key: cord-0910603-57uhyhc8
authors: Campitelli, P.; Lu, J.; Ozkan, S. B.
title: Dynamic Allostery Highlights the Evolutionary Differences between the CoV-1 and CoV-2 Main Proteases
date: 2022-03-15
journal: Biophys J
DOI: 10.1016/j.bpj.2022.03.012
sha: e495cfacf0ef4c20735cabac39640d81172ab682
doc_id: 910603
cord_uid: 57uhyhc8

The SARS-CoV-2 coronavirus has become one of the most immediate and widely-studied systems since its identification and subsequent global outbreak from 2019-2021. In an effort to understand the biophysical changes as a result of mutations, the mechanistic details of multiple different proteins within the SARS-CoV-2 virus have been studied and compared with SARS-CoV-1. Focusing on the main protease (mPro), we first explored the long-range dynamics using the Dynamic Coupling Index (DCI) to investigate the dynamic coupling between the catalytic site residues and the rest of the protein, both inter and intra chain, for the CoV-1 and CoV-2 mPro. We found that there is significant cross-chain coupling between these active sites and specific distal residues in the CoV-2 mPro not present in CoV-1. The enhanced long distance interactions, particularly between the two chains, suggest subsequently enhanced cooperativity for CoV-2. A further comparative analysis of the dynamic flexibility using the Dynamic Flexibility Index (DFI) between the CoV-1 and CoV-2 mPros shows that the inhibitor binding near active sites induces change in flexibility to a distal region of the protein, opposite in behavior between the two systems; this region becomes more flexible upon inhibitor binding in CoV-1 while it becomes less flexible in the CoV-2 mPro. Upon inspection, we show that, on average, the dynamic flexibility of the sites substituted from CoV-1 to CoV-2 changes significantly less than the average calculated across all residues within the structure, indicating that the differences in behaviors between the two systems is likely the result of allosteric influence, where the new substitutions in CoV-2 induce flexibility and dynamical changes elsewhere in the structure.

Since its initial onset in late 2019, SARS-CoV-2, or the coronavirus-2, has spread rapidly through over 200 countries resulting in millions of deaths worldwide, becoming the cause of a global pandemic unprecedented in the modern era. In a massive effort to combat the viral spread and subsequent toll on human health and life, large swathes of the scientific community including disciplines ranging from genetics, evolutionary biology, biological physics, data science, immunology and others have taken part in a focused effort to stymie the contagion outbreak resulting in an unparalleled development and production of vaccinations within one year of the virus' discovery.

Given the high rate of infection, profound global impact of the coronavirus-2 disease and the implication that the virus will continue to go through mutations which may result in more transmissible strains that may prove resistant to currently approved immunization procedures, the continued investigation into additional vaccinations or treatment methods remains critical. At the heart of further drug discovery, an understanding of the virus' biophysical behavior is required. Particularly, it is important to obtain mechanistic insights into critical proteins of the virus which regulate its ability to interact with host cells and successfully self-replicate for two major reasons. First, to find or design novel drugs or possible allosteric inhibitors, the dynamical behavior of viral proteins must be understood. Second, the mechanistic details of these proteins and, subsequently, how the shape of the mutational landscape regulates protein dynamics is necessary to understand whether observed mutations will confer resistance to developed drugs.

To that end, we focus on the SARS-CoV-2 main protease (mPro), an enzyme critical for the successful reproduction of the virus upon host cell infection. The mPro processes two major polyproteins into several nonstructural proteins which, in turn, are ultimately responsible for the production of structural proteins comprising the envelope, membrane, spike and nucleocapsid structural proteins (1, 2) . Thus, the mPro has undergone significant investigation as a potential drug target (3) (4) (5) (6) . When compared to CoV-1, the CoV-2 mPro contains 12 amino acid variations (henceforth 'mutations' will refer to these variations between the two systems), none of which have been implicated to play important roles in the enzymatic activity of the protein (7) . With a 96% sequence identity between the two structures and an of RMSD ~0.73 Å, logic dictates that inhibitors developed for the CoV-1 mPro should exhibit some level of effectiveness in the CoV-2 system; however in both in vitro and cell-based assay experiments, most SARS-CoV-1 mPro inhibitors which showed nM-level activity were relatively impotent against the SARS-CoV-2 mPro in enzymatic assays (8) (9) (10) . Further, current studies often report different enzymatic J o u r n a l P r e -p r o o f turnover rates of each system; reports range from the CoV-2 mPro exhibiting approximately similar kcat/Km values (10) to a two-fold increase in kcat/Km as compared to CoV-1 (0.21 μM -1 s -1 and 0.11 μM -1 s -1 , respectively) (11, 12) .

Importantly, both the CoV-1 and CoV-2 mPro systems are only biologically active as a homodimer, remaining inert in their monomeric forms (10, 13) . The dimerization dissociation constant has also been reported to range widely, from similar KD values (~2.5 μM) (10)to large differences and ranges (0.14 ± 0.03 μM for CoV-2 and ranges of 230 ± 30 μM down to 0.19 ± 0.03 μM for CoV-1) (14-16). However, it has been shown that small-molecule inhibitors can affect the CoV-2 monomer-dimer equilibrium (16, 7) .

A previous study conducted by McLeish, et al. (17) employed the ENM model using low-frequency modes to study allosteric interactions and dynamics of the CoV-2 mPro . This work showed that several regions of the protein exhibited strong cross-chain dynamic coupling as measured by a residue-residue dynamic cross-correlation map. Further, their work suggested that there are several residues located on the dimeric interface critical to allosteric interactions with the CoV-2 mPro catalytic sites and that there are additional allosteric sites that can significantly change the active site coupling by slight stiffening of local harmonic restraints employed within the ENM model.

In an effort to understand the specific residues which may have an effect on the differences in communication between chains of the CoV-1 and CoV-2 mPros , particularly to elucidate how evolutionary changes give rise to differences in dynamical behavior between the two, we employ two metrics, the Dynamic Flexibility Index (DFI) and the Dynamic Coupling Index (DCI) to evaluate the dynamics and site-specific interactions between different regions of the two proteins using coordinate information from Molecular Dynamics (MD) simulation data of two PDBs from each system (CoV-1:

3TNT (18) and 1UK3 (19) , CoV-2: 5R7Y (20) and 6Y84 (21)). Additional ENM analysis was performed using SARS-CoV-2 structures 6M03(22)(unbound) and 7BUY(5) (bound) and SARS-CoV-1 structures 1UK3(19)(unbound) and 3TIU(18) (bound). The DFI parameter measures each position's sensitivity to perturbations within a network of interactions and represents a given amino acid position's ability to explore its local conformational space (flexibility) while DCI measures the displacement response of an individual position to the perturbation of a second position or group of positions, relative to the average response to any perturbation of all possible positions and can capture the dynamic coupling between amino acid pairs. In this mechanistic study, we utilize DFI and DCI to further understand the differences in cross-chain interactions, site-specific changes in flexibility and the behavior the systems exhibit when J o u r n a l P r e -p r o o f bound to modeled inhibitors. Specifically, we investigate how the sequence variations between two mPros modify the dynamics and its consequence on observed biophysical properties.

Topology files for all structures were prepared using the AMBER LEaP program with the ff14SB force field (23) . Hydrogen atoms were added and each structure was surrounded by a 16.0 Å cubic box of water molecules using the TIP3P (24) water model. Na + and Clatoms were added for neutralization.

Each system was energy-minimized using the AMBER SANDER package (23) to remove any unfavorable torsional angles or steric clashes and ensure that the system reached a local energetic minimum. First, the protein was kept fixed with harmonic restraints to allow surrounding water molecules and ions to relax, followed by a second minimization step in which the restraints were removed and the proteinsolution was further minimized. Both minimization steps employed the method of steepest descent followed by conjugate gradient. The systems were then heated from 0K to 300K over 250 ps, at which point long-range electrostatic interactions were calculated using the particle mesh Ewald method (25) .

Direct-sum, non-bonded interactions were cut off at distances of 9.0 Å or greater and bond lengths of all covalent and hydrogen bonds were constrained using the SHAKE algorithm (26) . During production and heat-up, we used a Langevin thermostat to control the temperature at 300 K and a Berendsen barostat to adjust the pressure at 1 bar. A time step of 2 fs for the integrator was used for both the heat-up and production runs. To ensure the robustness of our analysis, we used two apo PDB structures separately for both CoV-1(3TNT (18), 1UK3 (19) ) and CoV-2(6Y84 (21), 5R7Y (20) ) and conducted the simulations above. All four simulations were run for a total of 1000 ns each, generating 2 μs of total simulation time for each system. The comparisons of each system at multiple time windows through the simulations as well their associated standard error is presented in Figure S2 and Figure S3 . RMSD profiles also show simulation convergence, found in Figure S4 .

Protocol for convergence of MD simulation dynamics has been established previously (27) . To calculate DFI and DCI, covariance matrix data were calculated over 200 ns slices of the trajectory of each simulation, using 50 ns moving windows that overlap by 25 ns. Fundamentally, the use of the Hessian by default indicates that we are restricting ourselves to a harmonic potential and, as such, we assume J o u r n a l P r e -p r o o f the data are sampled from a Gaussian distribution. Appropriate sampling is met assuming ergodicity is fulfilled in both simulation time as well as the time windows in which the covariance matrices used for analysis, resulting in two of the basic conditions: (i) All conformations sampled must belong to the same distribution to ensure the consistency of the potential energy of the systems underlying the equilibrium distributions. (ii) The time windows and subsequent covariance matrices obtained should be independent of the initial atomic coordinates to eliminate global motions and accurately capture equilibrium coordinate information. As such, the final average DFI profiles will be independent of the window size; that is, the averaging of DFI profiles from different time window sizes (i.e. 50 ns vs 75 ns) will give similar results and the calculated covariance matrices extracted from different times of trajectories should also result in similar DFI profiles.

The Dynamic Flexibility Index utilizes a PRS technique that combines Elastic Network Models (ENM), Linear Response Theory (LRT) and Perturbation Response Scanning (PRS), where mutations or amino acid interactions are modeled as fluctuation responses to force perturbations (28, 29) . The fluctuation response can be determined as:

Here,, ∆R is calculated as the fluctuation response vector of residue j as a result of unit force's F perturbation on residue i, averaged over multiple unit force directions to simulate an isotropic perturbation. H is the Hessian, a 3N × 3N matrix which can be constructed from 3-D atomic coordinate information and is composed of the second derivatives of the harmonic potential with respect to the components of the position's vectors of length N. For this work, the Hessian matrix was extracted directly from molecular dynamics simulations as the inverse of the covariance matrix. This method allows one to implicitly capture specific physiochemical properties and more accurate residue-residue interactions via atomistic force fields and subsequent all-atom simulation data.

Each position in the structure was perturbed sequentially to generate a Perturbation Response Matrix A J o u r n a l P r e -p r o o f

where | | = √⟨(∆ ) 2 ⟩ is the magnitude of fluctuation response at position i due to perturbations at position j. The DFI value of position i is then treated as the displacement response of position i relative to the net displacement response of the entire protein, which is calculated by sequentially perturbing each position in the structure.

It is also often useful to quantify position flexibility relative to the flexibility ranges unique to individual structures. To that end, DFI can be presented as a percentile rank

where n≤i is the number of positions with a DFI value ≤ DFIi. The denominator is the total displacement of all residues, used as a normalizing factor. All %DFI calculations present in this work used the DFI value of every residue of the full structures for ranking. The DFI parameter can be considered a measure of a given amino acid position's ability to explore its local conformational space.

Similar to DFI, the dynamic coupling index (DCI) also utilizes PRS with ENMand LRT. 

J o u r n a l P r e -p r o o f

As above, DCI can also be presented as a percentile rank as

where m≤i is the number of positions with a DCI value ≤ DCIi. As such, this parameter represents a measure of the dynamic coupling between i upon a perturbation to j.

One of the most important aspects of both DFI and DCI is that the entire network of interactions is explicitly included in subsequent calculations without the need of dimensionality reduction techniques such as Normal Mode Analysis through Principal Component Analysis. If one considers interactions such as allostery as an emergent property of an anisotropic interaction network, it is critical to include the interactions of the entire network to accurately model the effect one residue can have on another.

In the work presented here, we first wanted to investigate the ability for the catalytic sites to allosterically interact with the rest of the structure and, potentially, identify additional putative allosteric residues. Here we implement one of our tools which can determine the strength of dynamic coupling between two residues i and j, the Dynamic Coupling Index (DCI). DCI has been used previously in many different systems to identify important sites of regulation, particularly positions distal to active site residues named dynamic allosteric residue coupling (DARC) sites. DARC sites control the dynamics of the active site through dynamic allostery and previous work has shown that mutation of DARC sites could alter function (29) (30) (31) 28, (32) (33) (34) .

Here we analyzed the dynamic coupling between the catalytic residues H41 and C145 of chain A and the rest of the structure by applying force perturbations to H41 and C145 simultaneously. The obtained DCI profiles are then rescaled using percentile rankings (i.e. %DCI). Figure 1 shows the graphical depiction of this analysis. Dynamic coupling analysis (measured by %DCI) between catalytic site residues of H41 and C145 of chain A (yellow spheres) and the other residues of chain A and chain B mapped onto the SARS-CoV-2 mPro (left, PDB ID 5R7Y (20) ) and the the SARS-CoV-1 mPro (PDB ID 2GZ9 (35)). Green regions of each structure indicate stronger dynamic coupling while purple represents weak dynamic coupling. Within the chain the %DCI distribution is as expected based on proximity; residues close to the catalytic sites are strongly coupled, with those further away exhibiting weak coupling and this behavior is consistent across both systems. However, for A-to-B interactions, this behavior is nearly the opposite of what one would expect if coupling was a measure of proximity alone. Here, regions of chain B close in space to the chain A catalytic sites are weakly coupled to these sites, whereas some distal portions are very strongly coupled. Chain B sites which exhibited particularly strong coupling to the chain A catalytic residues are circled in red and represented as spheres, comprised of residues E55, I59 and R60 (top right circles) and residues N277, R279 and L286 (bottom circles).

Within a given chain, the %DCI distribution is, as expected, based on proximity; residues close to the catalytic sites are strongly coupled, with those further away exhibiting weak coupling. However, for where it was shown that this position is critical for communication between several domains and the active site and that this position was critical for successful viral replication (36) . Taken together, this work implies that this region of the structure may play a major role in allosteric regulation of the enzyme.

These coupling profiles further indicate that cross-chain communication is likely an important mechanistic regulator for the proper functioning of the SARS-CoV-2 mPro. In fact, a direct comparison of the same analysis to the CoV-1 mPro shows that this strong inter-chain coupling between the catalytic sites and these residue groups is nearly lost completely (Fig. 1 ).

Given the noticeable differences in coupling between the two systems, we next wanted to compare the changes in dynamic coupling to the active site residues of the mutation sites themselves. Figure 2 shows this direct comparison. In Figure 2 and subjected to further study (39) . Interestingly, a comparative analysis of the change in flexibility of these coldspots (of both chains A and B) between CoV-2 and CoV-1 shows that, while evolutionarily conserved, these sites experienced a significantly greater change in dynamic flexibility (as measured by %DFI) when compared to all residues in the structure (see supplemental figure S1 ). Additionally, performing dynamic coupling analysis of these coldspots residues uncovered a unique relationship for six specific positions. Through crystallographic studies, residues L141, F185 and Q192 of both chains A and B were shown to be structurally important, involved in the formation of substrate-binding sites (40, 22, 41) . When we analyzed these positions' dynamic coupling to catalytic site residues H41 and C145, we found that while the intrachain coupling remained relatively unchanged (within one standard deviation of the mean), the cross-chain dynamic coupling was significantly enhanced in SARS-CoV-2 with all three sites showing an increase in %DCI greater than one standard deviation from the mean. Further, positions F185 and Q192, which play critical roles in stabilizing the active sites (40, 22, 41) , exhibited %DCI increases greater than two standard deviations from the mean. Since many of the large changes in coupling are located at sites distal to the mutation sites, we wanted to next understand the changes between the two systems via site-specific flexibility as these changes in coupling could be due to dynamical differences which arise from changes in amino acid flexibility. 

In an effort to understand the mechanics underlying the behavior of the SARS-CoV-2 mPro, a comparative analysis of the dynamical differences between SARS-CoV-1 and SARS-CoV-2 was performed to capture any major differences in flexibility. Particularly, we wanted to investigate the dynamic flexibility differences as related to inhibitory binding events. To this end we studied the mPro of each virus using the Dynamic Flexibility Index (DFI). Similar to DCI, DFI combines PRS and LRT (28, 29) to evaluate each position's displacement response to random force perturbations at other previously been shown to be mechanistically critical in determining or regulating protein function (46, 47, 33) . Hinges play a pivotal role in the transfer of force through external perturbations throughout the chain in a cascading fashion and often control and mediate protein motion, similar to joints in a skeleton. Further, the formation of hinges, or, "hinge-shift mechanisms", have been linked to gain-offunction in multiple different enzyme families via protein evolution (48, 30, 49, 50) . Interestingly the same region also has prominent differences in DFI and a similar region exists between positions 231-261.

These results indicate that the differences in their biophysical properties such as dimerization (10, (14) (15) (16) between the two systems may be explained, mechanistically, through allosteric modulation as the formation of the hinge can allow for a greater ability for these regions to communicate across chains as a result of the higher propensity to transfer force elsewhere throughout the structure. Here, the mutations cause changes in flexibility of amino acid positions located elsewhere in the structure and, subsequently, result in differing dynamics between the two structures.

As comparative flexibility analysis above suggests that allosteric regulation may be a key component in capturing the changes in dynamics between the CoV-1 and CoV-2 mPro systems, we next wanted to determine how the allosteric response to ligand binding events (particularly inhibitor binding) differed between the two. In fact, recent work using Gaussian accelerated molecular dynamics indicates that there are potential cryptic binding pockets located far from the active site which may act to inhibit the active sites allosterically when bound to inhibitory drugs (51) . (19)(unbound) and PDB ID 3TIU(18) (bound). Generally, the dynamic flexibility profiles between structures were similar in both inhibitor-unbound and inhibitor-bound (bottom). A specific difference in behavior was observed for a set of residues (N277, R279 and L286, black circle) located at the dimeric interface of both chains A and B. Notably, when bound to an inhibitor, these residues of the CoV-1 mPro became more flexible, whereas the same residues in the inhibitor-bound CoV-2 mPro became less flexible. These figures were rendered using PyMOL (53) .

Here we have conducted a comparative analysis between the SARS-CoV-1 and SARS-CoV-2 mPro systems to shed mechanistic insight on the biophysical changes associated with the mutations between these two enzymes. When the DCI metric was applied to these two systems, we first found that the cross-chain dynamic coupling is enhanced for the CoV-2 mPro catalytic site residues as compared to the CoV-1 mPro system. The DCI profiles indicated that this type of cross-chain communication is likely an important mechanistic regulator and may be a critical functional difference between these two systems.

To further understand the mechanistic changes associated with the virus' functional evolution we utilized DFI to analyze the flexibility differences and found that, surprisingly, most of the large changes in amino acid flexibility in occurred CoV-2 at sites other than the sites that are substituted when CoV-1 and

CoV-2 sequences are compared. That is, the mutations brought about dynamical changes in the SARS-CoV-2 mPro at locations distal to the sites of mutation suggesting that allosteric regulation may be a key component in capturing the changes in dynamics between CoV-1 and CoV-2.

To determine how the allosteric response to ligand binding events (particularly inhibitor binding) differed between the two by incorporating the inhibitor interactions at the active site using the inhibitor bound structures. Here, the CoV-1, mPro (PDB ID 3TIU) and CoV-2 mPro (PDB ID 7BUY) structures were complexed with inhibitors bound to their respective catalytic sites, where four alpha carbons among each inhibitor were used as additional nodes in the ENM network. From these models, we analyzed the structural dynamics associated with ligand-bound mPro forms. Our results showed that dynamic flexibility differences were observed for a set of residues located at the dimeric interface of both chains while the DFI profiles of the rest were largely unchanged between CoV-1 and CoV-2 mPros. Notably, when bound to an inhibitor, these residues of the CoV-1 mPro became more flexible, whereas the same J o u r n a l P r e -p r o o f residues in the inhibitor-bound CoV-2 mPro became less flexible suggesting that interdomain interactions critical for mPro activity(54-56) get much less affected in CoV-2 when inhibitor is bound to active site.

Overall, our work shows that the CoV-2 mPro system exhibits enhanced cross-chain communication between catalytic site residues and the rest of the structure. Further, both dynamic coupling and dynamic flexibility analyses indicates that, largely, the dynamic changes as evaluated by DCI and DFI occur not at the sites of mutation but other, distal regions of the protein. This indicates that the functional differences between these two proteins are a result of dynamic allostery induced elsewhere in the structure upon these mutations. Finally, we show that specific regions of the CoV-2 mPro exhibit markedly different flexibility behavior when bound to an inhibitior as compared to the CoV-1 mPro.

Upon comparison between multiple metrics presented here as well as previous experimental and computational work, we highlight a region in which mutations or binding events may be able to inhibit proper functioning of the CoV-2 mPro. Future work will focus on the analysis of these signature regions with the greatest change in coupling and flexibility in an effort to identify putative allosteric binding sites for potential inhibitory drug discovery.

P. Campitelli and J. Lu each generated molecular dynamics data for the CoV-1 and CoV-2 systems. P.

Campitelli conducted all analysis presented here with the exception of the ENM modeled, ligand-bound work which was performed by J. Lu. P. Campitelli wrote the manuscript with additional writing contributions and editing from both J. Lu and S. B. Ozkan. S. B. Ozkan was responsible for the oversight and narrative of the analysis found in this manuscript.

The authors declare no competing interests.

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