key: cord-0861272-31iyux9f
authors: Aljindan, Reem Y.; Al-Subaie, Abeer M.; Al-Ohali, Ahoud I.; Kumar D, Thirumal; Doss C, George Priya; Kamaraj, Balu
title: Investigation of nonsynonymous mutations in the Spike protein of SARS-CoV-2 and its interaction with the ACE2 receptor by molecular docking and MM/GBSA approach
date: 2021-07-16
journal: Comput Biol Med
DOI: 10.1016/j.compbiomed.2021.104654
sha: 19d3fdf4db7a099282d6b76631fde582b31ac38f
doc_id: 861272
cord_uid: 31iyux9f

COVID-19 is an infectious and pathogenic viral disease caused by SARS-CoV-2 that leads to septic shock, coagulation dysfunction, and acute respiratory distress syndrome. The spreading rate of SARS-CoV-2 is higher than MERS-CoV and SARS-CoV. The receptor-binding domain (RBD) of the Spike-protein (S-protein) interacts with the human cells through the host angiotensin-converting enzyme 2 (ACE2) receptor. However, the molecular mechanism of pathological mutations of S-protein is still unclear. In this perspective, we investigated the impact of mutations in the S-protein and their interaction with the ACE2 receptor for SAR-CoV-2 viral infection. We examined the stability of pathological nonsynonymous mutations in the S-protein, and the binding behavior of the ACE2 receptor with the S-protein upon nonsynonymous mutations using the molecular docking and MM_GBSA approaches. Using the extensive bioinformatics pipeline, we screened the destabilizing (L8V, L8W, L18F, Y145H, M153T, F157S, G476S, L611F, A879S, C1247F, and C1254F) and stabilizing (H49Y, S50L, N501Y, D614G, A845V, and P1143L) nonsynonymous mutations in the S-protein. The docking and binding free energy (ddG) scores revealed that the stabilizing nonsynonymous mutations show increased interaction between the S-protein and the ACE2 receptor compared to native and destabilizing S-proteins and that they may have been responsible for the virulent high level. Further, the molecular dynamics simulation (MDS) approach reveals the structural transition of mutants (N501Y and D614G) S-protein. These insights might help researchers to understand the pathological mechanisms of the S-protein and provide clues regarding mutations in viral infection and disease propagation. Further, it helps researchers to develop an efficient treatment approach against this SARS-CoV-2 pandemic.

The first case of SARS-Cov-2 was reported in Dec 2019 in Wuhan, China. It was primarily called 2019-nCoV and later designated as SARS-Cov-2 because of its taxonomic and genomics relationship with SARS-CoV [1] [2] . COVID-19, the exceedingly infectious and pathogenic viral disease that leads to septic shock, coagulation dysfunction, and acute respiratory distress syndrome, is caused by SARS-CoV-2 [3] . The transmission rate of SARS-CoV-2 is higher than MERS-CoV and SARS-CoV [4] . SARS-CoV-2 is a single-stranded RNA genome and has 29,930 nucleotides. It has 14 open reading frames (ORFs) that code for 29 proteins, including four crucial structural proteins: spike (S) proteins, membrane (M), nucleocapsid (N), and envelope (E), as well as nine supporting proteins and 16 non-structural proteins [5] [6] . A recent study reported that mutations within the S-protein intercede the viral section that can tweak viral pathogenesis [7] .

The S-protein is practically separated into two segments and known as S1 and S2. The S1 segment of the S-protein binds with the host angiotensin-converting enzyme2 (ACE2) receptor.

The S2 region, which is isolated from the S1-S2 linker by the protease cleavage sites, aids in virus-host cell fusion [8] . The S1 segment of the S-protein contains a signal peptide (SP), Nterminal domain, and C-terminal domain. The C-terminal domain of the S1 segment contains a receptor-binding motif (RBM) and the receptor-binding domain (RBD), whereas the S2 segment of S-protein contains a heptad repeat (HR1&HR2), transmembrane domain (TM), fusion peptide (FP) , and a small cytoplasmic domain (CP). The above features were combined, making the Sprotein the critical target for developing antibodies, vaccines, and drugs [8, 9] . Furthermore, nonsynonymous mutations found in the Spike-gene may have a vital role in the pathogen's host range and pathogenicity. [10] .

Moreover, discovering a complete set of nonsynonymous mutations in the S-protein of SARS-CoV-2 and its effect on human ACE2 affinity is required to develop therapeutic remedies.

Hence, investigating the S-protein and its progression can improve our insights into host receptor interaction changes and pathogenic levels. We believe that Spike's essential significance, both in terms of antibody target and viral infection, is critical for developing an "early warning" pipeline to assess the pandemic's progression. The GISAID (A global initiative J o u r n a l P r e -p r o o f on sharing avian flu data) consortium has collected so far ~10,000 units of viral genome sequences and made them widely accessible to the research society. This effort allows scientists to examine the data to realize genome diversity [11, 12] , to postulate targetable targets for drug repurposing [13, 14] , and to build prevention approaches [15] . Mercatelli and Giorgi [16] completed the significant comparative analysis by inspecting more than 10,000 complete SARS-CoV-2 genomes. They reported every nonsynonymous mutation, further underlining the uprising of sub-clads, and genomic high mutation spots and arranged them genetically and geographically. These findings are mainly helpful for designing and measuring program efficacy for restricting the spread of SARS-Cov-2 on a regional basis [16] .

Other recent studies have identified 1,815 nonsynonymous mutations that belong to 1, 176 genomes from 29 countries [17, 18] . These mutations fall into 62 distinct types, with 29 different amino acid substitutions present in the S1 segment, 28 in the S2 segment, and 5 at the S1-S2 junction of the S-protein. Mutation D614G was the most abundant mutation and showed the highly infectious A2 subtypes of SARS-CoV-2 [17, 18] . RBD consists of 223 amino acid lengths in the S1-region of the S-protein that connects SARS-like coronaviruses to various hosts by directly binding to cellular ACE2 [19] . Shijulal and Umashankar et al. found six distinct types of mutations namely, V367F, P384L, S438F, K439N, G476S, and V483A in RBD domain of the Sprotein. They further analyzed and identified that only ~2% of the RBD mutations were nonsynonymous reported from the total S-protein. [17, 18] . Recently, the mutation N501Y was observed in the RBD domain, which has spread rapidly in the UK and other countries [20] [21] [22] [23] [24] .

The mutation N501Y has augmented the many discussions and questions, but only a small amount of data relating to it is currently available [21] . The mutation D614G, which originated either in China or Europe, and started to spread swiftly first in Europe and then throughout the world, is the focus of the current pandemic in a number of countries [25] [26] [27] .

In biological studies, in-silico mutational investigations have proven to be a promising alternative to wet-bench techniques [28, 29] . Further in-silico studies should play a vital role in developing information on this new virus to implement procedures that suppress its occurrence.

We hypothesize that nonsynonymous mutations in the S-protein alter the stability of its structure and interaction with the ACE2 receptor. Therefore, in this study utilizing comprehensive bioinformatics, we detected the highly significant nonsynonymous mutations in the S-protein J o u r n a l P r e -p r o o f based on the stability of the S-protein. Further, we predicted the binding behavior of the Sprotein upon nonsynonymous mutations with the ACE2 receptor using the molecular docking and MM_GBSA approaches. MD simulation approach revealed the structural changes of mutant (N501Y and D614G) proteins. Fig. 1 depicts the general workflow used in this work. This could help researchers in better understanding the pathogenic mechanism of S-protein and provide insights into the role of mutations in viral infection and disease propagation. Further, it helps researchers develop an efficient treatment approach against this pandemic SARS-CoV-2.

The human S-protein of the SARS-CoV-2 sequence (length: 1273 amino acids) was obtained in FASTA format from the UNIPROT (ID: P0DTC2) database [30] . The 62 nonsynonymous mutations information of S-protein was collected from the recent articles [17, 18, 21] and displayed in Fig. 2 . The amino acid sequence was further used to construct the three dimensional (3D) protein conformation of native and mutant S-protein. The ACE2 receptor structure (PDB ID: 1R42_A) [31] , was collected from the Protein Data Bank (PDB) database [32] .

The available PDB structures of S-proteins show many missing residues in the 3-D structure.

Some of the collected SNP residue positions do not present in the existed PDB structures. To fix this issue, we have used the previous research studies as an example and implemented them in this study to construct the three-dimensional (3-D) conformation of the native S-protein [33] [34] [35] [36] [37] [38] .

Hence, we used the I-TASSER server [39] to model the native S-protein. It is a threading-based structure prediction program which used to generate the three-dimensional structure of proteins from their amino acid sequence. Some recent studies opted for the threading approach to model the protein to check the interaction with other biological molecules [33] [34] [35] [36] [37] [38] . I-TASSER produces great quality model predictions of three-dimensional structures from amino acid sequences. It is a very accurate and practical approach. We used the PDB ID: 6XR8_A [40] as a template, and it has shown 100% sequence coverage and similarity in the S-protein query sequence. We acquired the most acceptable modeled structure from I-TASSER, based on the high c-score. The predicted model of native S-protein was further refined by the molecular dynamics simulation (MDS) approach using the GROMACS [41] package. The OPLS-AA force field [42] was used for the J o u r n a l P r e -p r o o f 6 refinement. Our MD simulations were carried out according to a procedure that has previously been published [43] [44] [45] . The pre-equilibrated protein was applied for MD simulations till 12 nanoseconds (ns). The RMSD value was calculated to examine the protein's structural alteration.

To further inspect the effect of nonsynonymous mutations on the S-protein, we generated the nonsynonymous mutations in the predicted S-protein model. Moreover, we used the SwissPDB viewer tool [46] program and performed an energy minimization to create the perfect mutant protein structures. Lastly, the PROCHECK [47] and PROSA [48] tools were applied to assess the reliability of native and mutant S-protein.

The S-protein amino acid sequence was used as an input for estimating protein stability upon nonsynonymous mutations. i-Stable Server [49] was applied to predict the stability alterations of S-protein upon nonsynonymous mutations. It gives results from I-Mutant2.0 [50] and MUpro [51] programs, predicting the meta results. The DDG scores from I-Mutant 2. 0 and Conf score from MUpro and i-Stable are considered to predict protein stability. First, I-Mutant-2.0, the DDG score less than 0 is predicted as decreasing stability, whereas DDG scores greater than 0 is predicted as increasing stability. Second, with MUpro, we obtained the Conf Score in the range of −1 to 1. A DDG score higher than 0 predicted increased stability, and a score lower than 0 predicted decreased stability. Last, the i-Stable conf score ranges between 0 and 1, where the higher value exposes higher confidence.

We used the modeled native and mutant S-protein structures as input to estimate how the protein's stability would vary as a result of nonsynonymous mutations.

CUPSAT (Cologne University Protein Stability Analysis Tool) [52] was used to analyze fluctuations in the stability of S-protein upon mutation. We uploaded the model structure of the S-protein as input and selected the location of the amino acid residue to be mutated. The stability of native and mutant S-proteins is predicted by calculating the difference in the free energy score (DDG score). Further, it provides information about J o u r n a l P r e -p r o o f nonsynonymous mutations, secondary structures, torsion angles, and solvent accessibility affected by the mutation.

ii. SDM2

The Site-directed mutator 2 (SDM2) Server was employed to compute the effect of nonsynonymous mutations on the stability of proteins [53] . It is a knowledge based tool and applies ESSTs tables to analyze the alterations in protein stability upon mutation.

Further, it computes the stability changes score between the native and mutant proteins. iii. DUET [54] is an integrated computational method used for calculating the impact of nonsynonymous mutations on the stability of the protein. We uploaded the 3-D model structures of native and mutant S-proteins as an input file.

The native and mutant S-proteins were docked with the ACE2 receptor molecule using HADDOCK v2. 4 [55] . The modeled native and mutant S-proteins and ACE2 protein (PDB ID: 1R42_A) molecules were used. We collected the interacting (binding) residues between the Sprotein and ACE-2 receptor from recent studies. [56] . The collected binding residues in S-protein together with the binding residues in the partner ACE-2 receptor were used as active residues, and the neighboring ones were used as passive residues. The default parameters we applied in our previous studies were also considered for the docking studies [57] [58] [59] [60] . The HADDOCK scoring function was executed based on the weighted aggregate of the various energy terms desolvation (Desolv) and restraints energy, Van der Waals (vdw), electrostatic (Elec), and buried surface area (BSA).

The MM-GBSA free energy decomposition analysis implemented in the S-protein and ACE2 receptor molecule and the binding affinity was estimated by the HawkDock server [61] .

HawkDock considers a relatively more minor protein as a flexible receptor and a bigger protein as an inert receptor. The HawkRank scoring function and the ATTRACT docking algorithm with J o u r n a l P r e -p r o o f MM/GBSA free energy decomposition analysis were used to calculate the binding free energy between the protein complexes. We applied the haddock output complex structures (native and mutant S-proteins and ACE2 receptor) as an input to compute the binding free energy. Lastly, the best ten models of interacting proteins were re-ranked by MM/GBSA calculation [62] [63] [64] . All protein-protein interactions were represented diagrammatically using the LigPlot program [65] and PYMOL software [66] .

The native and most prominent stabilizing mutants (N501Y and D614G) of the S-protein were utilized as input for the molecular dynamics simulations. The initial setup of the MD simulation was prepared by following our previously published protocol [43] [44] [45] . The CHARMM36m force field [67] was implemented in this simulation. The minimization, equilibration, and MD simulation procedures were performed per our previously published protocol [43] [44] [45] . Lastly, the MD simulation was carried out for 15 ns. XMGRACE [68] tool used to analyze the trajectories.

We analyzed the energy plot, RMSD, RMSF, the solvent-accessible surface area (SASA), Principle component analysis (PCA) [69] , and the number of hydrogen bonds (NH-bonds), and we made a comparison between native, stabilizing mutants (N501Y and D614G) to examine the structural behavior of the S-protein.

Observing the mutational effect on the S-protein and its interaction with the ACE2 receptor is very important for predicting the 3D conformation of the S-protein. Hence, we have used the I-TASSER online server to predict the 3D structure of the S-protein. The PDB ID: 6XR8_A was used as a template for predicting the 3D structure of the native S-protein. Further refinement of predicted model protein, MDS for 12ns performed to evaluate the stability of model protein for subsequent studies. The RMSD of the native S-protein is converged after 7ns (Fig. 3) . Further, the average structure of the native S-protein was shown at regular intervals. The best favorable structure was selected at the 12ns MDS, and subsequently used to build the mutant structures.

Further, the Swiss PDB viewer tool was used to build the mutant structures of the S-protein.

Then, PROCHECK and PROSA software used to calculate the consistency of predicted model J o u r n a l P r e -p r o o f structures. The native S-protein showed that 99.7% favored and allowed region and z-score of -6.6. Mutant structures showed in the range of 96.4% to 99.6% in favored residues and allowed region and z-score values in the range of 3.25 to 6.54. These predicted scores corroborate the high confidence level. Therefore, the native and mutant modeled S-protein structures were utilized for SNP and protein-protein docking analysis. The modeled structure of the native Sprotein and ACE2 receptor was shown in Fig. 4 .

To identify the effect of 62 nonsynonymous mutations of different domains (SP, NTD, CTD/RBD, Linker, S1/S2, FP, S2, HR1, HR2, TM, and CT domain) of S-protein, we utilized a broad approach of numerous coherent sequences and structure-based online servers. First, we used the sequence-based i-Stable Server to verify the stability of the S-protein (whether (Table 1) . On the other hand, Mupro predicted 39 and 23 nonsynonymous mutations as decrease stability and increase stability ( Table 1) (Table 1) .

Further, we have collated the CUPSAT, SDM 2.0, and DUET structure-based online servers to predict S-protein stability upon nonsynonymous mutations. CUPSAT predicted that out of 62 nonsynonymous mutations, 36 and 26 nonsynonymous mutations were destabilizing and stabilizing ( Table 2 ). The SDM 2.0 server depicted 28 nonsynonymous mutations with increased stability, and 34 nonsynonymous mutations with decreased stability ( Table 2) . While the DUET server predicted 46 and 16 nonsynonymous mutations as destabilizing and stabilizing (Table 2 ).

In combination, structure-based online servers predicted 17 nonsynonymous mutations (L8V,   L8W, L18F, S71F, Y145H, M153T, F157S, S221L, S221W, S247R, G476S, L611F, A831V, A852V, A879S, C1247F, and C1254F) with decreased stability, and 7 nonsynonymous mutations (H49Y, S50L, D215H, N501Y, D614G, A845V, and P1143L) with increased stability in the S-protein (Table 2) 

The HADDOCK tool and HawkDock Server was used to examine the binding energy between the native and mutant S-proteins [destabilizing (L8V, L8W, L18F, Y145H, M153T, F157S, G476S, L611F, A879S, C1247F, and C1254F) and stabilizing (H49Y, S50L, N501Y, D614G, A845V, and P1143L)] with the ACE2 receptor. The HADDOCK score must be computed in order to understand the interaction between biological partners. During docking, each structure is assigned a HADDOCK score, which allows the structures to be classified. The score is a sum of the intermolecular AIR energies, buried surface area (BSA) Electrostatic, Van der Waal, and desolvation (Dsolv) energies [70] [71] [72] .

The Haddock scores of the native S-protein-ACE2 complex, and destabilizing S-proteins of S- (Table 3a) , and the stabilizing nonsynonymous mutations of S-protein-ACE2 complexes exhibit a higher BSA score between the range of 2119.3 ± 118.2, and 2278.9 ± 100.4 (Table 3b ), compared to the native complex. We applied the MM_GBSA approach to evaluate the binding free energy (ddG) between the native and mutants S-protein and ACE2 receptor molecules to verify this further. The MM_GBSA score (ddg) of native complex and destabilizing mutation complexes were -60. (Table 4b) . Further, the interaction between the native and mutant S-protein and ACE2 receptor molecules is shown in Fig. 5a 

Therefore, the no. of H-bonds was calculated for the native, destabilizing, and stabilizing nonsynonymous mutations of S-protein-ACE2 complexes, and the values are depicted in Fig. 6 and Table 4a 

The recent studies reported that N501Y and d614G stabilized nonsynonymous mutations are the most promising in S-protein [73] [74] [75] [76] . Our analysis confirmed that both the nonsynonymous mutations (N501Y and d614G) stabilized and showed better interaction with ACE2. These results motivated us to observe the structural changes of native and these two advantageous nonsynonymous mutations (N501Y and d614G) at the atomic level. Hence, we implemented the MD simulation approach to investigate how the structural transition in the mutant (N501Y and d614G) S-proteins influencing the interaction with ACE2. We analyzed the total energy, Root Table 5 .

To investigate the convergence of the native and mutant (N501Y and D614G) protein systems, the total energy was measured and displayed in Fig. 8a . The native and mutant (N501Y and D614G) S-protein systems show the convergence from the beginning to the end of the simulation. To investigate the stability of the native and mutant (N501Y and D614G) protein system, the RMSD matrix for all Cα-atoms from the initial structure was measured (Fig. 8b ). Fig.   8b shows that the RMSD value of the native and mutant structures (N501Y and D614G) is 2.73 nm, 2.88 nm, and 3.01 nm, respectively (Table 5) . Furthermore, the RMSF value analysis revealed a significant difference in the fluctuation of residues between the native and mutant (N501Y and D614G) S-proteins (Fig. 8c) .

The Mutants N501Y and D614G structures have a greater degree of fluctuation in the residue of 501 and 614 along with neighboring residues than native S-protein throughout the simulation and shown in Fig. 8c . By analyzing the solvent-accessible surface area (SASA) Plots, we determined the geometry and surface of native and mutant (N501Y and D614G) S-proteins. The changes of SASA of native and mutant (N501Y and D614G) S-protein over time are depicted in Fig. 8d . In   Fig. 8d , from the beginning to the end of the simulation, the N501Y and D614G mutants have higher fluctuation in the SASA values compared to native structure (Fig. 8d) .

The protein folding, stability, and function are all dependent on the hydrogen bond. To better understand the stability of native and mutant (N501Y and D614G) S-proteins, we measured the J o u r n a l P r e -p r o o f intramolecular H-bond concerning time (Fig. 8e) . From the beginning to the end of the simulation, both the mutants show higher numbers of h-bonds than the native structure (Fig. 8e) .

Further, we performed PCA analysis to attain the motion of S-protein upon mutation. The projection of the first two eigenvectors in the PCA plot (Fig. 9a) shows that the structures of the mutants cover a broader region of phase space in both PC1 and PC2 planes than the native Sprotein, again indicating the expansion in the structures. The covariance value of native and mutant structures is listed in Table 5 . This further confirms the overall increased flexibility of the mutants (N501Y and D614G) compared to the native S-protein at 300K. Overall, the mutant structures (N501Y and D614G) exhibited more motion and flexibility than the native S-protein.

Nonsynonymous mutations in the S-protein may influence the stability of its structure and interaction with the ACE2 receptor, which provides clues for viral infection and disease spread.

Identifying the mutational effect on the S-protein and its interaction with the ACE2 receptor is very important for predicting the 3D conformation of the S-protein. Unfortunately, the entire length of the S-protein structure is not available in the protein data bank. We have used the earlier proved studies [33] [34] [35] [36] [37] [38] to fix this issue. Hence, we have preferred the I-TASSER online server to predict the 3D structure of the S-protein. Further refinement of predicted model Sprotein, the MDS performed to assess the stability of model structure for further studies. Fig. 3 shows that the RMSD of native S-protein is converged after 7 ns and produces stable conformations. Further, the average structure of the native s-protein was shown at regular intervals (Fig. 3) . The most favorable structure of native S-protein was selected at the 12 ns MD (Table 1 -2). We considered these 11 destabilizing and six stabilizing nonsynonymous mutations for further docking analysis to investigate the interaction behavior with the ACE2 receptor.

The screened nonsynonymous mutations in the S-protein may affect the stability of its structure and interaction with the ACE2 receptor, which provides clues for SARS-CoV-2 infection. (Table 3a & Fig. S1 ). On the other hand, the stabilizing nonsynonymous mutations of S-protein-ACE2 complexes have greater negative values as a HADDOCK score than the native S-protein-ACE2 complex and illustrate a better interaction between the biological partners (Table 3b Fig. 7b-e, & Fig. S2) . A more excellent BSA value facilitates an immediacy between the two molecules. The BSA, restraint and desolvation energy significantly associate with the HADDOCK docking score [70] [71] [72] . The S-protein loses its interaction with the ACE2 receptor upon destabilizing nonsynonymous mutations (L8V, L8W, L18F, Y145H, M153T, F157S, G476S, L611F, A879S, C1247F, and C1254F), whereas the stabilizing nonsynonymous mutations (H49Y, S50L, N501Y, D614G, A845V, and P1143L) in the S-protein shows increased interaction with the ACE2 receptor (Table 3a-b) We applied the MM_GBSA approach to evaluate the binding free energy (ddG) between the native and mutants S-protein and ACE2 receptor molecules to verify this further. From Table 4a, it is thus again clear that the destabilizing nonsynonymous mutations show less binding free energy than the native complex, and they lose their interaction with the ACE2 receptor. The stabilizing nonsynonymous mutations (H49Y, S50L, N501Y, D614G, A845V, and P1143L) of J o u r n a l P r e -p r o o f S-protein and ACE2 complexes show more binding free energy native complex (Table 4b) .

Hydrogen bonds are the essential interactions in biological identification processes and vital for establishing the binding specificity [57] [58] [59] [60] . The intermolecular hydrogen bonds can provide favorable binding energy [77, 78] . This principle is the destabilizing nonsynonymous mutations (Table 4b ). It is confirmed that all six stabilizing nonsynonymous mutations show better affinity with the ACE2 receptor compared to the native S-protein.

Further, MDS was performed using several parameters like total energy RMSD matrix, RMSF, SASA, and NH-bond to evaluate the conformational transitions of native and mutant S-proteins.

The total energy plot shows the convergence for the native and mutants S-protein system during the simulation and generates stable conformation, thus providing an appropriate foundation for further analysis (Fig. 8a) . In the RMSD matrix plot, the mutant (N501Y and D614G) structures showed higher deviation values, whereas the native structure showed lower deviation values (Table 5 , & Fig. 8b-d) . However, the stabilizing nonsynonymous mutations increase the interaction with ACE2. The MD simulation results confirm that the two advantageous stabilizing nonsynonymous mutations (N501Y and D614G) undergo the structural transitions and might be the reason for enhancing the interaction with ACE2. This confirms and provides evidence that was stabilizing nonsynonymous mutations have a high affinity with ACE2 and high virulent levels compared to native and destabilizing nonsynonymous mutations of the S-protein. There is some experimental evidence to corroborate our findings, which proves the better association between ACE2 and Sprotein upon stabilizing nonsynonymous mutations [79] [80] [81] [82] [83] [84] . Many recent studies reported that mainly the stabilizing nonsynonymous mutations N501Y and D614G have a better affinity with the ACE2 receptor [73-76; 79-84] . This result will help experimental scientists understand the mechanism of the S-protein and its interaction with the ACE2 receptor upon nonsynonymous mutations.

Using the vast bioinformatics approaches, we segregated and screened the destabilizing and ARG457,  LYS458, GLN474,  GLU484, CYS488,  TYR489, GLN493,  SER494, THR500,  GLY502   SER19, LYS31, GLU35,  ASP38, GLN42, LYS68,  GLU75, MET82, LYS353   Y145H-ACE2  -52.57  11  TYR449, TYR453,  GLU484, GLY485,  ARG457, TYR489,  GLN493, TYR505   THR27, HIS34, GLU35,  ASP38, LYS68, GLU75 -47.3  9  GLU406, LYS417,  TYR449, TYR453,  GLU484, THR500,  ASN501, GLY502   THR27, GLU35, ASP38,  TYR41, LYS353,  ASP355, ARG559   L611F-ACE2  Linker  -30.7  9  ARG403, GLU406,  LYS417, TYR453,  GLN474, ASN487,  TYR489, THR500,  TYR505   SER19, GLN24, ASP30 ARG457,  LYS458, GLN474,  GLU484, CYS488,  TYR489, GLN493,  SER494, THR500,  GLY502   SER19, LYS31, GLU35,  ASP38, GLN42, LYS68,  GLU75, MET82, LYS353   Y145H-ACE2  -52.57  11  TYR449, TYR453,  GLU484, GLY485,  ARG457, TYR489,  GLN493, TYR505   THR27, HIS34, GLU35,  ASP38, LYS68, GLU75 -47.3  9  GLU406, LYS417,  TYR449, TYR453,  GLU484, THR500,  ASN501, GLY502   THR27, GLU35, ASP38,  TYR41, LYS353,  ASP355, ARG559   L611F-ACE2  Linker  -30.7  9  ARG403, GLU406,  LYS417, TYR453,  GLN474, ASN487,  TYR489, THR500,  TYR505   SER19, GLN24, ASP30 

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Programs for checking the quality of protein structures solved by NMR

ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins

iStable: off-the-shelf predictor integration for predicting protein stability changes

0: predicting stability changes upon mutation from the protein sequence or structure

Prediction of protein stability changes for single site mutations using support vector machines

CUPSAT: prediction of protein stability upon point mutations

SDM: a server for predicting effects of mutations on protein stability

DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach

The HADDOCK2. 2 web server: user-friendly integrative modeling of biomolecular complexes

Computational biophysical characterization of the SARS-CoV-2 spike protein binding with the ACE2 receptor and implications for infectivity

Effect of novel leukemia mutations (K75E & E222K) on interferon regulatory factor 1 and its interaction with DNA: insights from molecular dynamics simulations and docking studies

Prioritization of SNPs in y+ LAT 1 culpable of Lysinuric protein intolerance and their mutational impacts using protein protein docking and molecular dynamics simulation studies

Structure and function of p53-DNA complexes with inactivation and rescue mutations: a molecular dynamics simulation study

Investigation of binding phenomenon of NSP3 and p130Cas mutants and their effect on cell signalling

HawkDock: a web server to predict and analyze the proteinprotein complex based on computational docking and MM/GBSA

Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations

Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set

Assessing the performance of the MM/PBSA and MM/GBSA methods. 6. Capability to predict protein-protein binding free energies and re-rank binding poses generated by protein-protein docking

LigPlot+: multiple ligand-protein interaction diagrams for drug discovery

The PyMOL molecular graphics system

CHARMM36m: an improved force field for folded and intrinsically disordered proteins

XMGRACE, Version 5.1. 19

Essential dynamics of proteins

Principles of docking: An overview of search algorithms and a guide to scoring functions

CAPRI: A critical assessment of predicted interactions

Satisfying hydrogen bonding potential in proteins

SARS-CoV-2 D614G spike mutation increases entry efficiency with enhanced ACE2-binding affinity

Higher infectivity of the SARS CoV 2 new variants is associated with K417N/T, E484K, and N501Y mutants: An insight from structural data

Tracking changes in SARS-CoV-2 Spike: evidence that D614G increases infectivity of the COVID-19 virus

The Spike D614G mutation increases SARS-CoV-2 infection of multiple human cell types

Evaluation of intrinsic binding energy from a hydrogen bonding group in an enzyme inhibitor

Evaluating electrostatic contributions to binding with the use of protein charge ladders

In silico investigation of critical binding pattern in SARS-CoV-2 spike protein with angiotensin-converting enzyme 2

Spike Protein/Angiotensin-Converting Enzyme 2 Binding Interface: Comparison with Experimental Evidence

The new SARS-CoV-2 strain shows a stronger binding affinity to ACE2 due to N501Y mutant

Enhanced binding of the N501Y mutated SARS CoV 2 spike protein to the human ACE2 receptor: insights from molecular dynamics simulations

Mutations in spike protein of SARS-CoV-2 modulate receptor binding, membrane fusion and immunogenicity: an Insight into viral tropism and pathogenesis of COVID-19

Structural and Dynamical Differences in the Spike Protein RBD in the SARS-CoV-2 Variants B. 1.1. 7 and B. 1.351

We would like to acknowledge the Bioinformatics Laboratory at College of Applied Medical Sciences in Jubail, Imam Abdulrahman Bin Faisal University, Jubail, 35816, Saudi Arabia, for their computing facility to carry out this work.

We have no conflicts of interest to disclose.J o u r n a l P r e -p r o o f