The temperature-dependent conformational ensemble of SARS-CoV-2 main protease (Mpro)
aDiamond Light Source, Harwell Science and Innovation Campus, Didcot, OX11 0DE, England, United Kingdom, bStructural Biology Initiative, CUNY Advanced Science Research Center, New York, NY 10031, USA, cBiology Department, Brookhaven National Laboratory, Upton, NY 11973, USA, dCenter for BioMolecular Structure, NSLS-II, Brookhaven National Laboratory, Upton, NY 11973, USA, eNational Virtual Biotechnology Laboratory (NVBL), US Department of Energy, Washington, DC, USA, fDepartment of Chemistry and Biochemistry, City College of New York, New York, NY 10031, USA, and gPhD Programs in Biochemistry, Biology, and Chemistry, The Graduate Center–City University of New York, New York, NY 10016, USA
*Correspondence e-mail: email@example.com
The COVID-19 pandemic, instigated by the SARS-CoV-2 coronavirus, continues to plague the globe. The SARS-CoV-2 main protease, or Mpro, is a promising target for the development of novel antiviral therapeutics. Previous X-ray crystal structures of Mpro were obtained at cryogenic temperature or room temperature only. Here we report a series of high-resolution crystal structures of unliganded Mpro across multiple temperatures from cryogenic to physiological, and another at high humidity. We interrogate these data sets with parsimonious multiconformer models, multi-copy ensemble models, and isomorphous difference density maps. Our analysis reveals a perturbation-dependent conformational landscape for Mpro, including a mobile zinc ion interleaved between the catalytic dyad, mercurial conformational heterogeneity at various sites including a key substrate-binding loop, and a far-reaching intramolecular network bridging the active site and dimer interface. Our results may inspire new strategies for antiviral drug development to aid preparation for future coronavirus pandemics.
Keywords: protein structure; protein flexibility; X-ray crystallography; allostery; SARS-CoV-2 main protease; temperature-dependent; COVID-19.
COVID-19 is a global pandemic disease caused by severe acute respiratory syndrome coronavirus 2. SARS-CoV-2 is a highly infectious airborne respiratory virus which has caused over 580 million infections and over 6.4 million deaths worldwide as of August 2022. Over the past two years, several approaches to prevent and treat COVID-19 have been successfully developed, including new vaccines, monoclonal antibody treatments (Baum et al., 2020), and repurposed existing therapeutics (Beigel et al., 2020; Boras et al., 2021). More recently, small-molecule antiviral drugs have fortunately been approved for clinical use to combat COVID-19. Notwithstanding these successes, additional chemical tools to modulate the function of coronavirus proteins would aid in the preparation for future coronavirus pandemics.
A promising target for potential new antiviral drugs against SARS-CoV-2 is a chymotrypsin-like protease known by several names: non-structural protein 5, nsp5, 3C-like protease, 3CLpro, main protease, or Mpro. Mpro is part of a polyprotein encoded by the viral RNA genome. After being excized from the polyprotein by its own proteolytic activity, Mpro cleaves at no fewer than 11 sites in the polyprotein to generate individual functional proteins (V'kovski et al., 2021) that help the virus replicate. Due to its importance to the SARS-CoV-2 life cycle, Mpro has been identified as a key target for COVID-19 drug design.
Drug design efforts focused on Mpro have been aided by insights from structural biology. The first SARS-CoV-2 Mpro crystal structures were released in the Protein Data Bank (PDB; Berman et al., 2000) early in the pandemic, within the first week of February 2020 (Jin et al., 2020). These structures revealed that, like SARS-CoV Mpro before it, SARS-CoV-2 Mpro is composed of two β-barrel domains known as domain I and domain II, and an α-helical bundle known as domain III [Fig. 1(a)]. The active-site cavity is located on the surface, with the His41-Cys145 catalytic dyad positioned between domain I and domain II. Domain III is involved in regulating dimerization (Zhang et al., 2020), which is critical for coronavirus Mpro catalytic activity (Fan et al., 2004; Goyal & Goyal, 2020). Since the initial structures of SARS-CoV-2 Mpro, X-ray crystallography has been used to identify promising ligand-binding sites and alternate structural states of the protein, resulting in a total of over 310 available structures. These efforts included cocrystallography with an eye toward drug repurposing (Vuong et al., 2020; Günther et al., 2021), as well as crystallographic screens of noncovalent and covalent small-molecule fragments to establish new toeholds for ab initio drug design (Douangamath et al., 2020), which were then leveraged via a crowdsourced process to design novel inhibitors (Chodera et al., 2020).
As with much modern protein crystallography, the above experiments were performed at cryogenic temperatures, which can bias protein conformational ensembles (Fraser et al., 2011; Keedy et al., 2014). To bypass this limitation, a room-temperature crystal structure of unliganded Mpro was reported (Kneller, Phillips, O'Neill, Jedrzejczak et al., 2020) (PDB entry 6wqf), although its resolution was only moderate (2.3 Å) and the conclusions of the article about the effects of cryogenic versus room temperature were later questioned (Jaskolski et al. 2021). Subsequent work built on this foundation of room-temperature crystallography to dissect Mpro function (Kneller, Phillips, Kovalevsky et al., 2020; Kneller, Phillips, O'Neill, Tan et al., 2020). However, no studies to date have reported crystal structures of Mpro across a wide range of temperatures. Previously, such a multitemperature crystallography strategy was instrumental for revealing novel aspects of correlated active-site conformational heterogeneity in a dynamic proline isomerase (Keedy et al., 2015) and of long-range allosteric signaling in a therapeutic target tyrosine phosphatase (Keedy et al., 2018).
Here we report high-resolution crystal structures of SARS-CoV-2 Mpro at five temperatures: 100 (cryogenic), 240 (above the so-called glass transition or dynamical transition; Keedy et al., 2015), 277 (`room temperature' in many crystallography studies), 298 (ambient), and 310 K (physiological). We also report a structure at ambient temperature but high relative humidity (99.5% RH) to gauge the relative effects of temperature versus humidity on Mpro. To our knowledge, this study represents the first experimentally based structural analysis for any SARS-CoV-2 protein at variable temperature and/or humidity. We used careful data collection with a helical strategy to minimize radiation damage, thereby isolating the effects of temperature and humidity on Mpro. For all data sets we have constructed parsimonious multiconformer models, as well as multi-copy crystallographic ensemble models, which provide complementary insights into protein structural flexibility as a function of temperature and humidity. Together, our data reveal a network of subtle but provocative temperature-dependent conformational heterogeneity spanning several functionally relevant sites throughout Mpro, which may help motivate an allosteric strategy for antiviral drug design to aid the preparation for future coronavirus pandemics.
2.1. Multitemperature crystallographic data collection and modeling
Data were obtained from single Mpro crystals using helical data collection, to maximize diffraction intensity while minimizing radiation damage (Fig. S1 in the supporting information). To probe the conformational landscape of Mpro, we obtained high-resolution structures at five different temperatures: 100, 240, 277, 298 (ambient; see Methods section), and 310 K. Our data sets thus span a broad temperature range: cryogenic, just above the glass transition or dynamic transition (Keedy et al., 2015), the range often noted as room temperature (roughly 293–300 K), and approximately physiological temperature. We also collected another 298 K data set with high relative humidity (99.5% RH).
For all but the 277 K data set (2.19 Å), the resolution was 2 Å or better [based on an outer shell CC1/2 cut-off of >∼0.3 (Karplus & Diederichs, 2012); see Methods section and Table 1]. The highest resolution was for the 240 K data set (1.53 Å). Even at the higher temperatures, we saw little to no evidence of radiation damage (Fig. S1). After data reduction, we created a multiconformer model for each temperature, which includes a single conformer for most portions of the structure, but alternate conformations where appropriate (Riley et al., 2021). See Methods section for more details on data collection and modeling, and Table 1 for the overall diffraction data and refinement statistics.
2.2. Overall structure as a function of temperature
The global structure of Mpro in our crystals remains similar across the temperatures [Figs. 1(d) and S2], as expected. Indeed, the maximum Cα r.m.s. deviation (RMSD) between any pair of structures in the ambient-humidity multitemperature series is only 0.64 Å, and the maximum all-atom RMSD is only 0.95 Å. However, there is a clear clustering between lower-temperature (100 and 240 K) and higher-temperature (277, 298, and 310 K) structures, based on either Cα RMSD [Fig. 1(d)] or all-atom RMSD (Fig. S2). These observations indicate that aspects of the Mpro conformational landscape change in response to temperature.
Humidity also appears to have some effect on Mpro structure, as shown by the fact that the overall largest pairwise Cα RMSD [0.65 Å, Fig. 1(d)] and all-atom RMSD (1.02 Å, Fig. S2) involve the 298 K high-humidity (99.5% RH) structure. However, the corresponding RMSD values for the 298 K ambient-humidity (36.7% RH) structure are only slightly smaller (<0.1 Å difference). These RMSD differences between high versus low humidity are minor compared to the differences between the high- versus low-temperature clusters mentioned above. Thus, temperature affects the Mpro structure noticeably more than does humidity. This result contrasts with previous studies of lysozyme in which similar structural alterations of the protein were achieved by either small changes in humidity or large changes in temperature (Atakisi et al., 2018); this discrepancy may result from different protein–solvent arrangements in the lysozyme versus Mpro crystal lattices.
2.3. Temperature dependence of local alternate conformations
To provide more detailed insights into the observed global temperature dependence, we sought to identify alternate conformations at the local scale that were stabilized or modulated by the temperature shifts in our experiments. We specifically focused our attention on areas of the protein that are of interest for drug design and/or biological function: the active site, nearby loops associated with substrate binding, and the dimer interface.
First, the Mpro active-site structure remains mostly consistent across our temperature series (Fig. S4). The catalytic amino acids are in very similar conformations across the temperatures. Additionally, a key active-site water molecule (known as H2Ocat), which hydrogen bonds to His41 of the catalytic dyad and the side chains of His164 and Asp187 (both in the active site), remains in the same position across our structures (Fig. S4). It has been suggested (Kneller, Phillips, O'Neill, Jedrzejczak et al., 2020) that this water may play the role of a third catalytic residue (in addition to the catalytic dyad of His41 and Cys145). As noted previously (Kneller, Phillips, O'Neill, Jedrzejczak et al., 2020), H2Ocat is not modeled in some cryogenic structures – but it is modeled in 87% (272/311) of the publicly available structures of SARS-CoV-2 Mpro as of October 11, 2021 (the vast majority of which are cryogenic), and perhaps should have been modeled in others (Jaskolski et al., 2021).
As with the active-site amino acids and H2Ocat, a dimethyl sulfoxide (DMSO) molecule from the crystallization solution is ordered nearby in each structure in the multitemperature series (Fig. S4, left of each panel). Interestingly, however, this DMSO molecule is displaced by a water molecule in the high-humidity data set (298 K, 99.5% RH) [Fig. S4(f)], suggesting that the solvation distribution of the Mpro active site is malleable. Similarly, another DMSO molecule in a distal region of the protein is ordered throughout the multitemperature series, but two waters and a new side-chain rotamer for Arg298 displace it in the high-humidity data set.
In addition to these solvent molecules, we observe an unanticipated feature in the active site in all of our data sets: an electron-density peak between the side chains of Cys145 and His41, which form the catalytic dyad (Figs. S4 and S5). We initially modeled a water molecule at this position, as in two previous apo structures: 7k3t Version 1.0 (the highest-resolution Mpro structure available; apo state; Andi et al., 2022) and 7jfq (`de-oxidized C145', no publication). However, the interatomic distances between a putative water oxygen and the nearest atoms in Cys145 and His41 are relatively short, leading to steric clashes (Fig. S6), so we explored other possible explanations for this peak. Zn2+ binds tightly to Mpro with 300 nM affinity (Panchariya et al. 2021), and when soaked into Mpro crystals in previous studies (PDB entry 7dk1 and 7b83) [Fig. S9(b)], it was well ordered at our site of interest amidst the catalytic dyad. By contrast, we did not intentionally include Zn2+ at any stage, and Zn2+ is absent from almost all structures of Mpro in the PDB aside from a select few structures for which it was intentionally included, arguing against its presence in our structures. Nonetheless, we considered the possibility that low levels of cellular Zn2+ serendipitously bound to our sample of Mpro during expression and purification, and remained present during crystallization and X-ray data collection.
We therefore performed X-ray fluorescence (XRF) experiments on the original crystallization drops. The XRF results are consistent with the presence of Zn2+ (Fig. S7), but not other candidate metals, such as Ni2+. Although we did not observe significant anomalous density at this site, this may be due to the fact that our diffraction data had been collected at a wavelength that does not perfectly align with the Zn2+ anomalous edge (see Methods section). We performed XRF many months after initial diffraction data collection, at which time the crystals no longer diffracted, so we could not collect new anomalous diffraction data at the Zn2+ anomalous edge for the crystals constituting our multitemperature series. Preparing a new batch of crystals to do so might have led to differences in the metal content, particularly given the absence of Zn2+ from the vast majority of structures of Mpro. Instead, we re-examined the original diffraction data for the apo structure 7k3t, which – critically – were collected from the same batch of crystals as our multitemperature data sets reported here, and are of significantly higher resolution (1.20 Å). We observe a strong anomalous peak at the position in question for 7k3t, despite also having used an off-edge wavelength for Zn2+ (Fig. 2). A new 7k3t Version 2.0 model is therefore deposited in the PDB with anomalous data included and Zn2+ instead of H2O modeled, and is described elsewhere (Andi et al., 2022).
In response to these observations, we have modeled Zn2+ at partial occupancy (0.20–0.31) in each of our multitemperature structures (Fig. S4). These refined crystallographic occupancies are similar and imply a small energy difference (Davis et al., 2006) of <0.5 kT. The resulting structures have excellent Zn2+-binding geometry and the resulting maps are free of large difference peaks. Interestingly, in our temperature series, the position of Zn2+ varies across temperatures by nearly 1 Å, which is in excess of the estimated coordinate error ranging from ±0.08 to ±0.32 Å for our structures (calculated using the Diffraction Precision Index online server; Kumar et al., 2015), along an approximately linear swath (Fig. S8). This result is in line with the presence of alternate conformations displaced by over 0.7 Å along a similar vector for the Zn2+ in the higher-resolution 7k3t Version 2.0 [Fig. S9(a)], and also coincides with a swath of O atoms from a series of covalent ligands [Fig. S8(c)].
Beyond the active site, we turned our attention to the nearby P5 binding pocket, specifically the loop composed of residues 192–198 (Fig. 3). Previously, the first report of a room-temperature structure of Mpro, which was in the apo form (PDB entry 6wqf) (Kneller, Phillips, O'Neill, Jedrzejczak et al., 2020), noted that this loop adopted a different conformation than in a prior 100 K apo structure (PDB entry 6y2e) (Zhang et al., 2020), including rotated peptide orientations for Ala194–Gly195 and Asp197–Thr198. However, all of the structures in our multitemperature series, including at lower temperatures (100 and 240 K), have a single backbone conformation in this region that matches that of 6wqf [Fig. 3(b)]. In addition, other apo cryogenic structures, including one at high (1.2 Å) resolution (PDB entry 7k3t), also match the 6wqf backbone conformation. All of these structures (6wqf, 6y2e, 7k3t, and our multitemperature series) derive from the same crystal form (Table 1). Thus, it appears that the different loop conformation adopted in 6y2e is not driven by temperature (Kneller, Phillips, O'Neill, Jedrzejczak et al., 2020), nor by ligand binding or crystal-lattice effects, but rather by some other aspect of the crystallization details or sample-handling conditions – including, perhaps, idiosyncratic effects of crystal cryocooling (Halle, 2004; Keedy et al., 2014). Our conclusion here is also supported by a recent retrospective analysis of existing structures (Jaskolski et al., 2021).
2.4. Crystallographic ensemble models reveal distinct backbone conformational heterogeneity
We next aimed to complement this analysis of our manually built multiconformer models with a more automated and explicitly unbiased approach to modeling flexibility that can handle larger-scale backbone flexibility such as loop motions. Therefore, we turned to Phenix ensemble refinement, which uses molecular dynamics simulations with time-averaged restraints to crystallographic data (Burnley et al., 2012). Phenix ensemble models have been used fruitfully for many applications (Woldeyes et al., 2014), including exploring the effects of temperature on protein crystals (Keedy et al., 2014), assessing the conformational plasticity of peptide–MHC interactions (Fodor et al., 2018), and rational protein design (Broom et al., 2020). After a scan of parameter space (see Methods section), we created one ensemble model per temperature, each of which contains 28 to 75 constituent models (Table 2). Compared to the multiconformer models, the ensemble models fit the experimental data equally well or better based on Rfree, albeit with slightly wider Rfree–Rwork gaps (Table 2 versus Table 1).
Using these ensemble models, we re-examined the P5 binding pocket loop mentioned above. The 100, 240, and 310 K ensemble models are similar to the previous `room-temperature' structure 6wqf, with mostly the same peptide orientation for Ala194–Gly195 and Asp197–Thr198 [Fig. 3(c)]. By contrast, the 277, 298, and 298 K (99.5% RH) ensemble models mostly match the flipped Ala194–Gly195 peptide orientation from the previous cryogenic structure 6y2e, and sample a swath of conformations for Asp197–Thr198 that span 6wqf and 6y2e [Fig. 3(d)]. This distinction between peptide conformations that match 6wqf versus 6y2e is not simply a by-product of resolution, as 298 K is higher resolution than 310 K. More broadly, our ensembles reveal that the backbone of residues 192–195 occupies a distinct clustered conformation at the lower temperatures of 100–240 K, while sampling a comprehensive swath of positions at the higher temperatures of 277–310 K [Fig. 3(e)]. Taken together, these results suggest that this region of Mpro has a complex relationship between temperature and conformational heterogeneity.
Beyond just the P5 loop, we also examined other regions with elevated and/or temperature-dependent ensemble Cα r.m.s. fluctuation (RMSF) [Fig. 4(a)] that were not previously noted as being temperature dependent. These regions segregate into different categories with distinct temperature dependence.
First, some regions display a generally positive correlation between backbone structural variability and diffraction experiment temperature. For example, in residues 68–76 [Fig. 4(c)], conformational diversity is restricted to the β-hairpin at 100 and 240 K, but appears to spread further down the β-strands at higher temperatures. In another case, residues 92–97 [Fig. 4(d)] and 218–227 [Fig. 4(e)] are highly ordered at 100 and 240 K, but mobile at warmer temperatures. Interestingly, although these two regions (92–97 and 218–227) are isolated from each other in the monomer and the biologically relevant dimer, together they form a contiguous patch with 68–76 in the crystal lattice. Finally, we observe one region with an atypical relation between backbone variability and temperature: the short 310 helix at residues 46–51 [Fig. 4(b)]. This region abuts the P5 substrate-binding loop composed of residues 192–198 with its complex temperature dependence (Fig. 3); together these two regions form one side of the active-site pocket [Fig. 1(b)].
2.5. A network of coupled conformational heterogeneity bridges the active site, substrate pocket, interdomain interface, and dimer interface
To complement the model-centric approaches above, we also looked for temperature-dependent conformational effects using an approach that is more directly data-driven: isomorphous Fo − Fo difference electron-density maps. We computed Fo − Fo difference maps for each temperature versus 100 K, and looked for patterns in terms of spatial colocalization of difference peaks. The global results confirm that the protein structure remains similar overall, with a smattering of difference peaks throughout the monomer asymmetric unit (Fig. S10). However, within those difference peaks lies a provocative stretch of difference features spanning the dimer interface, the interface between domain I and domain II of the monomer, and the edge of the P5 substrate-binding pocket (Fig. 5). These difference features may be somewhat resolution dependent, as they are least pronounced for 277 K (2.19 Å) and most pronounced for 240 K (1.53 Å), but their distribution across Mpro is qualitatively similar across temperatures. These features are not an artifact of diffraction anisotropy (Fig. S11).
A closer examination of the models in the vicinity of these difference features reveals what appears to be a series of correlated conformational motions keyed to temperature change. For example, Fo − Fo density shows that Glu290 shifts from a single side-chain rotamer at 100 K to two alternate rotamers with partial occupancies at 240 K [Fig. 5(a)]; this second rotamer seen at 240 K then remains as a single full-occupancy conformation for all higher temperatures. Spatially adjacent to Glu290, Cys128 gradually shifts from two alternate rotamers at 100 K toward a single rotamer at higher temperatures; at the intermediate temperature of 240 K, the alternate rotamer still exists but has lower occupancy than at 100 K (Fig. S12), which is consistent with the presence of a positive 240–100 K Fo − Fo peak [Fig. 5(a)]. Both Glu290 and Cys128 interact with a symmetry-related Arg4 across the biological dimer interface [Fig. 5(b)]. Interestingly, two small-molecule fragments from recent crystallographic screens (Douangamath et al., 2020; Noske et al., 2021) bind at this area of the dimer interface [Figs. 5(a) and 5(b)]. Moreover, ordered polyethylene glycol (PEG) molecules from several previous structures [PDB entries 7kvr, 7kvl, 7kfi, and 7lfe; Figs. 5(a) and 5(b)] illustrate the potential for future ligand design efforts to `grow' from one of these initial fragment hits (5rf0) toward the mobile Glu290 and Cys128. This observation reinforces the idea that molecules from crystallization solutions, such as glycols, can reveal useful features like cryptic binding pockets (Bansia et al., 2021).
Glu290 is connected to another interesting residue, Asp197, via a hydrogen-bond network with only one intervening side chain (Arg131). Within this vicinity, an interacting water molecule is liberated, and an adjacent residue, Thr198, shifts from two alternate side-chain rotamers to just one [Fig. 5(c)]. The Thr198 motion is linked to a conformational re-ordering for the nearby Glu240 side chain and Pro241 backbone, thus establishing a possible means for allosteric communication across the interdomain interface. In the opposite direction from Asp197, other adjacent residues experience changes in ordering per Fo − Fo peaks; these residues together form the 192–198 loop of the functionally important and mobile P5 pocket (Fig. 3) leading toward the active site.
Overall, these observations describe a series of conformational motions that bridge the dimer interface, interdomain interface, substrate-binding pocket, and active site (Fig. 5, center, boxes and oval). In this work, temperature is the perturbation/effector – but our results raise the enticing possibility that future small molecules could be used to allosterically perturb this network, thereby modulating enzyme dimerization and/or catalysis.
Our crystal structures of unliganded SARS-CoV-2 Mpro at variable temperature and humidity paint a picture of a complex protein conformational landscape. The structure of Mpro does not change linearly with temperature; rather, there is a global transition between roughly <240 and >277 K [Figs. 1(d) and S2]. This 240–277 K transition regime for Mpro does not coincide with the 180–220 K glass transition or dynamical transition threshold seen previously for other systems such as CypA (Keedy et al., 2015), suggesting protein-to-protein variability. More locally in Mpro, as temperature increases, different regions experience distinct types of changes to conformational heterogeneity (Fig. 4), in line with previous multitemperature studies of other proteins (Keedy et al., 2014). These effects are not limited to surface-exposed side chains as one might naïvely expect, but rather encompass motions of buried side chains (Fig. 5) and many backbone regions (Figs. 3 and 4). Our results here for Mpro, as well as a large body of previous literature for other systems, refute the assertion that X-ray crystallography under `unusual experimental conditions' like variable temperature is not useful for understanding proteins (Jaskolski et al., 2021). By contrast, our work is in line with computational analyses of B factors suggesting that different alternate conformations for Mpro (and other systems) can be accessed by varying temperatures and/or the crystal lattice (Pearce & Gros, 2021).
Our structures contain serendipitously bound low-occupancy Zn2+ at the site of chemistry, presumably from the bacterial cells used for protein expression. It should be noted that the presence of Zn2+ in the active site of our structures may bias the temperature response and complicate efforts to exploit these structures for structure-based drug design. It is unclear why this Zn2+ is absent in most previous structures of Mpro. Notably, recent structures of an acyl–enzyme intermediate structure (PDB entry 7khp) and a C145A mutant product-bound structure (PDB entry 7joy) of Mpro include a nearby water, ∼1.5 Å away but aligned with our approximately collinear multitemperature Zn2+ swath, which the authors suggested may play a role as a deacylating nucleophile (Lee et al., 2020). Questions about the energetic landscape of this catalytic dyad region and its relation to function could be explored in parallel with other experiments, such as variable pH to probe Cys145 oxidation and reactivity (Kneller, Phillips, O'Neill, Tan et al., 2020), and neutron crystallography to reveal a zwitterionic state of the catalytic dyad (Kneller, Phillips, Weiss et al., 2020), although questions remain about the interpretation of such data (Jaskolski et al., 2021).
High relative humidity during data collection does not dramatically affect our structures [Figs. 1(d) and S2]. However, it does alter the solvation shell in the active site (Fig. S4, bottom left) and elsewhere. Displaceable waters could potentially be exploited to design high-affinity small-molecule inhibitors, particularly when guided by water thermodynamics maps from simulations, as are available for Mpro and other SARS-CoV-2 proteins (Olson et al., 2020). More broadly, this result hints at the utility of humidity as an experimental variable in crystallography (Kiefersauer et al., 2000; Sanchez-Weatherby et al., 2009) for exploring solvent slaving to solvent energetics in ligand binding (Darby et al., 2019), protein dynamics (Lewandowski et al., 2015), and other functionally relevant phenomena.
Phenix ensemble models (Burnley et al., 2012) refined from our X-ray data sets helped us to illuminate temperature-dependent differences in conformational heterogeneity in certain areas of Mpro (Figs. 3 and 4) that were concealed by more traditional model types (Babcock et al., 2018). Despite its utility in this and other work, there is significant potential for improvement of the ensemble refinement methodology through, for example, integration of more sophisticated molecular mechanics force fields like Amber (Moriarty et al., 2020) into the molecular dynamics component (Burnley et al., 2012) to improve ensemble model geometry, or more sophisticated treatments of translation–libration–screw (TLS) groups to isolate interesting local conformational heterogeneity (Ploscariu et al., 2021). Although it was also beyond the scope of this study, ensemble models may reveal alternate conformational substates that are important for the catalytic cycle, which could be fruitfully targeted by small molecules for antiviral drug design.
Finally, our results emphasize the allure of allosteric inhibition of Mpro as an alternative therapeutic strategy. Allosteric inhibitors hold the potential to target unutilized sites, though they can face mutational escape by the protein target (Lu et al., 2020). Our structures illustrate apparently coupled conformational motions that bridge the active site, substrate-binding pocket, interdomain interface, and parts of the broad dimer interface (Figs. 4 and 5). This is particularly noteworthy since Mpro must dimerize to become an active enzyme (Fan et al., 2004; Goyal & Goyal, 2020). Interdomain flexing has also been observed, even in crystals (Jaskolski et al., 2021). The intramolecular network we describe includes several sites that are distal from the active site, one of which is highlighted by Glu240 difference density [Fig. 5(c)] corresponding to a temperature-dependent rotamer flip, with this site having already been characterized as ligandable by recent crystallographic screens of pre-existing drug molecules (Günther et al., 2021) and small-molecule fragments (Douangamath et al., 2020) (Fig. 5). Some new Mpro ligands have been shown by mass spectrometry to disrupt the Mpro dimer and allosterically inhibit catalysis, albeit weakly thus far (El-Baba et al., 2020), illustrating the potential of an allosteric strategy. As a complementary structure-based approach to current experiments on the dimeric crystal form of Mpro, future experiments could exploit mutations of the dimer interface to stabilize an inactive monomer, thus capturing a new structural target for crystallographic and solution screening for allosteric inhibitors that block dimerization. Ultimately, the present study offers insights into fundamental aspects of protein structural biophysics, and may also help pave the way for new efforts toward allosteric modulation of Mpro as a strategy for coronavirus drug design.
4.1. Cloning, expression, and purification
Full details of the cloning, expression, and purification are reported elsewhere (Andi et al., 2022). Briefly, the codon-optimized synthetic gene of full-length Mpro from SARS-CoV-2 was cloned into the pET29b vector. The cloned Mpro with C-terminal 6x histidine tag was expressed in E. coli using an auto-induction procedure (Studier, 2005). Cells were harvested, lysed using bacterial protein extraction agents (B-PER, ThermoFisher Scientific) in the presence of lysozyme, and purified with nickel-affinity chromatography followed by size-exclusion chromatography. The histidine tag was cleaved by human rhinovirus (HRV) 3C protease (AcroBIOSYSTEMS) and further purified by reverse nickel-affinity chromatography. The purified protein was then dialysed overnight at 4°C against 30 mM HEPES pH 7.4, 200 mM NaCl, 1 mM TCEP. Finally, the protein was concentrated to ∼7 mg ml−1 and either used for crystallization or stored at −80°C.
Plate-like crystals ranging from ∼100–400 µm along the longest axis (∼5–10 µm along the shortest axis) were grown via sitting-drop vapor diffusion. The crystals grew in `flower-like' clusters (Fig. S13). After mixing a 1:1 ratio of ∼7 mg ml−1 Mpro with a solution of 22% PEG 4000, 100 mM HEPES pH 7.0, 3–5% DMSO and incubating at a temperature of ∼298 K, crystals were seen after 2–6 d.
4.3. Crystal harvesting and X-ray data collection
Individual crystals were harvested using 10 µm MicroMesh loops (MiTeGen). For cryogenic temperature, crystals were cryocooled by the traditional practice of plunging into liquid nitrogen. For non-cryogenic temperatures at ambient humidity, crystals were coated with Paratone-N oil, then mounted on the goniometer for data collection. Data sets were also collected for crystals coated with Paratone-N oil and additionally enclosed in MicroRT capillaries (MiTeGen), but no differences were observed relative to Paratone-N oil only. For high humidity, crystals were not coated with Paratone-N oil, but were enclosed in MicroRT capillaries for the short transit to the goniometer, then removed once a humid air flow was established on the goniometer; this ensured the crystal was always maintained at high humidity after leaving the crystallization drop. Each crystal was equilibrated on the goniometer for 10–20 min, which is more than sufficient to reach stable conditions.
X-ray diffraction data were collected at the National Synchrotron Light Source II (NSLS-II) beamline 17-ID-2 (FMX) (Schneider et al., 2021) using an X-ray beam of energy 12.66 keV, corresponding to a wavelength of 0.9793 Å; a horizontal-bounce Si111 double-crystal monochromator; and an EIGER X 16M pixel array detector (Dectris). The temperature at the sample goniometer was controlled using a Cryostream 800 (Oxford Cryosystems). For the 298 K, 99.5% relative humidity (RH) data set, RH was controlled with an HC-LAB Humidity Controller (Arinax). Ambient temperature was measured to be ∼298 K and ambient humidity was measured to be 36.7%. A new crystal was used for each data set. Helical/vector data collection was used to traverse the length of each crystal, with a beam size of 10 × 10 µm. Using RADDOSE-3D (Bury et al., 2018), we estimated the average diffraction-weighted dose (ADWD) for our data sets to be 242 kGy for 100 K, 532 kGy for 240 K, 397 kGy for 277 K, 137 kGy for 298 K, 182 kGy for 298 K (99.5% RH), and 176 kGy for 310 K. All of these ADWD values are at or below the estimated room-temperature limit of about 400 kGy (Fischer, 2021) for our higher temperatures, although this limit is generally system dependent. The ADWD for 240 K is above the room-temperature limit, but such lower temperatures have higher dose tolerance. Additionally, there was no evidence of global radiation damage from Rd plots (Fig. S1), and local/specific radiation damage did not appreciably accrue during the course of each single-crystal data collection, as indicated by 2Fo − Fc electron-density maps around carboxyl groups (not shown).
For independent verification of the presence of metals in the crystal samples, we collected X-ray fluorescence spectra and performed edge scans. For fluorescence spectra data collection, the radiation emitted and scattered from the crystal was detected with an energy-dispersive Si drift detector (Ketek VIAMP KC), in a configuration to collect radiation scattered or emitted in a horizontal direction perpendicular to the incoming X-ray beam. For energy edge scans, the incoming photon energy was scanned across the absorption edge of the scatterer under investigation while detecting the integrated intensity of photons in a region of interest around the expected associated X-ray fluorescence emission line.
4.4. X-ray data reduction and modeling
The data reduction pipeline fast_dp (Winter & McAuley, 2011) was initially used for bulk data reduction during the beamtime, with selected data reprocessed using the xia2 DIALS (Winter et al., 2018) and xia2 3dii (XDS and XSCALE) pipelines (Kabsch, 2010), with xia2 3dii (XDS and XSCALE) also being used for the generation of Rd statistics (Diederichs, 2006) (Fig. S1). Resolution cut-offs were chosen based on CC1/2 being >∼0.3 in order to include data at lower signal levels that improve the model (Karplus & Diederichs, 2012), in combination with high overall and outer-shell completeness (Afonine et al., 2012; Arkhipova et al., 2017). Molecular replacement for each data set was performed via Phaser-MR from the Phenix software suite, using PDB entry 6yb7 as a search model. Phenix AutoBuild (Terwilliger et al., 2008) was used for initial model building and refinement, with subsequent iterative refinements performed using phenix.refine (Afonine et al., 2012) and Coot (Emsley & Cowtan, 2004). After a few initial rounds of refinements, H atoms were added using phenix.ready_set [Reduce (Word et al., 1999) and eLBOW (Moriarty et al., 2009)]. For refinement of each data set, X-ray/stereochemistry weight and X-ray/ADP weight were refined and optimized. Geometric and protein statistics of the final models were evaluated via MolProbity (Chen et al., 2010; Williams et al., 2018) and the JCSG–QC check server (https://smb.slac.stanford.edu/jcsg/QC/). Data collection and refinement statistics are shown in Table 1.
Crystallographic ensemble models were generated using phenix.ensemble_refinement (Burnley et al., 2012) in Version 1.18.2-3874 of Phenix. Alternate conformations were first removed from the multiconformer models, and H atoms were (re)added using phenix.ready_set. Next, a phenix.ensemble_refinement grid search was performed by repeating the simulation with four values of pTLS (1.0, 0.9, 0.8, 0.6) and three values of wxray_coupled_tbath_offset (10, 5, 2.5), and using a random_seed value of 2679941. τx was set automatically according to the high-resolution limit of the data set. From this grid, we present the analysis of the set of ensemble models that has the lowest mean Rfree: pTLS = 0.8 and wxray_coupled_tbath_offset = 10.0. Although we chose one ensemble model per data set, the trends we describe in this report were generally consistent across alternative ensemble models with different parameter choices. In line with this consistency, when comparing the alternative ensemble models we considered, the Rfree values for any particular crystal differed by at most 2.8% and Rwork values by at most 1.6%. Refinement statistics are shown in Table 2.
For Fo − Fo isomorphous difference map analysis, the phenix.fobs_minus_fobs_map executable in the Phenix software suite was used. This program performs internal scaling of the two data sets to each other. Each elevated temperature was compared to 100 K. The 100 K multiconformer model was used for phasing for each difference map. The effects of diffraction anisotropy were assessed using the STARANISO server (Tickle et al., 2018) to perform an anisotropic cut-off of merged intensity data for all temperatures. STARANISO data sets were compared to our multitemperature series in Coot and using phenix.fobs_minus_fobs (Fig. S11) to survey whether diffraction anisotropy gives rise to variations in difference density. For solvent content analysis, rwcontents (Version 7.1.009) from the CCP4 suite (Winn et al., 2011) was used.
5. Accession numbers and data availability
Models and structure factors are available in the Protein Data Bank under the following PDB entry accession codes (see also Tables 1 and 2): 7mhf, 7mhg, 7mhh, 7mhi, 7mhj, and 7mhk for muticonformer models, and 7mhl, 7mhm, 7mhn, 7mho, 7mhp, and 7mhq for ensemble models. New versions (Version 2.0) of each of these structures were deposited after modeling Zn2+ instead of H2O in the active site.
Diffraction data are available at the Integrated Resource for Reproducibility in Macromolecular Crystallography (https://proteindiffraction.org) under the following DOIs: 10.18430/m37mhf, 10.18430/m37mhg, 10.18430/m37mhh, 10.18430/m37mhi, 10.18430/m37mhj, and 10.18430/m37mhk.
Fo − Fo = isomorphous difference electron-density map. COVID-19 = coronavirus disease 2019. SARS-CoV-2 = severe acute respiratory syndrome coronavirus 2. Mpro = SARS-CoV-2 coronavirus main protease.
Supporting information file. DOI: https://doi.org//10.1107/S2052252522007497/be5291sup1.pdf
‡These authors contributed equally to this work.
We thank our reviewers for providing useful feedback that improved the manuscript, Jean Jakoncic for consultation on anomalous data processing, and Michael Thompson for helpful discussions. This research was supported by the DOE Office of Science through the National Virtual Biotechnology Laboratory (NVBL) with funding from the Coronavirus CARES Act and additional funding was provided by Brookhaven National Laboratory (BNL) for research on COVID-19 (LDRD 20–042). This research used beamline FMX (17-ID-2) of the National Synchrotron Light Source II, a US Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Brookhaven National Laboratory under contract No. DE-SC0012704. The Center for BioMolecular Structure (CBMS) is primarily supported by the National Institutes of Health, National Institute of General Medical Sciences (NIGMS) through a Center Core P30 Grant (P30GM133893), and by the DOE Office of Biological and Environmental Research (KP1605010). The authors declare no competing interests.
Funding for this research was provided by: National Institutes of Health (grant No. R35 GM133769 to DAK); Brookhaven National Laboratory (award No. LDRD 20-042).
Afonine, P. V., Grosse-Kunstleve, R. W., Echols, N., Headd, J. J., Moriarty, N. W., Mustyakimov, M., Terwilliger, T. C., Urzhumtsev, A., Zwart, P. H. & Adams, P. D. (2012). Acta Cryst. D68, 352–367. Web of Science CrossRef CAS IUCr Journals Google Scholar
Andi, B., Kumaran, D., Kreitler, D. F., Soares, A. S., Keereetaweep, J., Jakoncic, J., Lazo, E. O., Shi, W., Fuchs, M. R., Sweet, R. M., Shanklin, J., Adams, P. D., Schmidt, J. G., Head, M. S. & McSweeney, S. (2022). Sci. Rep. 12, 12197. Web of Science CrossRef PubMed Google Scholar
Arkhipova, V., Guskov, A. & Slotboom, D. (2017). J. Gen. Physiol. 149, 1091–1103. Web of Science CrossRef CAS PubMed Google Scholar
Atakisi, H., Moreau, D. W. & Thorne, R. E. (2018). Acta Cryst. D74, 264–278. Web of Science CrossRef IUCr Journals Google Scholar
Babcock, N. S., Keedy, D. A., Fraser, J. S. & Sivak, D. A. (2018). bioRxiv. doi: https://doi.org/10.1101/448795. Google Scholar
Bansia, H., Mahanta, P., Yennawar, N. H. & Ramakumar, S. (2021). J. Chem. Inf. Model. 61, 1322–1333. Web of Science CrossRef CAS PubMed Google Scholar
Baum, A., Ajithdoss, D., Copin, R., Zhou, A., Lanza, K., Negron, N., Ni, M., Wei, Y., Mohammadi, K., Musser, B., Atwal, G. S., Oyejide, A., Goez-Gazi, Y., Dutton, J., Clemmons, E., Staples, H. M., Bartley, C., Klaffke, B., Alfson, K., Gazi, M., Gonzalez, O., Dick, E. Jr, Carrion, R. Jr, Pessaint, L., Porto, M., Cook, A., Brown, R., Ali, V., Greenhouse, J., Taylor, T., Andersen, H., Lewis, M. G., Stahl, N., Murphy, A. J., Yancopoulos, G. D. & Kyratsous, C. A. (2020). Science, 370, 1110–1115. Web of Science CrossRef CAS PubMed Google Scholar
Beigel, J. H., Tomashek, K. M., Dodd, L. E., Mehta, A. K., Zingman, B. S., Kalil, A. C., Hohmann, E., Chu, H. Y., Luetkemeyer, A., Kline, S., Lopez de Castilla, D., Finberg, R. W., Dierberg, K., Tapson, V., Hsieh, L., Patterson, T. F., Paredes, R., Sweeney, D. A., Short, W. R., Touloumi, G., Lye, D. C., Ohmagari, N., Oh, M.-D., Ruiz-Palacios, G. M., Benfield, T., Fätkenheuer, G., Kortepeter, M. G., Atmar, R. L., Creech, C. B., Lundgren, J., Babiker, A. G., Pett, S., Neaton, J. D., Burgess, T. H., Bonnett, T., Green, M., Makowski, M., Osinusi, A., Nayak, S., Lane, H. C. & ACTT-1 Study Group Members, (2020). N. Engl. J. Med. 383, 1813–1826. Web of Science CrossRef PubMed Google Scholar
Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., Shindyalov, I. N. & Bourne, P. E. (2000). Nucleic Acids Res. 28, 235–242. Web of Science CrossRef PubMed CAS Google Scholar
Boras, B., Jones, R. M., Anson, B. J., Arenson, D., Aschenbrenner, L., Bakowski, M. A., Beutler, N., Binder, J., Chen, E., Eng, H., Hammond, H., Hammond, J., Haupt, R. E., Hoffman, R., Kadar, E. P., Kania, R., Kimoto, E., Kirkpatrick, M. G., Lanyon, L., Lendy, E. K., Lillis, J. R., Logue, J., Luthra, S. A., Ma, C., Mason, S. W., McGrath, M. E., Noell, S., Scott Obach, R., O?Brien, M. N., O?Connor, R., Ogilvie, K., Owen, D., Pettersson, M., Reese, M. R., Rogers, T. F., Rossulek, M. I., Sathish, J. G., Shirai, N., Steppan, C., Ticehurst, M., Updyke, L. W., Weston, S., Zhu, Y., Wang, J., Chatterjee, A. K., Mesecar, A. D., Frieman, M. B., Anderson, A. S. & Allerton, C. (2021). In Discovery of a Novel Inhibitor of Coronavirus 3CL Protease for the Potential Treatment of COVID-19. Cold Spring Harbor Laboratory. Google Scholar
Broom, A., Rakotoharisoa, R. V., Thompson, M. C., Zarifi, N., Nguyen, E., Mukhametzhanov, N., Liu, L., Fraser, J. S. & Chica, R. A. (2020). Nat. Commun. 11, 4808. Web of Science CrossRef PubMed Google Scholar
Burnley, B. T., Afonine, P. V., Adams, P. D. & Gros, P. (2012). eLife, 1, e00311. Web of Science CrossRef PubMed Google Scholar
Bury, C. S., Brooks-Bartlett, J. C., Walsh, S. P. & Garman, E. F. (2018). Protein Sci. 27, 217–228. Web of Science CrossRef CAS PubMed Google Scholar
Chen, V. B., Arendall, W. B., Headd, J. J., Keedy, D. A., Immormino, R. M., Kapral, G. J., Murray, L. W., Richardson, J. S. & Richardson, D. C. (2010). Acta Cryst. D66, 12–21. Web of Science CrossRef CAS IUCr Journals Google Scholar
Chodera, J., Lee, A. A., London, N. & von Delft, F. (2020). Nat. Chem. 12, 581. Web of Science CrossRef PubMed Google Scholar
Darby, J. F., Hopkins, A. P., Shimizu, S., Roberts, S. M., Brannigan, J. A., Turkenburg, J. P., Thomas, G. H., Hubbard, R. E. & Fischer, M. (2019). J. Am. Chem. Soc. 141, 15818–15826. Web of Science CrossRef CAS PubMed Google Scholar
Davis, I. W., Arendall, W. B. III, Richardson, D. C. & Richardson, J. S. (2006). Structure, 14, 265–274. Web of Science CrossRef PubMed CAS Google Scholar
Diederichs, K. (2006). Acta Cryst. D62, 96–101. Web of Science CrossRef CAS IUCr Journals Google Scholar
Douangamath, A., Fearon, D., Gehrtz, P., Krojer, T., Lukacik, P., Owen, C. D., Resnick, E., Strain-Damerell, C., Aimon, A., Ábrányi-Balogh, P., Brandão-Neto, J., Carbery, A., Davison, G., Dias, A., Downes, T. D., Dunnett, L., Fairhead, M., Firth, J. D., Jones, S. P., Keeley, A., Keserü, G. M., Klein, H. F., Martin, M. P., Noble, M. E. M., O'Brien, P., Powell, A., Reddi, R. N., Skyner, R., Snee, M., Waring, M. J., Wild, C., London, N., von Delft, F. & Walsh, M. A. (2020). Nat. Commun. 11, 5047. Web of Science CrossRef PubMed Google Scholar
El-Baba, T. J., Lutomski, C. A., Kantsadi, A. L., Malla, T. R., John, T., Mikhailov, V., Bolla, J. R., Schofield, C. J., Zitzmann, N., Vakonakis, I. & Robinson, C. V. (2020). Angew. Chem. Int. Ed. 59, 23544–23548. CAS Google Scholar
Emsley, P. & Cowtan, K. (2004). Acta Cryst. D60, 2126–2132. Web of Science CrossRef CAS IUCr Journals Google Scholar
Fan, K., Wei, P., Feng, Q., Chen, S., Huang, C., Ma, L., Lai, B., Pei, J., Liu, Y., Chen, J. & Lai, L. (2004). J. Biol. Chem. 279, 1637–1642. Web of Science CrossRef PubMed CAS Google Scholar
Fischer, M. (2021). Q. Rev. Biophys. 54, E1. Web of Science CrossRef PubMed Google Scholar
Fodor, J., Riley, B. T., Borg, N. A. & Buckle, A. M. (2018). J. Immunol. 200, 4134–4145. Web of Science CrossRef CAS PubMed Google Scholar
Fraser, J. S., van den Bedem, H., Samelson, A. J., Lang, P. T., Holton, J. M., Echols, N. & Alber, T. (2011). Proc. Natl Acad. Sci. USA, 108, 16247–16252. Web of Science CrossRef CAS PubMed Google Scholar
Goyal, B. & Goyal, D. (2020). ACS Comb. Sci. 22, 297–305. Web of Science CrossRef CAS PubMed Google Scholar
Günther, S., Reinke, P. Y. A., Fernández-García, Y., Lieske, J., Lane, T. J., Ginn, H. M., Koua, F. H. M., Ehrt, C., Ewert, W., Oberthuer, D., Yefanov, O., Meier, S., Lorenzen, K., Krichel, B., Kopicki, J.-D., Gelisio, L., Brehm, W., Dunkel, I., Seychell, B., Gieseler, H., Norton-Baker, B., Escudero-Pérez, B., Domaracky, M., Saouane, S., Tolstikova, A., White, T. A., Hänle, A., Groessler, M., Fleckenstein, H., Trost, F., Galchenkova, M., Gevorkov, Y., Li, C., Awel, S., Peck, A., Barthelmess, M., Schlünzen, F., Lourdu Xavier, P., Werner, N., Andaleeb, H., Ullah, N., Falke, S., Srinivasan, V., França, B. A., Schwinzer, M., Brognaro, H., Rogers, C., Melo, D., Zaitseva-Doyle, J. J., Knoska, J., Peña-Murillo, G. E., Mashhour, A. R., Hennicke, V., Fischer, P., Hakanpää, J., Meyer, J., Gribbon, P., Ellinger, B., Kuzikov, M., Wolf, M., Beccari, A. R., Bourenkov, G., von Stetten, D., Pompidor, G., Bento, I., Panneerselvam, S., Karpics, I., Schneider, T. R., Garcia-Alai, M. M., Niebling, S., Günther, C., Schmidt, C., Schubert, R., Han, H., Boger, J., Monteiro, D. C. F., Zhang, L., Sun, X., Pletzer-Zelgert, J., Wollenhaupt, J., Feiler, C. G., Weiss, M. S., Schulz, E.-C., Mehrabi, P., Karničar, K., Usenik, A., Loboda, J., Tidow, H., Chari, A., Hilgenfeld, R., Uetrecht, C., Cox, R., Zaliani, A., Beck, T., Rarey, M., Günther, S., Turk, D., Hinrichs, W., Chapman, H. N., Pearson, A. R., Betzel, C. & Meents, A. (2021). Science, 372, 642–646. Web of Science PubMed Google Scholar
Halle, B. (2004). Proc. Natl Acad. Sci. USA, 101, 4793–4798. Web of Science CrossRef PubMed CAS Google Scholar
Jaskolski, M., Dauter, Z., Shabalin, I. G., Gilski, M., Brzezinski, D., Kowiel, M., Rupp, B. & Wlodawer, A. (2021). IUCrJ, 8, 238–256. Web of Science CrossRef CAS PubMed IUCr Journals Google Scholar
Jin, Z., Du, X., Xu, Y., Deng, Y., Liu, M., Zhao, Y., Zhang, B., Li, X., Zhang, L., Peng, C., Duan, Y., Yu, J., Wang, L., Yang, K., Liu, F., Jiang, R., Yang, X., You, T., Liu, X., Yang, X., Bai, F., Liu, H., Liu, X., Guddat, L. W., Xu, W., Xiao, G., Qin, C., Shi, Z., Jiang, H., Rao, Z. & Yang, H. (2020). Nature, 582, 289–293. Web of Science CrossRef CAS PubMed Google Scholar
Kabsch, W. (2010). Acta Cryst. D66, 125–132. Web of Science CrossRef CAS IUCr Journals Google Scholar
Karplus, P. A. & Diederichs, K. (2012). Science, 336, 1030–1033. Web of Science CrossRef CAS PubMed Google Scholar
Keedy, D. A., Hill, Z. B., Biel, J. T., Kang, E., Rettenmaier, T. J., Brandão-Neto, J., Pearce, N. M., von Delft, F., Wells, J. A. & Fraser, J. S. (2018). eLife, 7, e36307. Web of Science CrossRef PubMed Google Scholar
Keedy, D. A., Kenner, L. R., Warkentin, M., Woldeyes, R. A., Hopkins, J. B., Thompson, M. C., Brewster, A. S., Van Benschoten, A. H., Baxter, E. L., Uervirojnangkoorn, M., McPhillips, S. E., Song, J., Alonso-Mori, R., Holton, J. M., Weis, W. I., Brunger, A. T., Soltis, S. M., Lemke, H., Gonzalez, A., Sauter, N. K., Cohen, A. E., van den Bedem, H., Thorne, R. E. & Fraser, J. S. (2015). eLife, 4, e07574. Web of Science CrossRef PubMed Google Scholar
Keedy, D. A., van den Bedem, H., Sivak, D. A., Petsko, G. A., Ringe, D., Wilson, M. A. & Fraser, J. S. (2014). Structure, 22, 899–910. Web of Science CrossRef CAS PubMed Google Scholar
Kiefersauer, R., Than, M. E., Dobbek, H., Gremer, L., Melero, M., Strobl, S., Dias, J. M., Soulimane, T. & Huber, R. (2000). J. Appl. Cryst. 33, 1223–1230. Web of Science CrossRef CAS IUCr Journals Google Scholar
Kneller, D. W., Phillips, G., Kovalevsky, A. & Coates, L. (2020). Acta Cryst. F76, 483–487. Web of Science CrossRef IUCr Journals Google Scholar
Kneller, D. W., Phillips, G., O'Neill, H. M., Jedrzejczak, R., Stols, L., Langan, P., Joachimiak, A., Coates, L. & Kovalevsky, A. (2020). Nat. Commun. 11, 3202. Web of Science CrossRef PubMed Google Scholar
Kneller, D. W., Phillips, G., O'Neill, H. M., Tan, K., Joachimiak, A., Coates, L. & Kovalevsky, A. (2020). IUCrJ, 7, 1028–1035. Web of Science CrossRef CAS IUCr Journals Google Scholar
Kneller, D. W., Phillips, G., Weiss, K. L., Pant, S., Zhang, Q., O'Neill, H. M., Coates, L. & Kovalevsky, A. (2020). J. Biol. Chem. 295, 17365–17373. Web of Science CrossRef CAS PubMed Google Scholar
Kumar, K. S. D., Gurusaran, M., Satheesh, S. N., Radha, P., Pavithra, S., Thulaa Tharshan, K. P. S., Helliwell, J. R. & Sekar, K. (2015). J. Appl. Cryst. 48, 939–942. Web of Science CrossRef CAS IUCr Journals Google Scholar
Lee, J., Worrall, L. J., Vuckovic, M., Rosell, F. I., Gentile, F., Ton, A.-T., Caveney, N. A., Ban, F., Cherkasov, A., Paetzel, M. & Strynadka, N. C. J. (2020). Nat. Commun. 11, 5877. Web of Science CrossRef PubMed Google Scholar
Lewandowski, J. R., Halse, M. E., Blackledge, M. & Emsley, L. (2015). Science, 348, 578–581. Web of Science CrossRef CAS PubMed Google Scholar
Lu, S., Qiu, Y., Ni, D., He, X., Pu, J. & Zhang, J. (2020). Drug Discov. Today, 25, 177–184. Web of Science CrossRef CAS PubMed Google Scholar
Moriarty, N. W., Grosse-Kunstleve, R. W. & Adams, P. D. (2009). Acta Cryst. D65, 1074–1080. Web of Science CrossRef CAS IUCr Journals Google Scholar
Moriarty, N. W., Janowski, P. A., Swails, J. M., Nguyen, H., Richardson, J. S., Case, D. A. & Adams, P. D. (2020). Acta Cryst. D76, 51–62. Web of Science CrossRef IUCr Journals Google Scholar
Noske, G. D., Nakamura, A. M., Gawriljuk, V. O., Fernandes, R. S., Lima, G. M. A., Rosa, H. V. D., Pereira, H. D., Zeri, A. C. M., Nascimento, A. A. F. Z., Freire, M. C. L. C., Oliva, G. & Godoy, A. S. (2021). In A Crystallographic Snapshot of SARS-CoV-2 Main Protease Maturation Process. Cold Spring Harbor Laboratory. Google Scholar
Olson, B., Cruz, A., Chen, L., Ghattas, M., Ji, Y., Huang, K., Ayoub, S. Jr, Luchko, T., McKay, D. J. & Kurtzman, T. (2020). J. Comput. Aided Mol. Des. 34, 1219–1228. Web of Science CrossRef CAS PubMed Google Scholar
Panchariya, L., Khan, W. A., Kuila, S., Sonkar, K., Sahoo, S., Ghoshal, A., Kumar, A., Verma, D. K., Hasan, A., Khan, M. A., Jain, N., Mohapatra, A. K., Das, S., Thakur, J. K., Maiti, S., Nanda, R. K., Halder, R., Sunil, S. & Arockiasamy, A. (2021). Chem. Commun. 57, 10083–10086. Web of Science CrossRef CAS Google Scholar
Pearce, N. M. & Gros, P. (2021). Nat. Commun. 12, 5493. Web of Science CrossRef PubMed Google Scholar
Ploscariu, N., Burnley, T., Gros, P. & Pearce, N. M. (2021). Acta Cryst. D77, 1357–1364. Web of Science CrossRef IUCr Journals Google Scholar
Riley, B. T., Wankowicz, S. A., de Oliveira, S. H. P., van Zundert, G. C. P., Hogan, D. W., Fraser, J. S., Keedy, D. A. & van den Bedem, H. (2021). Protein Sci. 30, 270–285. Web of Science CrossRef CAS PubMed Google Scholar
Sanchez-Weatherby, J., Bowler, M. W., Huet, J., Gobbo, A., Felisaz, F., Lavault, B., Moya, R., Kadlec, J., Ravelli, R. B. G. & Cipriani, F. (2009). Acta Cryst. D65, 1237–1246. Web of Science CrossRef CAS IUCr Journals Google Scholar
Schneider, D. K., Shi, W., Andi, B., Jakoncic, J., Gao, Y., Bhogadi, D. K., Myers, S. F., Martins, B., Skinner, J. M., Aishima, J., Qian, K., Bernstein, H. J., Lazo, E. O., Langdon, T., Lara, J., Shea-McCarthy, G., Idir, M., Huang, L., Chubar, O., Sweet, R. M., Berman, L. E., McSweeney, S. & Fuchs, M. R. (2021). J. Synchrotron Rad. 28, 650–665. Web of Science CrossRef CAS IUCr Journals Google Scholar
Studier, F. W. (2005). Protein Expr. Purif. 41, 207–234. Web of Science CrossRef PubMed CAS Google Scholar
Terwilliger, T. C., Grosse-Kunstleve, R. W., Afonine, P. V., Moriarty, N. W., Zwart, P. H., Hung, L.-W., Read, R. J. & Adams, P. D. (2008). Acta Cryst. D64, 61–69. Web of Science CrossRef CAS IUCr Journals Google Scholar
Tickle, I. J., Flensburg, C., Keller, P., Paciorek, W., Sharff, A., Vonrhein, C. & Bricogne, G. (2018). STARANISO, https://staraniso.globalphasing.org/cgi-bin/staraniso.cgi. Cambridge, UK: Global Phasing Ltd. Google Scholar
V'kovski, P., Kratzel, A., Steiner, S., Stalder, H. & Thiel, V. (2021). Nat. Rev. Microbiol. 19, 155–170. Web of Science CAS PubMed Google Scholar
Vuong, W., Khan, M. B., Fischer, C., Arutyunova, E., Lamer, T., Shields, J., Saffran, H. A., McKay, R. T., van Belkum, M. J., Joyce, M. A., Young, H. S., Tyrrell, D. L., Vederas, J. C. & Lemieux, M. J. (2020). Nat. Commun. 11, 4282. Web of Science CrossRef PubMed Google Scholar
Williams, C. J., Headd, J. J., Moriarty, N. W., Prisant, M. G., Videau, L. L., Deis, L. N., Verma, V., Keedy, D. A., Hintze, B. J., Chen, V. B., Jain, S., Lewis, S. M., Arendall, W. B., Snoeyink, J., Adams, P. D., Lovell, S. C., Richardson, J. S. & Richardson, J. S. (2018). Protein Sci. 27, 293–315. Web of Science CrossRef CAS PubMed Google Scholar
Winn, M. D., Ballard, C. C., Cowtan, K. D., Dodson, E. J., Emsley, P., Evans, P. R., Keegan, R. M., Krissinel, E. B., Leslie, A. G. W., McCoy, A., McNicholas, S. J., Murshudov, G. N., Pannu, N. S., Potterton, E. A., Powell, H. R., Read, R. J., Vagin, A. & Wilson, K. S. (2011). Acta Cryst. D67, 235–242. Web of Science CrossRef CAS IUCr Journals Google Scholar
Winter, G. & McAuley, K. E. (2011). Methods, 55, 81–93. Web of Science CrossRef CAS PubMed Google Scholar
Winter, G., Waterman, D. G., Parkhurst, J. M., Brewster, A. S., Gildea, R. J., Gerstel, M., Fuentes-Montero, L., Vollmar, M., Michels-Clark, T., Young, I. D., Sauter, N. K. & Evans, G. (2018). Acta Cryst. D74, 85–97. Web of Science CrossRef IUCr Journals Google Scholar
Woldeyes, R. A., Sivak, D. A. & Fraser, J. S. (2014). Curr. Opin. Struct. Biol. 28, 56–62. Web of Science CrossRef CAS PubMed Google Scholar
Word, J. M., Lovell, S. C., Richardson, J. S. & Richardson, D. C. (1999). J. Mol. Biol. 285, 1735–1747. Web of Science CrossRef CAS PubMed Google Scholar
Zhang, L., Lin, D., Sun, X., Curth, U., Drosten, C., Sauerhering, L., Becker, S., Rox, K. & Hilgenfeld, R. (2020). Science, 368, 409–412. Web of Science CrossRef CAS PubMed Google Scholar
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