang & Xu, 2013[Wang, Z. & Xu, J. (2013). Bioinformatics, 29, 266-273.]; Ma et al., 2015[Ma, J., Wang, S., Wang, Z. & Xu, J. (2015). Bioinformatics, 31, 3506-3513.]).

Since the SML methods learn and predict residue pairs at the same time, they suffer from a similar inability, as seen with older EC methods employing local statistical models, to distinguish directly and indirectly covarying residue pairs. However, to consider this potentially valuable information, more recent approaches combine methods from both categories: EC and SML. The SML predictions can be used as either priors to EC methods, one such example being GREMLIN (Ovchinnikov, Kinch et al., 2015[Ovchinnikov, S., Kinch, L., Park, H., Liao, Y., Pei, J., Kim, D. E., Kamisetty, H., Grishin, N. V. & Baker, D. (2015). eLife, 4, e09248.]), or the output of multiple EC methods along with sequence profiles can be used as features in SML methods, for example MetaPSICOV (Jones et al., 2015[Jones, D. T., Singh, T., Kosciolek, T. & Tetchner, S. (2015). Bioinformatics, 31, 999-1006.]) and PconsC2 (Skwark et al., 2014[Skwark, M. J., Raimondi, D., Michel, M. & Elofsson, A. (2014). PLoS Comput. Biol. 10, e1003889.]). Pipelines combining various EC and SML methods are often referred to as metapredictors, and a useful comparison of the best methods has recently been published (Wang et al., 2017[Wang, S., Sun, S., Li, Z., Zhang, R. & Xu, J. (2017). PLoS Comput. Biol. 13, e1005324.]). In most cases, metapredictors outperform individual EC or SML methods in contact-prediction accuracy, but the improvement in structure prediction is less clear. The improvement in contact-prediction accuracy is particularly noticeable for cases where the available sequences are fewer or less diverse (de Oliveira et al., 2016[Oliveira, S. H. de, Shi, J. & Deane, C. M. (2016). Bioinformatics, 33, 373-381.]; Wuyun et al., 2016[Wuyun, Q., Zheng, W., Peng, Z. & Yang, J. (2016). Brief Bioinform., https://doi.org/10.1093/bib/bbw106.]). Major resources for contact prediction are listed in Table 1[link].

1.1. The number and diversity of sequences required for accurate contact predictions

Alongside the earliest EC methods implementing a global statistical model, numerous guides have been proposed to approximate the minimum size of a multiple sequence alignment required for useful contact predictions. Originally, 1000 sequence homologues was considered to be the minimum for accurate predictions (Jones et al., 2012[Jones, D. T., Buchan, D. W. A., Cozzetto, D. & Pontil, M. (2012). Bioinformatics, 28, 184-190.]; Marks et al., 2012[Marks, D. S., Hopf, T. A. & Sander, C. (2012). Nat. Biotechnol. 30, 1072-1080.]; Andreani & Söding, 2015[Andreani, J. & Söding, J. (2015). Bioinformatics, 31, 1729-1737.]). More recently, Marks and co­workers have recommended a more sequence-specific length-dependent factor for their method EVFold, whereby the sequence count in the alignment should exceed five times the protein length to obtain good ab initio folding results (Marks et al., 2012[Marks, D. S., Hopf, T. A. & Sander, C. (2012). Nat. Biotechnol. 30, 1072-1080.]). Others have also suggested similar requirements (Kamisetty et al., 2013[Kamisetty, H., Ovchinnikov, S. & Baker, D. (2013). Proc. Natl Acad. Sci. USA, 110, 15674-15679.]), but more recent work has slightly lowered this threshold by either improved covariance-detection algorithms (Ovchinnikov, Kinch et al., 2015[Ovchinnikov, S., Kinch, L., Park, H., Liao, Y., Pei, J., Kim, D. E., Kamisetty, H., Grishin, N. V. & Baker, D. (2015). eLife, 4, e09248.]) or the use, where available, of structural information to decode a predicted contact map (Jeong & Kim, 2016[Jeong, C. & Kim, D. (2016). BMC Bioinformatics, 17, 99.]). However, none of these estimates captures the diversity in a collection of sequences, which is also important for accurate contact prediction, and so they can be misleading. One of the most recent guidelines considers the diversity in a multiple sequence alignment after clustering at around 80% sequence identity: the number of effective sequences. Although different groups vary in their choice of sequence-identity cutoff (Morcos et al., 2011[Morcos, F., Pagnani, A., Lunt, B., Bertolino, A., Marks, D. S., Sander, C., Zecchina, R., Onuchic, J. N., Hwa, T. & Weigt, M. (2011). Proc. Natl Acad. Sci. USA, 108, E1293-E1301.]; Jones et al., 2015[Jones, D. T., Singh, T., Kosciolek, T. & Tetchner, S. (2015). Bioinformatics, 31, 999-1006.]; Skwark et al., 2014[Skwark, M. J., Raimondi, D., Michel, M. & Elofsson, A. (2014). PLoS Comput. Biol. 10, e1003889.]; Hopf et al., 2012[Hopf, T. A., Colwell, L. J., Sheridan, R., Rost, B., Sander, C. & Marks, D. S. (2012). Cell, 149, 1607-1621.]), the overall consensus suggests that the number of effective sequences is the most effective measure of alignment diversity and hence a useful predictor of prediction accuracy. The most recent EC methods require around 100–200 effective sequences for the top contact predictions to be accurate (Jones et al., 2015[Jones, D. T., Singh, T., Kosciolek, T. & Tetchner, S. (2015). Bioinformatics, 31, 999-1006.]; Skwark et al., 2014[Skwark, M. J., Raimondi, D., Michel, M. & Elofsson, A. (2014). PLoS Comput. Biol. 10, e1003889.]). The number of contacts which can be accurately predicted increases with the number of effective sequences: to robustly generate accurate three-dimensional structure models requires roughly fivefold more sequences (Ovchinnikov et al., 2017[Ovchinnikov, S., Park, H., Varghese, N., Huang, P.-S., Pavlopoulos, G. A., Kim, D. E., Kamisetty, H., Kyrpides, N. C. & Baker, D. (2017). Science, 355, 294-298.]).

1.2. The challenge of distinguishing intramolecular and intermolecular contacts

Currently, the methods used to predict residue-contact pairings cannot reliably distinguish intramolecular and intermolecular contacts, yet separating the two is important for the optimal performance of many of the methods mentioned below. Intramolecular residue contacts are generally more straightforward to predict, as a single protein-sequence alignment is sufficient. However, if the target forms homo-oligomers, the final contact prediction will potentially contain both intramolecular and intermolecular contacts if the latter are important for structure and function and hence under evolutionary pressure. Under such circumstances, interpretation of the predicted contacts can be misleading. At the same time, if structural information is available for the monomeric or dimeric protein structure, the predicted contact information can be essential in identifying potentially new oligomeric states by identifying strongly predicted but unsatisfied contact pairs (Hopf et al., 2012[Hopf, T. A., Colwell, L. J., Sheridan, R., Rost, B., Sander, C. & Marks, D. S. (2012). Cell, 149, 1607-1621.]; Jana et al., 2014[Jana, B., Morcos, F. & Onuchic, J. N. (2014). Phys. Chem. Chem. Phys. 16, 6496-6507.]).

Where structural information to disentangle intramolecular and intermolecular contacts in homo-oligomers is not available, accessibility predictions may help: an intermolecular contact is more likely where both of a candidate contacting pair of residues are predicted to lie at the molecular surface. Already used in some contact-prediction algorithms to help the ranking of intramolecular contacts (for example PconsC2; Skwark et al., 2014[Skwark, M. J., Raimondi, D., Michel, M. & Elofsson, A. (2014). PLoS Comput. Biol. 10, e1003889.]), the continued development of solvent-accessible surface-area prediction methods (Heffernan et al., 2015[Heffernan, R., Dehzangi, A., Lyons, J., Paliwal, K., Sharma, A., Wang, J., Sattar, A., Zhou, Y. & Yang, Y. (2015). Bioinformatics, 32, 843-849.]) should facilitate the partitioning of intramolecular and intermolecular contacts in predicted contact maps. For transmembrane proteins, lipophilicity predictions are commonly used to detect membrane-facing residues (Koehler Leman et al., 2015[Koehler Leman, J., Ulmschneider, M. B. & Gray, J. J. (2015). Proteins, 83, 1-24.]), which could help to distinguish intermolecular contacts in the bilayer.

In cases of hetero-oligomeric protein complexes, a different challenge is faced when predicting contact pairs. Although the disentanglement of intramolecular and intermolecular contacts is not required, the generation of the multiple sequence alignment for EC methods is challenging because orthologous interacting pairs of sequences must reliably be identified from a large number of species. Typically, sequences from individual alignments are paired using bacterial genome coordinates, i.e. the closer their location in the genome the more likely their co-expression and physical interaction (Ovchinnikov et al., 2014[Ovchinnikov, S., Kamisetty, H. & Baker, D. (2014). Elife, 3, e02030.]; Hopf et al., 2014[Hopf, T. A., Schärfe, C. P. I., Rodrigues, J. P. G. L. M., Green, A. G., Kohlbacher, O., Sander, C., Bonvin, A. M. J. J. & Marks, D. S. (2014). Elife, 3, 10.]; Skerker et al., 2008[Skerker, J. M., Perchuk, B. S., Siryaporn, A., Lubin, E. A., Ashenberg, O., Goulian, M. & Laub, M. T. (2008). Cell, 133, 1043-1054.]). This information is important because the conservation of protein–protein interactions may not be present amongst all homologues. Using this approach, several studies have shown the applicability of EC methods to hetero-oligomeric protein complexes. For example, Hopf and coworkers correctly predicted 17 out of 19 residue contacts in the interface of the DinJ–YafQ complex (Hopf et al., 2014[Hopf, T. A., Schärfe, C. P. I., Rodrigues, J. P. G. L. M., Green, A. G., Kohlbacher, O., Sander, C., Bonvin, A. M. J. J. & Marks, D. S. (2014). Elife, 3, 10.]). Ovchinnikov and coworkers reliably predicted the few contacting residue pairs between the proteins of the 50S ribosomal subunit complex and other protein complexes (Ovchinnikov et al., 2014[Ovchinnikov, S., Kamisetty, H. & Baker, D. (2014). Elife, 3, e02030.]). In each case, the predicted contact information enabled accurate models of the protein complexes to be determined based on the individual component structures. A second, simpler method has been developed to create sequence alignments for protein–protein interface contact predictions. This method is bacterial genome-independent and matches orthologous sequence pairs using genome BLAST scores (Iserte et al., 2015[Iserte, J., Simonetti, F. L., Zea, D. J., Teppa, E. & Marino-Buslje, C. (2015). Nucleic Acids Res. 43, W320-W325.]; Yu et al., 2016[Yu, J., Vavrusa, M., Andreani, J., Rey, J., Tufféry, P. & Guerois, R. (2016). Nucleic Acids Res. 44, W542-W549.]; Ochoa & Pazos, 2010[Ochoa, D. & Pazos, F. (2010). Bioinformatics, 26, 1370-1371.]). In general, this second alignment-generation method may produce a less accurate set of matched pairs for prokaryotic proteins, but has already proven to be useful for protein–protein interactions in eukaryotes (Iserte et al., 2015[Iserte, J., Simonetti, F. L., Zea, D. J., Teppa, E. & Marino-Buslje, C. (2015). Nucleic Acids Res. 43, W320-W325.]), and new methods may well improve the accuracy for these organisms (Gueudré et al., 2016[Gueudré, T., Baldassi, C., Zamparo, M., Weigt, M. & Pagnani, A. (2016). Proc. Natl Acad. Sci. USA, 113, 12186-12191.]; Bitbol et al., 2016[Bitbol, A. F., Dwyer, R. S., Colwell, L. J. & Wingreen, N. S. (2016). Proc. Natl Acad. Sci. USA, 113, 12180-12185.]). The reader is referred to Table 1[link] for major resources in this area.

2. Predicting the domain structure of the target

Protein domains are the units of folded protein structure. An accurate accounting of the domain composition of a protein as a potential subject of structural characterization is valuable before target selection, when sample preparation is being planned, and when considering strategies for protein crystal structure solution. Accurately defined domain boundaries improve the performance of various key bioinformatics methods, such as tools that recognize distant homology between (part of) the target and known protein structures (Rigden, 2002[Rigden, D. J. (2002). Protein Eng. 15, 65-77.]), ab initio modelling (see, for example, Baker et al., 2016[Baker, J. A., Simkovic, F., Taylor, H. M. & Rigden, D. J. (2016). Proteins, 84, 1431-1442.]) and even contact prediction itself (Kosciolek & Jones, 2015[Kosciolek, T. & Jones, D. T. (2015). Proteins, 84, Suppl. 1, 145-151.]). The recognition of nontrivial evolutionary relationships by sensitive sequence comparisons or (contact-aided) ab initio modelling may help the structural biologist at the early stage of assessing the inherent novelty or otherwise of a new putative target. Commonly, proteins are expressed heterologously in an incomplete form, especially for NMR or X-ray crystallographic studies. There are various reasons for this. For example, intrinsically disordered terminal regions, which are known to impede crystallization (Slabinski et al., 2007[Slabinski, L., Jaroszewski, L., Rychlewski, L., Wilson, I. A., Lesley, S. A. & Godzik, A. (2007). Bioinformatics, 23, 3403-3405.]), would typically be eliminated from a protein destined for crystallization. Very large proteins, the expression and purification of which are likely to prove difficult, will generally be dealt with in sections commencing and terminating at domain boundaries (see, for example, Zacharchenko et al., 2015[Zacharchenko, T., von Castelmur, E., Rigden, D. J. & Mayans, O. (2015). Biochem. Soc. Trans. 43, 850-855.]). Finally, as a methodology that is limited in its tractable molecular-weight range, NMR studies often focus on isolated domains.

Predicting domain boundaries using predicted contacts (Fig. 1[link]) is based on a very simple idea: that native contacts, and hence predicted contacts, are more abundant within domains than between domains. Thus, in a contact map for a protein of two equally sized domains, the area containing predicted contacts between residues in the first half and residues in the second half would be sparsely populated in comparison to the zones containing intra-domain predictions. These patterns are often apparent on visual inspection, and have been employed by bioinformaticians to parse target sequences for ab initio modelling (Ovchinnikov, Kim et al., 2015[Ovchinnikov, S., Kim, D. E., Wang, R. Y., Liu, Y., DiMaio, F. & Baker, D. (2015). Proteins, 84, Suppl. 1, 67-75.]; Baker et al., 2016[Baker, J. A., Simkovic, F., Taylor, H. M. & Rigden, D. J. (2016). Proteins, 84, 1431-1442.]), but can also be analysed quantitatively. For this, putative domain boundaries are sampled along the protein chain, with stronger predictions corresponding to minima in the density of predicted interdomain contacts (Rigden, 2002[Rigden, D. J. (2002). Protein Eng. 15, 65-77.]). Dating from an epoch of lower quality predictions, the idea has been revisited recently and implemented using a kernel-smoothing method (Sadowski, 2013[Sadowski, M. I. (2013). Proteins, 81, 253-260.]). A comparison with other methods of sequence-based domain-boundary prediction showed it to be the best performing and also to be applicable to proteins containing more than two domains (Sadowski, 2013[Sadowski, M. I. (2013). Proteins, 81, 253-260.]). That domainpred software would be the recommended approach for structural biologists interested in predicting the domain composition of their proteins of interest, but it seems currently unavailable. However, similar functionality has been made available in ConKit (Table 1[link]) which can accept the required list of predicted contacts in a wide variety of formats. Finally, interesting recent work has also demonstrated the ability of covariance analysis to detect putative folding units within largely intrinsically disordered proteins (Toth-Petroczy et al., 2016[Toth-Petroczy, A., Palmedo, P., Ingraham, J., Hopf, T. A., Berger, B., Sander, C. & Marks, D. S. (2016). Cell, 167, 158-170.e12.]).

3. Applications to crystal structure determination

For protein crystal structure solution the phasing problem – the ability to only directly measure intensity data – must be overcome using experimental or computational means. Contact prediction is most relevant to molecular replacement (MR) as a computational route to structure solution (Fig. 1[link]). In MR, a `search model' that is believed to approximate at least a part of the unknown target structure is positioned in the symmetric unit, usually by sequential rotation and translation steps. This placed structure can then be used as a source of approximate phase information, allowing the calculation of initial electron-density maps. However, before considering MR specifically it is worth reiterating the value, for all phasing approaches, of a comprehensive understanding of the domain structure of the protein target.

3.1. Better characterizing the target

Recognized evolutionary relationships between (domains of) the target and known protein structures or families can valuably predict the existence of features facilitating experimental structure solution. For example, metal-binding, base-binding or cofactor-binding sites can each ligate natural ligands, or artificial analogues thereof, containing atoms with useful anomalous scattering properties and/or high masses. Single-crystal or multi-crystal diffraction data in such cases are suitable for solution by anomalous scattering and/or isomorphous replacement approaches (Dauter, 2002[Dauter, Z. (2002). Curr. Opin. Struct. Biol. 12, 674-678.]; Hendrickson, 2014[Hendrickson, W. A. (2014). Q. Rev. Biophys. 47, 49-93.]). Prominent methods for detecting even distant homologies include HHpred (Söding et al., 2005[Söding, J., Biegert, A. & Lupas, A. N. (2005). Nucleic Acids Res. 33, W244-W248.]; https://toolkit.tuebingen.mpg.de/hhpred), Phyre (Kelley et al., 2015[Kelley, L. A., Mezulis, S., Yates, C. M., Wass, M. N. & Sternberg, M. J. (2015). Nat. Protoc. 10, 845-858.]; http://www.sbg.bio.ic.ac.uk/~phyre2/) and FFAS-3D (Xu et al., 2014[Xu, D., Jaroszewski, L., Li, Z. & Godzik, A. (2014). Bioinformatics, 30, 660-667.]; http://ffas.godziklab.org). Such methods to recognize hidden evolutionary relationships between the target and known structures benefit from contact prediction in two ways. Firstly, the improved domain parsing described above can improve the sensitivity of homology-detection tools: known folds or families can be more confidently matched to sub­sections of the target encompassing individual domains than they can to a whole multi-domain sequence (Rigden, 2002[Rigden, D. J. (2002). Protein Eng. 15, 65-77.]). Secondly, fold-recognition methods may, in the near future, be able to directly exploit predicted contact information: putative matched folds that are in accord with the predicted contacts for the target, according to the alignment of the two, can be awarded a higher score. Work in this area has recently been published (Ovchinnikov et al., 2017[Ovchinnikov, S., Park, H., Varghese, N., Huang, P.-S., Pavlopoulos, G. A., Kim, D. E., Kamisetty, H., Kyrpides, N. C. & Baker, D. (2017). Science, 355, 294-298.]; Taylor, 2016[Taylor, W. R. (2016). Algorithms Mol. Biol. 11, 17.]).

3.2. Deriving and ranking search models for MR

Predicted contacts can help to derive better search models by informing on the super-secondary, tertiary and quaternary structure of the target (Fig. 1[link]). Perhaps the most obvious application lies in using contact predictions to build better structure models. Structural bioinformaticians have been quick to exploit predicted contact information to model representatives of structurally uncharacterized protein families (Ovchinnikov, Kinch et al., 2015[Ovchinnikov, S., Kinch, L., Park, H., Liao, Y., Pei, J., Kim, D. E., Kamisetty, H., Grishin, N. V. & Baker, D. (2015). eLife, 4, e09248.]; Hopf et al., 2012[Hopf, T. A., Colwell, L. J., Sheridan, R., Rost, B., Sander, C. & Marks, D. S. (2012). Cell, 149, 1607-1621.]). While the results are typically sufficient for very valuable functional inference by fold matching (Ovchinnikov, Kinch et al., 2015[Ovchinnikov, S., Kinch, L., Park, H., Liao, Y., Pei, J., Kim, D. E., Kamisetty, H., Grishin, N. V. & Baker, D. (2015). eLife, 4, e09248.]), the overall moderate accuracy of the final models, compounded in some cases by poor backbone stereochemistry (Marks et al., 2011[Marks, D. S., Colwell, L. J., Sheridan, R., Hopf, T. A., Pagnani, A., Zecchina, R. & Sander, C. (2011). PLoS One, 6, e28766.]), left open the question as to their value to MR. Addressing this issue, Simkovic and coworkers recently explored the value of contact-assisted ab initio models in the context of the AMPLE cluster-and-truncate search-model preparation framework (Simkovic et al., 2016[Simkovic, F., Thomas, J. M. H., Keegan, R. M., Winn, M. D., Mayans, O. & Rigden, D. J. (2016). IUCrJ, 3, 259-270.]). The work compared unassisted models, those informed by the predictions from the general method PconsC2 (Skwark et al., 2014[Skwark, M. J., Raimondi, D., Michel, M. & Elofsson, A. (2014). PLoS Comput. Biol. 10, e1003889.]), and those guided by a novel combination of PconsC2 with a β-sheet-specific method, bbcontacts (Andreani & Söding, 2015[Andreani, J. & Söding, J. (2015). Bioinformatics, 31, 1729-1737.]). In a set of 21 cases, spanning sizes of 62–221 residues, resolutions of 1.0–2.3 Å and all fold classes, they found multiple targets that could only be solved using models informed by predicted contact information. The benefits of this information were twofold: better modelling of larger proteins extended the upper size limit of the method, and β-rich proteins, which were previously very rarely successful (Bibby et al., 2012[Bibby, J., Keegan, R. M., Mayans, O., Winn, M. D. & Rigden, D. J. (2012). Acta Cryst. D68, 1622-1631.]), were successfully solved more frequently. A quite independent relevance of predicted contacts to AMPLE's search-model preparation comes from the realisation that predicted contacts, rather like sequence conservation, derive from evolutionary pressure to retain biologically important structural features (see §[link]6). Thus, predicted contacts may help to identify the key features shared between a target that are known or suspected to be only distantly related to deposited structures. The ability of contact predictions to guide search-model preparation, even of single homologues, using AMPLE's truncation approach is currently being explored.

Finally, for tertiary structure, intriguing recent work points to a general ability of contact predictions to enable predictions to be made about alternative conformations of a given structure (Jana et al., 2014[Jana, B., Morcos, F. & Onuchic, J. N. (2014). Phys. Chem. Chem. Phys. 16, 6496-6507.]; Sfriso et al., 2016[Sfriso, P., Duran-Frigola, M., Mosca, R., Emperador, A., Aloy, P. & Orozco, M. (2016). Structure, 24, 116-126.]). The rationale here is that any biologically important conformation will lead to evolutionary pressure on relevant contacts that would manifest itself as a detectable covariance between the pair of positions involved. This phenomenon was noted previously during contact-based modelling, where a single modelled structure proved incapable of explaining fully the pattern of covarying residue pairs since the predicted contacts resulted from two distinct conformations (Hopf et al., 2012[Hopf, T. A., Colwell, L. J., Sheridan, R., Rost, B., Sander, C. & Marks, D. S. (2012). Cell, 149, 1607-1621.]). This opens the way to convert a single structure of a homologue of the template to a set of putative conformations (Sfriso et al., 2016[Sfriso, P., Duran-Frigola, M., Mosca, R., Emperador, A., Aloy, P. & Orozco, M. (2016). Structure, 24, 116-126.]) to trial by MR. This might enable successful structure solution in frequently encountered cases where a protein exhibits structural plasticity (open and closed forms, R- and T-state etc.) yet the target crystal is not in a conformation represented by the PDB.

The availability of the bbcontacts algorithm (Andreani & Söding, 2015[Andreani, J. & Söding, J. (2015). Bioinformatics, 31, 1729-1737.]), which can sensitively detect and distinguish parallel and antiparallel β-sheet predictions in a predicted contact map, also offers a route to search-model ranking for library-based MR methods (Fig. 1[link]). Such programs include ARCIMBOLDOBORGES, which attempts structure solution using libraries of recurring super-secondary structures composed of a few regular secondary-structure elements derived from an analysis of the PDB (Sammito et al., 2013[Sammito, M., Millán, C., Rodríguez, D. D., de Ilarduya, I. M., Meindl, K., De Marino, I., Petrillo, G., Buey, R. M., de Pereda, J. M., Zeth, K., Sheldrick, G. M. & Usón, I. (2013). Nat. Methods, 10, 1099-1101.]). These libraries are relatively large, but the runtimes for structure solution could be reduced by assigning parallel and/or antiparallel β-sheets to the target and ordering the processing of search models to prioritize those containing the correct kind of strand matching. Similarly, approaches based on screening of the whole PDB (Keegan et al., 2016[Keegan, R., Waterman, D. G., Hopper, D. J., Coates, L., Taylor, G., Guo, J., Coker, A. R., Erskine, P. T., Wood, S. P. & Cooper, J. B. (2016). Acta Cryst. D72, 933-943.]; Stokes-Rees & Sliz, 2010[Stokes-Rees, I. & Sliz, P. (2010). Proc. Natl Acad. Sci. USA, 107, 21476-21481.]) may also rank search models according to the predicted β-sheet composition of the target.

Predicting the quaternary structure of the target may also be valuable for MR and is relevant for both homo-oligomers and hetero-oligomers (Fig. 1[link]). In essence, the data-driven docking approaches developed in structural bioinformatics to exploit predicted contact information can be used to derive and rank oligomeric search models. In comparison to individual subunits, these contain a greater fraction of the scattering matter of the target and therefore, if sufficiently accurate, should exhibit improved signal to noise and hence a better chance of successful structure solution. A single docking server, InterEvDock, that automatically incorporates evolutionary covariance into its calculations has very recently become available (Yu et al., 2016[Yu, J., Vavrusa, M., Andreani, J., Rey, J., Tufféry, P. & Guerois, R. (2016). Nucleic Acids Res. 44, W542-W549.]; see Table 1[link]). It carries out rigid-body docking of two structures using FRODOCK (Ramírez-Aportela et al., 2016[Ramírez-Aportela, E., López-Blanco, J. R. & Chacón, P. (2016). Bioinformatics, 32, 2386-2388.]). A pool of 10 000 poses is then scored in three different fashions, one being a residue-based co-evolution score derived from the i-COMS server (Iserte et al., 2015[Iserte, J., Simonetti, F. L., Zea, D. J., Teppa, E. & Marino-Buslje, C. (2015). Nucleic Acids Res. 43, W320-W325.]; Ochoa & Pazos, 2010[Ochoa, D. & Pazos, F. (2010). Bioinformatics, 26, 1370-1371.]; Table 1[link]), and the server reports the top ten consensus models found by clustering the best scoring poses by each of the three evaluations. A crystallo­grapher might also reproduce approaches in which predicted contacts either guide docking (Hopf et al., 2014[Hopf, T. A., Schärfe, C. P. I., Rodrigues, J. P. G. L. M., Green, A. G., Kohlbacher, O., Sander, C., Bonvin, A. M. J. J. & Marks, D. S. (2014). Elife, 3, 10.]) with HADDOCK (Dominguez et al., 2003[Dominguez, C., Boelens, R. & Bonvin, A. M. J. J. (2003). J. Am. Chem. Soc. 125, 1731-1737.]) or rank the results of docking with PatchDock v.1.0 (Duhovny et al., 2002[Duhovny, D., Nussinov, R. & Wolfson, H. J. (2002). Algorithms in Bioinformatics, edited by R. Guigó & D. Gusfield, pp. 185-200. Berlin, Heidelberg: Springer-Verlag. https://doi.org/10.1007/3-540-45784-4_14.]) and refine with Rosetta (Ovchinnikov et al., 2014[Ovchinnikov, S., Kamisetty, H. & Baker, D. (2014). Elife, 3, e02030.]). At present, most docking servers are not optimized to exploit predicted contact information: they may accept sets of residues on each docked protein believed to be close to the interface, but do not accept paired predicted contacts. Although this can be expected to change in the near future, a user would currently be obliged to inspect the results manually to determine whether high-ranking intermolecular contacts are present in poses from some top-performing servers such as ClusPro (Comeau et al., 2004[Comeau, S. R., Gatchell, D. W., Vajda, S. & Camacho, C. J. (2004). Bioinformatics, 20, 45-50.]) or servers specialized for the flexible docking of protein partners such as SwarmDock (Torchala et al., 2013[Torchala, M., Moal, I. H., Chaleil, R. A., Fernandez-Recio, J. & Bates, P. A. (2013). Bioinformatics, 29, 807-809.]). Finally, it is worth reiterating here the additional difficulties of contact prediction between two different proteins: a concatenated alignment in which orthologues of each are matched between a series of species is required. Reliable identification of such pairs is not trivial. For this reason, some current leading methods such as GREMLIN (Ovchinnikov et al., 2014[Ovchinnikov, S., Kamisetty, H. & Baker, D. (2014). Elife, 3, e02030.]) have thus far focused on cases in which microbial genome-context information provides additional support for orthologue identification. Of course, for contacts in homo-oligomers these limitations do not apply. Prediction of these assemblies will be particularly reliable in cases such as membrane pores, where symmetry provides an additional useful restraint on docking (see, for example, DiMaio, Leaver-Fay et al., 2011[DiMaio, F., Leaver-Fay, A., Bradley, P., Baker, D. & André, I. (2011). PLoS One, 6, e20450.]).

4. Fitting structures and tracing sequences in lower resolution maps and envelopes

The outcomes of structural biology methods that aim to yield atomic models, such as X-ray crystallography and, increasingly, cryo-EM, depend sensitively on the data available. Only at ultrahigh resolution can X-ray structures be accurately refined using the X-ray diffraction data alone since the data-to-parameter is too low (Rupp, 2009[Rupp, B. (2009). Biomolecular Crystallography: Principles, Practice, and Application to Structural Biology, p. 627. New York: Garland Science.]). More typically, the refinement of crystal structures employs additional information to supplement the observed diffraction data, most obviously chemical information such as bond distances but also, where available, additional restraints from noncrystallographic symmetry. At lower resolutions, however, even this additional information may prove to be inadequate for atomic refinement, and reconstructions may therefore comprise only structures or models for individual subunits or domains placed within a low-resolution map or envelope and rigid-body refined. It is in the area of lower resolution structure interpretation that contact predictions have the most to offer by providing additional restraints that should be satisfied by the emerging structural model (Fig. 1[link]). These predictions will therefore help not only medium- to low-resolution crystal structures and cryo-EM reconstructions, but also the interpretation of envelopes derived from SAXS and SANS (Svergun et al., 2013[Svergun, D. I., Koch, M. H. J., Timmins, P. A. & May, R. P. (2013). Small Angle X-ray and Neutron Scattering from Solutions of Biological Macromolecules. Oxford University Press.]). Applications can be divided into those dependent on intramolecular contact predictions and those deriving from intermolecular restraints.

Intramolecular contacts are valuable here in several ways, as already mentioned. For novel folds for which low-resolution data are available, contacts will enable better models to be derived for subsequent fitting into maps or envelopes. These would be cases such as ribosomal structures (see, for example, Brown et al., 2014[Brown, A., Amunts, A., Bai, X.-C., Sugimoto, Y., Edwards, P. C., Murshudov, G., Scheres, S. H. W. & Ramakrishnan, V. (2014). Science, 346, 718-722.]), where supernumerary subunits could be modelled ab initio and fitted using the approach mentioned above. In a more recent study, Rosetta-generated ab initio models, guided by evolutionary restraints, were used to resolve the amino-acid registry, the connectivity of the helices and the placement of the subunits of the cytochrome bd oxidase complex in a low-resolution (3.1–4 Å) electron-density map derived from weak experimental phase information (Safarian et al., 2016[Safarian, S., Rajendran, C., Müller, H., Preu, J., Langer, J. D., Ovchinnikov, S., Hirose, T., Kusumoto, T., Sakamoto, J. & Michel, H. (2016). Science, 352, 583-586.]). In cases where a structure, experimental or modelled, cannot be fitted well to the map or envelope, the prediction of alternative conformations using predicted contacts (Sfriso et al., 2016[Sfriso, P., Duran-Frigola, M., Mosca, R., Emperador, A., Aloy, P. & Orozco, M. (2016). Structure, 24, 116-126.]) may produce better fitting candidate structures.

The assignment of sequence register to a low-resolution, backbone-traced structure is another potential area of application (Fig. 1[link]). Programs such as Buccaneer (Cowtan, 2006[Cowtan, K. (2006). Acta Cryst. D62, 1002-1011.]) and ARP/wARP (Langer et al., 2008[Langer, G., Cohen, S. X., Lamzin, V. S. & Perrakis, A. (2008). Nat. Protoc. 3, 1171-1179.]) recognize side-chain density shape and attempt to dock putatively assigned residues to a provided sequence. However, below a certain resolution the number of assigned residues and the confidence of their identification will drop. At this point contact predictions may help: a strong prediction from one residue that is already docked to the sequence to another ill-defined position may anchor sequence-register definition for a whole range of the target protein. One example of such an application is the successful tracing of the protein sequence of the a subunit of Thermus thermophilus V/A-ATPase in a 6.4 Å resolution cryo-EM density map, which resulted in a complete model of the rotary ATPase (Schep et al., 2016[Schep, D. G., Zhao, J. & Rubinstein, J. L. (2016). Proc. Natl Acad. Sci. USA, 113, 3245-3250.]). Covariance analysis was also used to confirm the helical assignments of the 2.95 Å resolution crystal structure of a human tetraspanin (Zimmerman et al., 2016[Zimmerman, B., Kelly, B., McMillan, B. J., Seegar, T. C., Dror, R. O., Kruse, A. C. & Blacklow, S. C. (2016). Cell, 167, 1041-1051.]). There is therefore a need to make predicted contact information conveniently available from within structure-building and refinement programs.

As mentioned above, predicted intermolecular contacts offer a generic way to rank and select the most likely interaction mode of a pair of structures (Ovchinnikov et al., 2014[Ovchinnikov, S., Kamisetty, H. & Baker, D. (2014). Elife, 3, e02030.]; Hopf et al., 2014[Hopf, T. A., Schärfe, C. P. I., Rodrigues, J. P. G. L. M., Green, A. G., Kohlbacher, O., Sander, C., Bonvin, A. M. J. J. & Marks, D. S. (2014). Elife, 3, 10.]; Yu et al., 2016[Yu, J., Vavrusa, M., Andreani, J., Rey, J., Tufféry, P. & Guerois, R. (2016). Nucleic Acids Res. 44, W542-W549.]). The most obvious application is therefore to assist in the interpretation of density for multi-subunit complexes. Proteins that are significantly anisotropic can often be fitted quite reliably even at lower density, but three-dimensional forms with fewer features often fit equally well to a map or envelope in several ways (Joseph et al., 2016[Joseph, A. P., Swapna, L. S., Rakesh, R. & Srinivasan, N. (2016). J. Struct. Biol. 195, 294-305.]). Disambiguating these situations using sequence conservation has recently been explored (Joseph et al., 2016[Joseph, A. P., Swapna, L. S., Rakesh, R. & Srinivasan, N. (2016). J. Struct. Biol. 195, 294-305.]), but predicted contacts arguably offer a more direct signal of intermolecular interaction and are independent of existing interaction information (Segura et al., 2016[Segura, J., Sanchez-Garcia, R., Tabas-Madrid, D., Cuenca-Alba, J., Sorzano, C. O. & Carazo, J. M. (2016). Biophys. J. 110, 766-775.]). For example, in the cytochrome bd oxidase work mentioned above, covariation information was used to confirm the intermolecular interactions resulting from the placement of the covariance-assisted ab initio models (Safarian et al., 2016[Safarian, S., Rajendran, C., Müller, H., Preu, J., Langer, J. D., Ovchinnikov, S., Hirose, T., Kusumoto, T., Sakamoto, J. & Michel, H. (2016). Science, 352, 583-586.]). In the future, such information could be used in several fashions. Use might first entail the prior generation of range of potential multimeric structures, each in broad agreement with the predicted contact signal, in the expectation that one might fit much better than the others. Such a library could also be used for direct fitting to experimental scattering information (see, for example, Schindler et al., 2016[Schindler, C. E., de Vries, S. J., Sasse, A. & Zacharias, M. (2016). Structure, 24, 1387-1397.]; Jimenez-Garcia et al., 2015[Jiménez-García, B., Pons, C., Svergun, D. I., Bernadó, P. & Fernández-Recio, J. (2015). Nucleic Acids Res. 43, W356-W361.]). Secondly, putative fits for a first subunit could be visually inspected for those that are compatible with the placement of the second subunit in such a way as to satisfy the predicted contacts. Thirdly, programs for the automated fitting of structures to density such as gamma-TEMPy (Pandurangan et al., 2015[Pandurangan, A. P., Vasishtan, D., Alber, F. & Topf, M. (2015). Structure, 23, 2365-2376.]) or 3DIANA (Segura et al., 2016[Segura, J., Sanchez-Garcia, R., Tabas-Madrid, D., Cuenca-Alba, J., Sorzano, C. O. & Carazo, J. M. (2016). Biophys. J. 110, 766-775.]) could be engineered to directly include satisfaction of predicted contact information in their scoring functions. More speculatively, predicted contacts may ultimately inform not just on the orientation of the known subunits in a complex but also on the composition of a complex, information that may be only incompletely available. Thus, future genome-scale screening to find which proteins share covarying residue pairs with which others, and thereby assemble an in silico interactome, has already been envisaged (Hopf et al., 2014[Hopf, T. A., Schärfe, C. P. I., Rodrigues, J. P. G. L. M., Green, A. G., Kohlbacher, O., Sander, C., Bonvin, A. M. J. J. & Marks, D. S. (2014). Elife, 3, 10.]). Such information might help the structural biologist synthesize, purify and reconstruct all necessary components of the stable, biologically relevant macromolecular complex.

5. Nuclear magnetic resonance

NMR is a method in which the use of labelling strategies to provide additional restraints, particularly long-range distance restraints, to guide folding has been key to extending the upper bound on tractable molecular weight to larger proteins (Raman et al., 2010[Raman, S., Lange, O. F., Rossi, P., Tyka, M., Wang, X., Aramini, J., Liu, G., Ramelot, T. A., Eletsky, A., Szyperski, T., Kennedy, M. A., Prestegard, J., Montelione, G. T. & Baker, D. (2010). Science, 327, 1014-1018.]; Lange et al., 2012[Lange, O. F., Rossi, P., Sgourakis, N. G., Song, Y., Lee, H. W., Aramini, J. M., Ertekin, A., Xiao, R., Acton, T. B., Montelione, G. T. & Baker, D. (2012). Proc. Natl Acad. Sci. USA, 109, 10873-10878.]; Göbl et al., 2014[Göbl, C., Madl, T., Simon, B. & Sattler, M. (2014). Prog. Nucl. Magn. Reson. Spectrosc. 80, 26-63.]). For RNA and protein–RNA complexes, additional restraints have been derived from EPR information (Duss et al., 2014[Duss, O., Yulikov, M., Jeschke, G. & Allain, F. H.-T. (2014). Nat. Commun. 5, 3669.], 2015[UniProt Consortium (2015). Nucleic Acids Res. 43, D204-D212.]), and fluorescence can also provide distance restraints (Göbl et al., 2014[Göbl, C., Madl, T., Simon, B. & Sattler, M. (2014). Prog. Nucl. Magn. Reson. Spectrosc. 80, 26-63.]). However, researchers have also been quick to perceive the value of predicted contacts derived from evolutionary covariance (Tang et al., 2015[Tang, Y., Huang, Y. J., Hopf, T. A., Sander, C., Marks, D. S. & Montelione, G. T. (2015). Nat. Methods, 12, 751-754.]) which, in comparison to experimental methods, avoid complications relating to the labelling of the macromolecule (Duss et al., 2015[Duss, O., Yulikov, M., Allain, F. H.-T. & Jeschke, G. (2015). Methods Enzymol. 558, 279-331.]) and to any modification-induced change to the structure, dynamics or function of the target. The major effort in the area so far is EC-NMR (Tang et al., 2015[Tang, Y., Huang, Y. J., Hopf, T. A., Sander, C., Marks, D. S. & Montelione, G. T. (2015). Nat. Methods, 12, 751-754.]), in which CYANA is used to generate structural ensembles based on both NMR data (NMR resonance assignments for 1H–15N and/or 1H–13C methyl resonances and NOESY cross-peaks) and covariance-based predicted contacts. These ensembles are then used in an iterative fashion to revisit and edit the input data, with the one data type providing an internal check on the other, enabling the elimination of incorrect NOESY peaks and false-positive contact predictions. The method thus elegantly exploits the complementarity of the two data sources, ultimately producing structures based on refined and improved input data sets. Contact-prediction information can also be fed into CS-Rosetta since Rosetta's sampling and scoring functions have proven to be highly effective for structure determination by NMR (Raman et al., 2010[Raman, S., Lange, O. F., Rossi, P., Tyka, M., Wang, X., Aramini, J., Liu, G., Ramelot, T. A., Eletsky, A., Szyperski, T., Kennedy, M. A., Prestegard, J., Montelione, G. T. & Baker, D. (2010). Science, 327, 1014-1018.]; van der Schot & Bonvin, 2015[Schot, G. van der & Bonvin, A. M. J. J. (2015). J. Biomol. NMR, 62, 497-502.]).

6. Structural analysis and interpretation

When analysing a refined crystal structure, it is not always straightforward to distinguish biologically meaningful interactions between subunits from those intermolecular contacts that simply result from the formation of a crystal lattice (Capitani et al., 2016[Capitani, G., Duarte, J. M., Baskaran, K., Bliven, S. & Somody, J. C. (2016). Bioinformatics, 32, 481-489.]). Years of research suggest that no single metric of interfaces can partition physiologically relevant interactions from mere crystal contacts (Jones & Thornton, 1996[Jones, S. & Thornton, J. M. (1996). Proc. Natl Acad. Sci. USA, 93, 13-20.]), so that current state-of-the-art approaches such as jsPISA offer multiple relevant measurements such as interface area, hydrophobicity and predicted binding energy (Krissinel, 2015[Krissinel, E. (2015). Nucleic Acids Res. 43, W314-W319.]). Contact predictions offer a further appealing way to distinguish the two interface classes (Fig. 1[link]) since, as mentioned at the outset, evolutionary covariance spanning an interface will only emerge where pressure to maintain the interaction has been exerted during evolution. Although complications will emerge when, for example, homologous proteins genuinely differ in their oligomeric state, notably successful use of contact predictions to help parse crystal structure contents has already been seen with structures of protocadherin domain fragments (Nicoludis et al., 2015[Nicoludis, J. M., Lau, S.-Y., Schärfe, C. P. I., Marks, D. S., Weihofen, W. A. & Gaudet, R. (2015). Structure, 23, 2087-2098.]). There, contact predictions supported certain interfaces as biologically relevant over others of similar size and performance with respect to conventional metrics, in a fashion also supported by sequence conservation and the positions of post-translational modification sites.

Structural bioinformatics provides a wide variety of orthogonal analyses that can help to predict the location of functional sites in a given structure (Rigden, 2017[Rigden, D. J. (2017). Editor. From Protein Structure to Function with Bioinformatics, 2nd ed. Heidelberg: Springer Nature.]). The density of the covariance signal across the structure, coded for example as an EC score reflecting the number and the strength of the contact predictions associated with each residue, has the potential to be a useful addition to the list (Fig. 1[link]). For example, in work using contact predictions to fold transmembrane proteins, residues with high scores were found at known substrate-binding sites (Hopf et al., 2012[Hopf, T. A., Colwell, L. J., Sheridan, R., Rost, B., Sander, C. & Marks, D. S. (2012). Cell, 149, 1607-1621.]). Similarly, in models of families that have not yet been structurally characterized experimentally, high-scoring residues were found at predicted catalytic or cofactor-binding sites and lining probable pores (Hopf et al., 2012[Hopf, T. A., Colwell, L. J., Sheridan, R., Rost, B., Sander, C. & Marks, D. S. (2012). Cell, 149, 1607-1621.]). More recent work takes a network approach to infer functional sites from contact predictions (Parente et al., 2015[Parente, D. J., Ray, J. C. & Swint-Kruse, L. (2015). Proteins, 83, 2293-2306.]). One example is the identification of functional residues, both catalytic and interfacial, in the aldolase family using eigenvector centrality, which describes residue hotspots in contact maps (Parente et al., 2015[Parente, D. J., Ray, J. C. & Swint-Kruse, L. (2015). Proteins, 83, 2293-2306.]). A very recent paper exploits a known structure to aid the interpretation of contact-prediction information, enabling functional site prediction (Jeong & Kim, 2016[Jeong, C. & Kim, D. (2016). BMC Bioinformatics, 17, 99.]). Another study uses contact predictions to identify druggable protein–protein interfaces through a combination of fragment docking and EC methods (Bai et al., 2016[Bai, F., Morcos, F., Cheng, R. R., Jiang, H. & Onuchic, J. N. (2016). Proc. Natl. Acad. Sci. USA, 113, E8051-E8058.]), and a further recent paper demonstrates the value of the covariance signal for inferring the detrimental or benign nature of single amino-acid polymorphisms (Hopf et al., 2016[Hopf, T. A., Ingraham, J. I., Poelwijk, F. J., Scharfe, C. P. I., Springer, M., Sander, C. & Marks, D. S. (2016). Nature Biotechnol. 35, 128-135.]). Finally, it is interesting to view longstanding conventional sequence conservation (Ashkenazy et al., 2016[Ashkenazy, H., Abadi, S., Martz, E., Chay, O., Mayrose, I., Pupko, T. & Ben-Tal, N. (2016). Nucleic Acids Res. 44, W344-W350.]) and the new pairwise covariance methods discussed here as the simplest cases of coevolutionary analysis, an analysis which can readily be extended to determine larger functionally relevant covarying groups (Grigolon et al., 2016[Grigolon, S., Franz, S. & Marsili, M. (2016). Mol. Biosyst. 12, 2147-2158.]).

7. Rosetta as a unifying structural bioinformatics framework

The utility of contact predictions can be compared with that of experimental methods for deriving distance restraints, such as chemical cross-linking (Belsom et al., 2016[Belsom, A., Schneider, M., Brock, O. & Rappsilber, J. (2016). Trends Biochem. Sci. 41, 564-567.]), spin-labelling combined with electron paramagnetic resonance (Fischer et al., 2016[Fischer, A. W., Bordignon, E., Bleicken, S., García-Sáez, A. J., Jeschke, G. & Meiler, J. (2016). J. Struct. Biol. 195, 62-71.]) or fluorescence (Göbl et al., 2014[Göbl, C., Madl, T., Simon, B. & Sattler, M. (2014). Prog. Nucl. Magn. Reson. Spectrosc. 80, 26-63.]), which have played such a valuable role in the integrative structure determination of large complexes (Webb et al., 2011[Webb, B., Lasker, K., Schneidman-Duhovny, D., Tjioe, E., Phillips, J., Kim, S. J., Velázquez-Muriel, J., Russel, D. & Sali, A. (2011). Methods Mol. Biol. 781, 377-397.]). Recent blind analysis of the value of experimental cross-linking data to protein structure prediction (Belsom et al., 2016[Belsom, A., Schneider, M., Brock, O. & Rappsilber, J. (2016). Trends Biochem. Sci. 41, 564-567.]) has highlighted the limitations of uneven coverage and poor definition of β-sheets. Selective labelling to gain long-range distance information for NMR of large proteins also suffers from its own complications (Lange et al., 2012[Lange, O. F., Rossi, P., Sgourakis, N. G., Song, Y., Lee, H. W., Aramini, J. M., Ertekin, A., Xiao, R., Acton, T. B., Montelione, G. T. & Baker, D. (2012). Proc. Natl Acad. Sci. USA, 109, 10873-10878.]) when the methyl-containing probe residues are unevenly distributed. Although contact prediction has its own limitations, it is well placed to occupy a complementary role to experimental distance restraints (Tang et al., 2015[Tang, Y., Huang, Y. J., Hopf, T. A., Sander, C., Marks, D. S. & Montelione, G. T. (2015). Nat. Methods, 12, 751-754.]). Effectively exploiting contact predictions alongside sources of experimental restraints for structure prediction requires an extensible and unifying structural bioinformatics approach. Here, it is worth considering Rosetta in more detail as a software package that is well suited to this rationale and has a considerable track record in this area.

Macromolecular structure-prediction approaches such as Rosetta are based on the hypothesis that the native states of proteins are at global free-energy minima, and carry out a large-scale search of conformational space for the lowest energy structure. The success of such approaches depends on two factors: the accuracy of the energy function and the ability of the search to converge on the lowest energy state. Because of the very large number of degrees of freedom in biomolecular systems, the second challenge, the search problem, is the primary bottleneck to accurate prediction. For all but the smallest proteins (less than 80 amino acids), the conformational space is too large for accurate ab initio structure prediction. However, when experimental information is available it can be used to focus the search for lowest energy states on the relevant part of the conformational search and can enable the determination of the structures of quite complex proteins and biomolecular complexes. For example, the incorporation of even quite limited electron-density data (DiMaio, Terwilliger et al., 2011[DiMaio, F., Terwilliger, T. C., Read, R. J., Wlodawer, A., Oberdorfer, G., Wagner, U., Valkov, E., Alon, A., Fass, D., Axelrod, H. L., Das, D., Vorobiev, S. M., Iwaï, H., Pokkuluri, P. R. & Baker, D. (2011). Nature (London), 473, 540-543.]; DiMaio et al., 2013[DiMaio, F., Echols, N., Headd, J. J., Terwilliger, T. C., Adams, P. D. & Baker, D. (2013). Nat. Methods, 10, 1102-1104.]), NMR data (Raman et al., 2010[Raman, S., Lange, O. F., Rossi, P., Tyka, M., Wang, X., Aramini, J., Liu, G., Ramelot, T. A., Eletsky, A., Szyperski, T., Kennedy, M. A., Prestegard, J., Montelione, G. T. & Baker, D. (2010). Science, 327, 1014-1018.]; van der Schot & Bonvin, 2015[Schot, G. van der & Bonvin, A. M. J. J. (2015). J. Biomol. NMR, 62, 497-502.]) or cryo-EM data (DiMaio et al., 2015[DiMaio, F., Song, Y., Li, X., Brunner, M. J., Xu, C., Conticello, V., Egelman, E., Marlovits, T. C., Cheng, Y. & Baker, D. (2015). Nat. Methods, 12, 361-365.]; Wang et al., 2015[Wang, R. Y., Kudryashev, M., Li, X., Egelman, E. H., Basler, M., Cheng, Y., Baker, D. & DiMaio, F. (2015). Nat. Methods, 12, 335-338.]) into Rosetta can allow the generation of very accurate models. In contrast to conventional structure-prediction methods, the experimental data do not fully determine the structure – instead they guide the search process – and hence fewer data are required. Co-evolution data are treated within Rosetta just as experimental data are, and the power of co-evolution restraints to guide the search for the lowest energy structures has been illustrated in multiple quite accurate blind predictions (Ovchinnikov, Kim et al., 2015[Ovchinnikov, S., Kim, D. E., Wang, R. Y., Liu, Y., DiMaio, F. & Baker, D. (2015). Proteins, 84, Suppl. 1, 67-75.]; Safarian et al., 2016[Safarian, S., Rajendran, C., Müller, H., Preu, J., Langer, J. D., Ovchinnikov, S., Hirose, T., Kusumoto, T., Sakamoto, J. & Michel, H. (2016). Science, 352, 583-586.]). Within this framework, the integration of co-evolution data with cryo-EM, X-ray or NMR data is straightforward: all are read into Rosetta and used to guide the conformational search. The issue of how to weight the different sources of information (co-evolution data versus experimental data) guiding the search can be resolved by experimenting with different weightings and choosing that which results in models with the lowest energy.

8. Conclusion

As we have shown, predicted contacts deriving from evolutionary covariance already offer exciting possibilities to the experimental structural biologist as much as to the bioinformatician. The area remains highly active and new approaches (see, for example, Yang et al., 2016[Yang, J., Jin, Q.-Y., Zhang, B. & Shen, H.-B. (2016). Bioinformatics, 32, 2435-2443.]) can confidently be expected to continue to improve performance in the near future. These include approaches where additional information can be exploited to improve the precision of contact predictions (Zhang et al., 2016[Zhang, H., Huang, Q., Bei, Z., Wei, Y. & Floudas, C. A. (2016). Proteins, 84, 332-348.]; Hopf et al., 2012[Hopf, T. A., Colwell, L. J., Sheridan, R., Rost, B., Sander, C. & Marks, D. S. (2012). Cell, 149, 1607-1621.]; Wang & Barth, 2015[Wang, Y. & Barth, P. (2015). Nat. Commun. 6, 7196.]; Hönigschmid & Frishman, 2016[Hönigschmid, P. & Frishman, D. (2016). J. Struct. Biol. 194, 112-123.]). Other recent progress has been made in the prediction of interacting pairs of proteins, from among paralogous families, without the help of genome-context information, developments which should increase the reach of intermolecular contact prediction still further (Gueudré et al., 2016[Gueudré, T., Baldassi, C., Zamparo, M., Weigt, M. & Pagnani, A. (2016). Proc. Natl Acad. Sci. USA, 113, 12186-12191.]; Bitbol et al., 2016[Bitbol, A. F., Dwyer, R. S., Colwell, L. J. & Wingreen, N. S. (2016). Proc. Natl Acad. Sci. USA, 113, 12180-12185.]).

Funding information

Funding for this research was provided by: Biotechnology and Biological Sciences Research Council (award No. BB/L008696/1).

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IUCrJ
Volume 4| Part 3| May 2017| Pages 291-300
ISSN: 2052-2525