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Comprehensive deep mutational scanning reveals the pH induced stability and binding differences between SARS-CoV-2 spike RBD and human ACE2
Shafiul Haque, Darin Mansor Mathkor, Mustfa Faisal Alkhanani, Farkad Bantun, Aiman M. Momenah, Hani Faidah, Naif A. Jalal & Vijay Kumar
To cite this article: Shafiul Haque, Darin Mansor Mathkor, Mustfa Faisal Alkhanani, Farkad Bantun, Aiman M. Momenah, Hani Faidah, Naif A. Jalal & Vijay Kumar (2023): Comprehensive deep mutational scanning reveals the pH induced stability and binding differences between SARS-CoV-2 spike RBD and human ACE2, Journal of Biomolecular Structure and Dynamics, DOI:
10.1080/07391102.2023.2194007
To link to this article: https://doi.org/10.1080/07391102.2023.2194007
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Comprehensive deep mutational scanning reveals the pH induced stability and binding differences between SARS-CoV-2 spike RBD and human ACE2
Shafiul Haquea,b,c, Darin Mansor Mathkora, Mustfa Faisal Alkhananid, Farkad Bantune, Aiman M. Momenahe, Hani Faidahe, Naif A. Jalaleand Vijay Kumarf
aResearch and Scientific Studies Unit, College of Nursing and Allied Health Sciences, Jazan University, Jazan-45142, Saudi Arabia;bGilbert and Rose-Marie Chagoury School of Medicine, Lebanese American University, Beirut, Lebanon;cCentre of Medical and Bio-Allied Health Sciences Research, Ajman University, Ajman, United Arab Emirates;dBiology Department, College of Sciences, University of Hafr Al Batin, Hafr Al Batin, Saudi Arabia;eDepartment of Microbiology, Faculty of Medicine, Umm Al-Qura University, Makkah, Saudi Arabia;fAmity Institute of Neuropsychology & Neurosciences, Amity University, Noida, Uttar Pradesh, India
Communicated by Ramaswamy H. Sarma
ABSTRACT
The SARS-CoV-2 spike (S) glycoprotein with its mobile receptor-binding domain (RBD), binds to the human ACE2 receptor and thus facilitates virus entry through low-pH-endosomal pathways. The high degree of SARS-CoV-2 mutability has raised concern among scientists and medical professionals because it created doubt about the effectiveness of drugs and vaccinations designed specifically for COVID-19. In this study, we used computational saturation mutagenesis approach, including structure- based free energy calculations to analyse the effects of the missense mutations on the SARS-CoV-2 S- RBD stability and the S-RBD binding affinity with ACE2 at three different pH (pH 4.5, pH 6.5, and pH 7.4). A total of 3705 mutations in the S-RBD protein were analyzed, and we discovered that most of these mutations destabilize the RBD protein. Specifically, residues G404, G431, G447, A475, and G526 were important for RBD protein stability. In addition, RBD residues Y449, Y489, Y495, Q498, and N487 were critical for the RBD-ACE2 interaction. Next, we found that the distribution of the mean stability changes and mean binding energy changes of RBD due to mutations at both serological and endoso- mal pH correlated well, indicating the similar effects of mutations. Overall, this computational analysis is useful for understanding the effects of missense mutations in SARS-CoV-2 pathogenesis at differ- ent pH.
ARTICLE HISTORY Received 14 November 2022 Accepted 25 February 2023
KEYWORDS
SARS-CoV-2 S-RBD; missense mutations; saturation mutagenesis; stability; RBD- ACE2 binding affinity
1. Introduction
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV- 2) is a non-segmented, positive-sense single-stranded RNA virus of 30 kb genome size. The spike (S) glycoprotein is essential for its pathogenicity. This homotrimeric S protein facilitates the release and entry of the viral genome into host cells and mediates the attachment and binding of the virus to host cell receptors, ACE2 (Hoffmann et al.,2020; Lan et al., 2020; Walls et al., 2020; Q. Wang et al., 2020). The S protein is composed of two subunits: Subunit 1 (S1), which contains the ACE2 receptor-binding domain (RBD), and Subunit 2 (S2), which contributes to the fusion process (Hoffmann et al., 2020; Shang et al.,2020). The S protein from SARS-CoV-2 has a greater affinity for ACE2 compared with those of SARS-CoV (Walls et al., 2020) and bat coronavirus S (Tai et al., 2020).
The binding interface between RBD and ACE2 and the atomic features of the SARS-Cov-2 S proteins were both uncovered by structural analysis. The two common prefusion conformations for uncleaved and furin-cleaved SARS-CoV-2 spikes are: a single-up conformation and an all-down con- formation of RBD in the S1 subunit (Walls et al.,2020; Wrapp
et al.,2020; Wrobel et al., 2020). The ‘up’orientation of RBD is linked to the epitope accessibility of RBD-directed antibod- ies and is required for interaction with the ACE2 receptor.
Also, a post-fusion structure of the SARS-CoV-2 spike further revealed even more structural differences between prefusion and post-fusion conformations (Cai et al.,2020).
With respect to the reference Wuhan genome, SARS-CoV-2 has experienced more than 10,000 mutations (Wang et al.,2020).
The majority of the mutations associated with the different SARS-CoV-2 variants are known to have increased affinity for the ACE2 receptor and are crucial for the virus spread (Chan et al., 2020; Starr et al.,2020). These variants are also demonstrated to increase transmissibility (Davies et al., 2021), alter infectivity (Li et al.,2020; L. Zhang et al.,2020), ineffective towards neutralizing antibodies and escape from neutralization (Dejnirattisai et al., 2021; Mannar et al., 2022; Wibmer et al.,2021; Xie et al., 2021;
Xu et al., 2022; J. Zhang et al., 2021). The underlying reason for the increased binding affinity in these variants has not been linked to a sizable alteration in either the spike or ACE2 recep- tor’s three-dimensional structures. Therefore, it is important to study possible RBD mutations and their potential effects on
CONTACTVijay Kumar [email protected] Amity Institute of Neuropsychology & Neurosciences, Amity University, Noida, Uttar Pradesh 201303, India.
Supplemental data for this article can be accessed online athttps://doi.org/10.1080/07391102.2023.2194007.
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COVID-19 development and vaccination interactions. Accordingly, RBD mutations that can improve binding affinity with ACE2 and/or affect antibody neutralization have been extensively mapped through high-throughput mutational studies (C. Chen et al.,2021; J. Chen et al.,2020; Greaney et al.,2021; Starr et al., 2021). These investigations emphasise the significance of moni- toring single and multiple RBD mutations and their increased potential for ACE2 binding and/or immune evasion. The various computational techniques have been used in SARS-CoV-2 research and are easily able to estimate the consequences of harmful mutations on protein function and structure (Ahamad et al.,2022; Alam et al.,2021; Ali et al.,2022; C. Chen et al.,2021;
Gan et al.,2021; Laurini et al.,2021; Teng et al.,2009; Teng et al., 2021).
The interaction of RBD and ACE2, which can happen through serological pH as well as low-pH-endosomal routes, is what allows SARS-CoV-2 to infect receptive cells. Therefore, it is crucial to look into the RBD stability and RBD-ACE2 bind- ing affinity. According to this viewpoint, we apply the com- putational saturation mutagenesis to study the impacts of S- RBD mutations on the stability and binding free energy (BFE) of the RBD protein and the ACE2 at three different pH, namely 5.5, 6.5, and 7.4. Because SARS-CoV-2 variants exhibit different mutation patterns, it is critical to comprehend their mutation consequences. To this purpose, we have used com- putational saturation mutagenesis to examine 3705 RBD (residue 333–526 on S protein) mutations in experimentally established protein structures at three different pH levels (Q.
Wang et al., 2020; Zhou et al., 2020). This is, to the best of our knowledge, the first study to look at the mutation land- scape of the SARS-CoV-2 RBD region at different pH levels.
2. Methods
2.1. Structure preparation
All the three-dimensional structures are collected from Protein Data Bank (PDB) [16]. The cryo-EM structures of SARS-CoV-2 S in complex with single ACE2 at serological pH 7.4 (PDB ID: 7KNB) and at endosomal pH 5.5 (PDB ID: 7KNE) (Zhou et al., 2020), along with the crystal structure of RBD- ACE2 complex at pH 6.5 (PDB ID: 6LZG) (Q. Wang et al., 2020) was used for stability and interaction analysis.
2.2. Free energy and binding affinity calculations of S- RBD mutations
To determine the effect of mutations on protein’s stability and binding, we here applied saturation mutagenesis to mutate each residue in the complex structure to the other 19 amino acids. The mutation-induced change in the stability and bind- ing affinity of S-RBD was investigated using the FoldX tool (Schymkowitz et al.,2005). Foldx calculates free energy,DG, by incorporating the contributions of hydrophobic and polar groups to the solvation energy, Van der Waals, hydrogen bonding, and electrostatic interactions. These energy parame- ters were determined experimentally (Schymkowitz et al., 2005). FoldX tool is extensively utilized for computational
saturation mutagenesis studies (Cheng et al.,2012; Teng et al., 2021; Vedithi et al., 2020; Xue et al., 2022). Mutations in the protein complex structure were introduced using the BuildModel module of FoldX (Guerois et al.,2002). The folding energy change (DDG) between the mutant structure (mut) and wild-type (wt) structure was calculated using:
DDG¼DGðmutÞDGðwtÞ
A negativeDDG value indicates that the mutation leads to stabilization of the protein structure whereas, a positive value indicates that mutation leads to destabilization of the protein.
Next, the BFE change was computed by the’AnalyseComplex’ command and is mathematically represented as:
DDGbind¼DGbindðmutÞ–DGbindðwtÞ
A negative DDGbind value indicates that the mutation increased the binding energy and a positive value suggests that the mutation decreased the binding energy.
2.3. Heatmaps of mutational stability and binding energy
The changes in stability and binding energy for single muta- tions were used to generate heatmaps in Excel. The y-axis represented the mutation residues, while the x-axis repre- sented the amino acid types of single mutations. The stabil- ity or binding energy values of all mutations were colored where the blue color indicated the destabilization of the pro- tein, and the red color indicated the stabilization of the pro- tein. The values of stability or binding energies were represented as a gradient between these two color ranges.
2.4. Ligplot analyses
Further, the residual interactions in the three-dimensional com- plex structures were analysed through PDBSUM server (http://
www.ebi.ac.uk/pdbsum). The molecular interactions in each com- plex structure of the S-RBD protein with ACE2 were analyzed using DIMPLOT module of ligplotþ (Laskowski & Swindells, 2011).
3. Results
3.1. Mutations induced changes in the stability of SARS- CoV-2 S-RBD protein at different pH
To analyse the effects of the systematic mutations on SARS-CoV- 2 S-RBD stability, we generated 3705 mutations by mutating all 195 residues of RBD to all other 19 canonical amino acids and computed the folding energy changes (DDG) introduced by these mutations in monomeric S-RBD structures (Supplementary Tables 1–3). The structures used in the present study are (i) the crystal structure of the RBD-ACE2 complex at pH 6.5 (PDB ID:
6LZG) (Q. Wang et al., 2020), (ii) the cryo-EM structure of SARS- CoV-2 spike in complex with single ACE2 at pH 7.4 (PDB ID:
7KNB) (Zhou et al.,2020), and (iii) cryo-EM structure of SARS-CoV- 2 spike in complex with single ACE2 at pH 5.5 (PDB ID: 7KNE) (Zhou et al.,2020).
3.2. Stability changes of S-RBD protein at pH 6.5 (6LZG) The mutations induced free energy changes at pH 6.5 calcu- lated by FoldX are reported inFigure 1. Lower energy values indicate favorable mutations that stabilize the RBD and can improve the binding with the ACE2. Whereas, higher energy values indicate mutations that destabilize the RBD and pos- sibly affect the binding dynamics with the ACE2.
Figure 1(A) shows that out of 3705 missense mutations, 2434 (66%) mutations increased the free energy of the RBD by at least 0.5 kcal/mol whereas, 288 (8%) mutations decreased the free energy of the RBD by at most 0.5 kcal/mol, and 983 (26%) exhibited neutral effect on the stability of the RBD. The Foldx suite has a standard error of energy computation of 0.5 kcal/mol (Schymkowitz et al., 2005). As a result, the folding energy changes within the range (–0.5 < DDG <0.5) are insignificant or classified as neutral. Specifically, 1311 (35%) mutations highly destabil- ize the RBD at pH 6.5 (DDG > 2.5 kcal/mol), and 1123 (30%) mutations moderately destabilize the RBD (0.5 <
DDG 2.5 kcal/mol). Only 12 (0.32%) mutations had a highly stabilizing effect (DDG<–2.5 kcal/mol), and 276 (7%) moderately stabilize the RBD (–2.5< DDG 0.5 kcal/mol).
A total of 983 (27%) mutations had a neutral effect (–0.5<
DDG0.5 kcal/mol) on RBD stability.
The line chart depicts the mutation distribution along the whole length of the RBD (Figure 1B). The positive mean val- ues are represented by the upward lines, while the negative mean values are represented by the downward lines. As illus- trated in Figure 1(B), the mean value of DDG ranges from þ25.68 kcal/mol in G431 to 1.71 kcal/mol in S514, which lies in the S2 subunit of the S protein. Based on the mean value of DDG, mutations at V401, G431, P507, and G526 showed the greatest destabilizing effects (Figure 1B and Table 1). The three most destabilizing missense mutations are V401W (45.63 kcal/mol), G431W (45.34 kcal/mol), P507W (42.45 kcal/mol), and G526H (29.69 kcal/mol). Also, mutations with the highest stabilizing effects are found in residues S375, T385, G504, and S514. The most stabilizing missense mutations are S399L (–2.68 kcal/mol), A397L (–3.90 kcal/mol), and S514F (–3.87 kcal/mol) (Figure 1BandTable 1).
3.3. Stability changes of S-RBD protein at serological pH, 7.4 (7KNB)
The RBD mutations stability analysis at pH 7.4 showed that out of 3705 mutations, 758 (20.4%) mutations showed strong effects (DDG >2.5 kcal/mol) and 1250 (33.8%) mutations showed moderate effects (0.5< DDG 2.5 kcal/mol) on RBD destabilization (Figure 2A). In contrast, only 18 (0.48%) muta- tions have strong effects (DDG <2.5 kcal/mol) and 433 (11.68%) mutations have moderate effects (2.5 DDG
<0.5 kcal/mol) on RBD stabilizing. Also, 1146 (31.93%) mutations had a neutral effect on the stability changes (–0.5
<DDG0.5 kcal/mol).
Next, the distribution ofDDG mean of mutations in each pos- ition is shown in Figure 2(B). As shown, the DDG value range from þ10.26 kcal/mol in G431 to 1.36 kcal/mol in T523. The mutations at glycine residues G404, G416, G431, G447, and G526 have maximum destabilizing effects whereas, the mutations at residues T376, A397, S494, T500, S514, and T523 have maximum stabilizing effects (Figure 2B, Table 1). Among all mutations destabilizing the RBD protein, G431F has introduced the largest positive DDG (30.29 kcal/mol) while, G431W has 22.03 kcal/mol.
The mutations showing the maximum stabilizing effect were A397Y (3.95 kcal/mol), D398I (4.19 kcal/mol), and T500Y (3.57 kcal/mol) (Figure 2B,Table 1).
3.4. Stability changes of S-RBD protein at endosomal pH, 5.5 (7KNE)
Similarly, at pH 5.5, 2008 (54%) of the mutations destabilized the RBD structure whereas, 476 (13%) of the missense muta- tions stabilized the structure. The number of mutations showed neutral changes in the stability at pH 5.5 increased (1221, 33%) when compared to the other two RBD struc- tures. Figure 3(A)displays the pie chart of the effects of the missense mutations on the stability of RBD at pH 5.5. At pH 5.5, 758 (21%) mutations were highly destabilized (DDG >
2.5 kcal/mol), and 1250 (33%) mutations were moderately destabilized (0.5 < DDG 2.5 kcal/mol). Whereas, only 12 (0.3%) mutations exert a highly stabilizing effect (DDG
Figure 1. Effects of missense mutations on RBD stability at pH 6.5. (A) Pie charts summarize the contribution of mutations on stability changes in RBD at pH 6.5.
(B) Line chart showing meanDDG values of destabilizing mutations (positive values depicted as upward lines) and stabilizing mutations (negative values depicted as downward lines).
<–2.5 kcal/mol), and 464 (13%) mutations have moderately stabilized the RBD (–2.5<DDG 0.5 kcal/mol).
The distribution of DDG mean of mutations ranges from þ8.49 kcal/mol in G431 to 1.14 kcal/mol in S399 as shown in Figure 3(B). The mutations at residues G404, G431, S438, A475, C480, C488, and G526 have maximum destabilizing effects whereas, the mutations at residues A363, A397, S399, and Y495 have maximum stabilizing effects (Figure 3B,Table 1). Among all destabilizing mutations, S348Y has introduced the largest positive DDG (36.64 kcal/mol) while, A475W has 33.31 kcal/mol. The mutations showing maximum stabilizing effect are A397M (3.55 kcal/mol), D442Y (3.62 kcal/mol), and Y495I (3.37 kcal/mol).
3.5. Comparison of effects of mutation on stability of RBD in RBD-ACE2 complex structures at
different pH
We compared the mutational effects on the stability change of RBD in the RBD-ACE2 complex at three different pH. Five glycine residues (G404, G416, G526, G431, and G447) showed the highest mean destabilizing effects along with N422.
Whereas, the residues G339, G504, S514, T500, and T523 showed the highest mean stabilizing effects. Interestingly,
the distribution of the effect of all missense mutations in the RBD mean stability changes at pH 6.5 and pH 7.4 were highly correlated (R2 ¼ 0.6193) (Supplementary Figure 1A).
Whereas, the distribution of the effect of missense mutations in the RBD mean stability at pH 6.5 and pH 5.5 did not sig- nificantly correlated (R2¼0.4366) (Supplementary Figure 1B).
Moreover, the distribution of the mean stability changes due to mutations at pH 5.5 and pH 7.5 corelated well with R2 ¼ 0.6884 (Supplementary Figure 1C).
3.6. Stability changes of S-RBD interfacial variants at different pH
Further, the residual interactions in the three-dimensional complex structures were analysed through PDBSUM server (http://www.ebi.ac.uk/pdbsum). The common residues in the S-RBD interface in all the three complex structures are K417, G446, Y449, Y453, L455, F456, A475, G476, F486, N487, Y489, F490, Q493, S494, G496, Q498, T500, N501, G502, and Y505 (Supplementary Figure 2A–C).
The binding energy and dissociation constant (Kd) of the RBD-ACE2 complex structure, calculated using PRODIGY (PROtein binDIng enerGY prediction) tool (https://bianca.sci- ence.uu.nl/prodigy/) were found as 12.4 (kcal/mol) and
Table 1. Important residue and mutations based on stability changes of SARS-CoV-2 S-RBD.
Residues DDG (Mean) Mutation with maximumDDG Mutation with minimumDDG
SARS-CoV-2 RBD-ACE2 complex at pH 6.5 (PDB ID: 6LZG)
C336 9.91 V350W 27.62 A397L 3.90
V401 10.25 A363W 30.43 S399L 2.68
G431 25.68 V401W 45.63 Q493F 2.50
D442 8.82 N422W 33.58 Q498L 2.43
P507 14.24 G431W 45.34 Q498M 2.10
G526 16.98 G431Y 42.85 S514Y 3.55
S375 0.56 G431H 38.76 S514F 3.87
T385 0.81 N501W 20.04 S514L 3.01
N394 0.43 P507W 42.45 S514M 2.83
G504 0.68 G526W 29.69
S514 1.72
SARS-CoV-2 RBD-ACE2 complex at pH 7.4 (PDB ID: 7KNB)
C391 6.33 N422W 25.21 T393P 3.54
G404 7.75 G431W 22.20 A397Y 3.95
N422 5.22 G431Y 28.59 A397I 3.80
G431 10.26 G431F 30.29 A397L 3.45
G447 7.62 S438W 23.35 A397M 3.67
G526 5.27 S438Y 21.00 D398I 4.19
T376 0.82 A475W 26.88 D398S 3.33
A397 0.76 T500Y 3.57
T500 1.22 S514M 2.84
G504 0.79 S514L 2.30
S514 1.09
T523 1.369
SARS-CoV-2 RBD-ACE2 complex at pH 5.5 (PDB ID: 7KNE)
G404 6.82 N422W 22.64 T393P 2.31
G416 5.36 G431H 25.66 A397W 3.14
G431 8.49 G431I 12.04 A397I 3.62
S438 7.45 S438W 29.32 A397M 3.55
G447 5.30 S438Y 36.64 A397L 3.01
A475 7.07 S438F 28.66 S399L 2.41
G502 6.50 A475W 33.31 D442Y 3.62
T333 0.59 A475Y 26.44 D442F 2.10
T376 0.75 N501W 18.09 R454F 2.47
S399 1.14 Y495I 3.37
N439 0.71 Y495M 3.32
T500 0.59 S514M 2.56
T523 1.02
Note: The residues or mutations with the maximum or minimum stability changes,DDG mean andDDG values at three different pH are shown asboldfonts.
8.31010 for 6LZG, 13.1 (kcal/mol) and 2.51010 for 7KNB, and 12.2 (kcal/mol) and 1.1109 for 7KNE. The smaller value of Kd signifies the stronger binding (i.e. the greater affinity) (L. C. Xue et al.,2016).
Next, the RBD interfacial mutants induced stability changes were shown as a heatmap (Figure 4), where the y-axis represents the interfacial residues and the x-axis represent the mutations. The computed stability values were coloured with a gradient ranging from blue (stabi- lized binding) to red (destabilized binding). Figure 4 depicts the different results of single-point mutations and highlights those that are unstable. For some residues, the presence of a mutation has no effect on the stability. Most mutations at other locations will decrease protein stability.
Interestingly, mutations to Trp, Tyr, Phe or His for some of the RBD residues generally improve structural stability.
The results revealed that mutations at K417, N487, Q493, S494, Q498, and T500 were mostly destabilizing in all the three structures (Figure 4A–C). Moreover, the observation of the mutations induced stability changes indicated ana- lytic similarity in stability analysis in the three complex structures (Supplementary Figure S3).
3.6.1. Mutations induced changes in binding affinity of SARS-CoV-2 S-RBD protein to ACE2 at different pH Missense mutations in the RBD region could alter the key inter- action site and influence RBD-ACE2 binding affinity. The binding free energy (BFE) change due to mutation (DDGbind) is repre- sented as,DDGbind¼DGmutDGwt, whereDGwtis the wild-type BFE andDGmutis the mutant BFE. A positive BFE change suggests that the mutation decreases the free energy of the binding, and negative BFE suggests an increase in the binding affinity of the complex, making the virus more infective. We calculated the BFE changes (DDGbind) of total 3705 RBD mutations in the RBD-ACE2 complex and classified them into one of five categories according to theirDDGbind(Supplementary Tables 4–6).
3.7. Binding affinity changes of S-RBD protein in RBD- ACE2 complex structures at pH 6.5 (6LZG)
The majority of mutations result in minor changes in binding free energies, but some result in large changes. Of 3705 RBD mutations, 747 (20%) mutations reduced the binding affinity with positive BFE changes, 1130 (30.5%) mutations stabilized the binding with negative BFE changes while 1828 (49.33%)
Figure 2. Effects of missense mutations on RBD stability at serological pH 7.4. (A) Pie charts summarize the contribution of mutations on stability changes in RBD at pH 7.4. (B) Line chart summarizing the stability changes forDDG mean of residues. The destabilizing mutations are depicted as positive values and stabilizing mutations are depicted as negative values.
Figure 3. Effects of missense mutations on RBD stability at endosomal pH 4.5. (A) Pie charts summarize the contribution of mutations on stability changes in RBD at pH 4.5. (B) Line chart summarizing the stability changes forDDG mean of residues. The destabilizing mutations are depicted as positive values and stabilizing mutations are depicted as negative values.
mutations had a neutral effect (Supplementary Table 4).
Among 3705 mutations, 20 (0.53%) mutations strongly reduced (DDGbind2.5 kcal/mol) the binding affinity of RBD- ACE2 complex, while 69 (1.86%) mutations moderately decreased the binding affinity (0.5< DDGbind <2.5 kcal/mol).
The results also indicated that out of 3705 stabilizing muta- tions, 46 (1.24%) mutations strongly increased the binding affinity with DDGbind <2.5 kcal/mol, whereas 288 (7.77%) mutations showed moderate effects on stabilizing (2.5 DDGbind 0.5 kcal/mol) the complex (Figure 5A). Also, 796 (21.48%) mutations showed minimal effects on BFE changes (-0.5<DDGbind0.5 kcal/mol) of the complex.
The residues, A475 and G502 has the maximum DDGbind mean in RBD-ACE2 of 3.85 kcal/mol and 2.67 kcal/mol,
respectively (Table 2). The mutations A475F, A475H and N501W showed the largest binding energy change in all RBD mutations (Table 2). Interestingly, G502 neighboring residues Q493 and Q498 has minimum DDGbind mean at 2.66 kcal/mol, 2.11 kcal/mol, respectively. These three resi- dues lie in a 12-residue sequence motif (L492-G504) that has significant effects on RBD-ACE2 binding affinity. Many muta- tions in this motif significantly affects the binding as shown previously also (Teng et al., 2021). For example, the largest negative BFE, i.e. stabilizing effect is introduced by Q498M, Q493K, and Y495E of 4.37 kcal/mol, 4.35 kcal/mol, and 4.10 kcal/mol. In contrast, N501W in this motif (DDGbind¼ 14.39 kcal/mol) has the highest destabilizing effect on RBD- ACE2 complex (Table 2).
Figure 4. The effect of SARS-CoV-2 S-RBD interfacial mutations on protein stability. Heatmap shows the stability changes,DDG of all interfacial mutations of RBD at pH (A) 6.5, (B) 7.4, and (C) 4.5. The boxes of each mutation were colored with the gradient of a range between blue (destabilized) and red (stabilized) along with theirDDG values.
3.8. Binding affinity changes of S-RBD protein in RBD- ACE2 complex structures at serological pH 7.5 (7KNB)
We next examined the effect of RBD mutations on the binding affinity changes in RBD-ACE2 complex at pH 7.4 (Supplementary
Table 5). Out of 3705 mutations, 742 (20%) mutations reduced the binding affinity with positive BFE changes, whereas 1319 (35.6%) mutations stabilized the binding with negative BFE changes. Most of the mutations, 1644 (44%) exert neutral effects on binding affinity changes. Among 742 mutations, only 8 (0.21%) mutations strongly reduced (DDGbind2.5 kcal/mol) the
Table 2. Important residue and mutations based on RBD–ACE2 interaction BFE (DDGbind) changes.
Residue DDGbind (Mean) Mutation with maximumDDGbind Mutation with minimumDDGbind SARS-CoV-2 RBD-ACE2 complex at pH 6.5 (PDB ID: 6LZG)
R454 0.24 A475F 19.93 E484P 3.75
L455 0.21 A475R 8.02 N487D 3.73
A475 3.85 A475Y 8.38 Q493F 4.11
G496 1.22 A475W 9.04 Q493K 4.35
G502 2.67 A475H 13.05 Q493M 4.12
G476 1.83 G496F 7.66 Q493R 4.03
E484 2.41 G496Y 7.96 Q493W 3.93
N487 1.62 N501W 14.33 Y495I 4.01
F490 0.88 G502P 11.35 Y495E 4.10
Q493 2.66 Q498M 4.37
Y495 0.98 Q498V 3.75
Q498 2.11 N501Q 3.78
SARS-CoV-2 RBD-ACE2 complex at pH 7.4 (PDB ID: 7KNB)
T376 0.20 L455Y 4.57 G476D 4.99
V407 0.29 L455W 9.04 G476R 6.57
A435 0.25 A475W 22.58 N487R 5.84
L455 0.75 G502F 6.21 S494Y 4.98
G502 1.62 G502H 5.33 G496S 5.07
Y449 3.35 G502P 4.09 Q498D 5.18
G476 3.55 G502W 4.39 Q498E 4.99
G485 2.20 G502Y 5.15 Q498T 5.45
N487 3.04 Q498V 6.88
Y489 2.60 N501G 5.44
S494 2.70
G496 2.75
Q498 3.53
SARS-CoV-2 RBD-ACE2 complex at pH 5.5 (PDB ID: 7KNE)
A475 2.58 L455H 5.05 Q498F 7.53
G502 3.00 A475Y 6.77 Q498Y 7.15
G447 3.28 G502P 7.76 G496H 6.52
R403 2.49 A475F 11.83 N501V 6.25
Y449 2.08 A475H 16.05 G447R 6.15
G476 2.06 A475W 25.31 G447M 5.67
Y495 2.44 G447W 5.60
G496 2.81 N501E 5.53
Q498 3.37 Q498V 5.45
N501 3.09 N501S 5.39
G504 2.48 Q493K 5.24
Q498W 5.09
Y495E 5.06
Note: The residues or mutations with the maximum or minimum binding affinity changes,DDGbindmean andDDGbindvalues at three dif- ferent pH are shown asboldfonts.
Figure 5. The effect of RBD mutations on ACE2 binding affinity. Pie charts summarize the contribution of mutations on binding affinity changes in RBD at (A) pH 6.5, (B) pH 7.4, and (C) pH 4.5.
binding affinity of RBD-ACE2 complex, while 29 (0.78%) muta- tions moderately decreased the binding affinity (0.5< DDGbind
<2.5 kcal/mol). Also, 705 (19%) mutations exert little effect on decrease in the binding affinity (DDGbind <0.5 kcal/mol). The results also indicated that out of 1319 mutations, 150 (4.04%) mutations strongly increased the binding affinity with DDGbind
<2.5 kcal/mol, whereas 365 (9.85%) mutations moderately affect the binding affinity of the complex (2.5 DDGbind
0.5 kcal/mol) (Figure 5B). Also, 804 (21.7%) mutations have minimal effects on BFE changes of the complex (-0.5< DDGbind 0.5 kcal/mol).
The mutations at RBD residues V407, A435, L455, and G502 exhibit destabilizing effects on RBD-ACE2 binding affinity with DDGbind mean of 0.29, 0.25, 0.75 and 1.62 kcal/mol, respect- ively (Table 2). The mutations A475W, L455W, G502W, and G502F had maximum effects on destabilizing the complex.
Whereas, the residues Y449, G476, N487, G496, and Q498 exert maximum stabilizing effect to the complex withDDGbindmean of 3.35, 3.55, 3.04, 2.75, and 3.53 kcal/mol, respect- ively. The mutations Q489V, G476R, N487R, and N501G exhib- ited maximum stabilizing effect withDDGbindmean of6.88, 6.57,5.84, and5.44 kcal/mol, respectively.
Figure 6.Effects of RBD interfacial mutations on ACE2 binding interaction. Heatmap shows the binding free energy changes,DDGbindof all interfacial mutations of RBD at pH (A) 6.5, (B) 7.4, and (C) 4.5. The mutations stabilizing (red) and destabilizing (blue) the binding interactions are shown in the heatmaps along with theirDDGbindvalues.
3.9. Binding affinity changes of S-RBD protein in RBD- ACE2 complex structures at endosomal pH 5.5 (7KNE)
Similarly, for the RBD-ACE2 complex at acidic pH 5.5, 18 mutations showed large-decrease in the BFE of the complex, 25 mutations moderately decrease the BFE, 1895 mutations showed neutral effect, while 343 mutations showed moder- ate increase in BEF, and 131 mutations showed a large- increase in the BFE of the complex (Figure 5C).
Also, similar to 6LZG structure, A475 and G502 have the maximum DDGbind mean of 2.58 kcal/mol and 3.02 kcal/mol, respectively (Table 2). The mutations A475W, A475H, A475F, G502P and L455H displayed the largest binding energy change in all RBD mutations (Table 2). Whereas, the residues G447, N487, Q493, G496, Q498 and N501 exert maximum stabilizing effect to the complex. The mutations Q489F, G496H, N501V, G447R, Q493K, and Y495E exhibited max- imum stabilizing effect (Table 2).
3.10. Comparison of effects of mutations on binding affinities of RBD to ACE2 in RBD-ACE2 structures at different pH
We compared the mutational effects of RBD binding on RBD- ACE2 complexes structure at three different pH. Three resi- dues (L455, A475, and G502) showed the highest mean destabilizing binding affinities, while six residues (N487, Q493, Y495, G496, Q498, and N501) showed the largest mean stabilizing binding effects. Moreover, the distribution of the effect of mutations in the BFE changes at pH 6.5 and pH 7.4 did not show any correlation (R2 ¼ 0.08) (Supplementary Figure 4A). The distribution of the effect of mutations in the RBD binding affinity at pH 6.5 and pH 5.5 correlated with R2¼0.26 (Supplementary Figure 4B) whereas BFE changes due to mutations at pH 5.5 and pH 7.5 core- lated with R2¼0.32 (Supplementary Figure 4C).
3.11. Binding changes of S-RBD interfacial variants at different pH
Figure 6 maps the changes in binding energy upon muta- tions and exposes unstable ones. Observation of heat maps of binding energy of individual mutations revealed similarity in binding analysis in all the three complex structures. The results revealed that mutations at G476, N487, Y489, G496, and Q498 mostly increased the binding to ACE2 in all the three complex structures (Figure 6A–C and Table 2). In add- ition, the mutations in the residues Y449, T500, and N501 mostly favor the binding to ACE2 at serological and endoso- mal pH (Figure 6B and 6C). The mutations at residues K417, G476, and G502 were frequently shown to disrupt the ACE2 binding. Interestingly, the amino acids changes to Asn, Gln, Cys, Gly, or Ala of the RBD interfacial residues generally reduces the binding to ACE2.
Moreover, the observation of the mutation induced stabil- ity changes indicated analytic similarity in stability analysis in the three complex structures (Supplementary Figure S5).
4. Discussion
The SARS-CoV-2 spike protein is one of the primary targets for vaccine development and neutralising antibodies, and many have acquired immune evasion mechanisms, some of which match characteristics of the recently revealed endoso- mal pH-dependent conformational masking (Zhou et al., 2020). The stability of the S protein is decisive for the speedy transmissions of infection (Moreira et al.,2020) and is critical in producing therapeutic drugs and vaccines (Kyriakidis et al., 2021).
Since December 2020, five rapidly spreading strains of SARS-CoV-2 have emerged, including the Alpha (B.1.1.7) vari- ant, the Beta (B.1.351) variant, the Gamma (P.1) variant, the Delta (B.1.617.2) variant, and the Omicron (B.1.1.529) variant (Fan Wu et al., 2020; Wu et al., 2020). These variants have many mutations in the spike protein which facilitates viral entry through the hACE2 receptor.
Charles et al. (Charles et al., 2023) recently used ITC to compare the binding affinities of these variants to hACE2.
For each variant, the KD values ranged from 2 nM for Alpha- RBD to 6.0 nM for the Delta variant. In comparison to the ori- ginal Wuhan strain (WHCV), all of the variants demonstrated tighter binding to the hACE2 receptor. The trend of binding affinity was as follows:
Alpha>GammaOmicron>Beta>Delta>WHCV This study along with many others have indicated that all of the variants had a stronger affinity for hACE2 than WHCV (Ali et al., 2022; Tian et al., 2021; X. Xue et al., 2021). This suggests that mutations that promote tighter binding could be a contributing factor in the observed difference in transmissibility.
To understand how mutations affect the stability of S-RBD protein at different pH, we predicted the stability changes of RBD protein at three different pH. Our prediction showed that at both serological (pH 7.4) and endosomal pH (pH 5.5), half of the RBD mutations (54–56%) destabilize the RBD.
Whereas, at pH 6.5, more than half of the S-RBD mutations (66%) are destabilizing. Majority of these destabilizing mutations included the substitution of Gly, Val and Ala resi- dues, which are hydrophobic amino acids with longer hydro- phobic side chains. Prediction of the effects of mutations on the stability of RBD at different pH revealed nearly identical results. The top five amino acid residues with the highest average destabilizing effect were G404, G431, G447, A475, and G526 (Table 1). This study also showed a significant cor- relation in the effects of mutations on the RBD stability at both serological and endosomal pH, suggesting that a muta- tion in the RBD will have a similar effect on the stability and folding of the RBD regardless of the different pH values.
The binding of the S-RBD to human ACE2 allows SARS- CoV-2 to enter the human cells (Hoffmann et al., 2020). Our results showed that most of the RBD mutations have small BFE changes, while some of them showed large BFE changes.
Almost 20% of the RBD mutations decreased the binding affinity of the RBD protein to human ACE2. Whereas, 30%- 35% mutations increased the binding affinity, leading to more infectious SARS-CoV-2. It is worthy to note that
residues Y449, Y489. Y495, Q498, and N487 are potential hot spots and will increase the S-RBD-ACE2 binding (Table 2).
Interestingly, at pH 5.5 and 7.5, the effects of mutations on S-RBD binding affinity to hACE2 are somewhat corelated.
5. Conclusion
The infectivity of SARS-CoV-2 is a critical factor for preventive measurements against COVID-19. However, determining the viral infectivity of all SARS-CoV-2 variants experimentally is extremely difficult. The continuous evolution of SARS-CoV-2 worsens these issues. We investigated the potential hotspots of SARS CoV-2 RBD that can destabilise and decrease ACE2 binding at both endosomal and serological pH in this work.
Computational analysis revealed that the interfacial RBD resi- dues K417, N487, Q493, S494, Q498, and T500 are hotspots because their variations can significantly disrupt the RBD protein. Notably, different studies also supported the K417, N487, Q493, and S494 as hotspots. The mutations at residues K417, G476, and G502 were frequently seen to affect ACE2 binding.
In conclusion, our findings shed light on the putative effects of potential hotspots on interactions with ACE2 at dif- ferent pH levels, which can aid in the development of new therapeutic drugs against potential SARS-CoV-2 variants.
Acknowledgements
The authors extend their appreciation to the Deputyship for Research &
Innovation, Ministry of Education in Saudi Arabia for funding this research work.
Disclosure statement
The authors declare that there is no conflict of interest.
Consent for publication
All authors consent for the publication of this study.
Funding
Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia through the Project Number RUP3-7.
ORCID
Vijay Kumar http://orcid.org/0000-0002-3621-5025
Authors’ contributions
Conceptualization, S.H., D.M.M., and V.K.; methodology, S.H., D.M.M., M.F.A., F.B., and V.K.; software, A.M.M, and H.F.; formal analysis, F.B., A.M.M, H.F., and N.A.J; data curation, S.H., D.M.M., and V.K.; writing—ori- ginal draft preparation, S.H., D.M.M., F.B., and N.A.J.; writing—review and editing, all authors; supervision, A.M.M., N.A.J., and V.K. All authors con- tributed to the article and have approved the submitted version.
Data availability statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.
References
Ahamad, S., Hema, K., Ahmad, S., Kumar, V., & Gupta, D. (2022). Insights into the structure and dynamics of SARS-CoV-2 spike glycoprotein double mutant L452R-E484Q. 3 Biotech, 12(4), 87. https://doi.org/10.
1007/s13205-022-03151-0[pii]
Alam, I., Radovanovic, A., Incitti, R., Kamau, A. A., Alarawi, M., Azhar, E. I.,
& Gojobori, T. (2021). CovMT: An interactive SARS-CoV-2 mutation tracker, with a focus on critical variants. Lancet Infectious Diseases.
21(5), 602.https://doi.org/10.1016/S1473-3099(21)00078-5
Ali, N., Khan, R., AlAsmari, A. F., & Kumar, V. (2022). In silico investiga- tions of heparin binding to SARS-CoV-2 variants with a focus at the RBD/ACE2 interface. Process Biochemistry (Barking, London, England), 115, 70–79. https://doi.org/10.1016/j.procbio.2022.02.012 S1359- 5113(22)00056-3 [pii]
Cai, Y., Zhang, J., Xiao, T., Peng, H., Sterling, S. M., Walsh, R. M., Rawson, S., Rits-Volloch, S., & Chen, B. (2020). Distinct conformational states of SARS-CoV-2 spike protein.Science (New York, N.Y.), 369(6511), 1586– 1592. https://doi.org/10.1126/science.abd4251[Mismatch] science.
abd4251 [pii]
Chan, K. K., Dorosky, D., Sharma, P., Abbasi, S. A., Dye, J. M., Kranz, D. M., Herbert, A. S., & Procko, E. (2020). Engineering human ACE2 to opti- mize binding to the spike protein of SARS coronavirus 2.Science (New York, N.Y.), 369(6508), 1261–1265. https://doi.org/10.1126/science.
abc0870
Charles, J., McCann, N., Ploplis, V. A., & Castellino, F. J. (2023). Spike pro- tein receptor-binding domains from SARS-CoV-2 variants of interest bind human ACE2 more tightly than the prototype spike protein.
Biochemical and Biophysical Research Communications, 641, 61–66.
https://doi.org/10.1016/j.bbrc.2022.12.011
Chen, C., Boorla, V. S., Banerjee, D., Chowdhury, R., Cavener, V. S., Nissly, R. H., Gontu, A., Boyle, N. R., Vandegrift, K., Nair, M. S., Kuchipudi, S. V., & Maranas, C. D. (2021). Computational prediction of the effect of amino acid changes on the binding affinity between SARS-CoV-2 spike RBD and human ACE2.Proceedings of the National Academy of Sciences, 118(42), e2106480118. https://doi.org/10.1073/pnas.
2106480118
Chen, J., Wang, R., Wang, M., & Wei, G. W. (2020). Mutations strength- ened SARS-CoV-2 infectivity. Journal of Molecular Biology, 432(19), 5212–5226.https://doi.org/10.1016/j.jmb.2020.07.009
Cheng, T. M., Goehring, L., Jeffery, L., Lu, Y. E., Hayles, J., Novak, B., &
Bates, P. A. (2012). A structural systems biology approach for quantify- ing the systemic consequences of missense mutations in proteins.
PLoS Computational Biology, 8(10), e1002738.https://doi.org/10.1371/
journal.pcbi.1002738
Davies, N. G., Abbott, S., Barnard, R. C., Jarvis, C. I., Kucharski, A. J., Munday, J. D., Pearson, C. A. B., Russell, T. W., Tully, D. C., Washburne, A. D., Wenseleers, T., Gimma, A., Waites, W., Wong, K. L. M., van Zandvoort, K., Silverman, J. D., Diaz-Ordaz, K., Keogh, R., Eggo, R. M.,
… Edmunds, W. J., CMMID COVID-19 Working Group. (2021).
Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England. Science, 372(6538), eabg3055. https://doi.org/10.1126/sci- ence.abg3055
Dejnirattisai, W., Zhou, D., Supasa, P., Liu, C., Mentzer, A. J., Ginn, H. M., Zhao, Y., Duyvesteyn, H. M., Tuekprakhon, A., Nutalai, R., Wang, B., Lopez-Camacho, C., Slon-Campos, J., Walter, T. S., Skelly, D., Costa Clemens, S. A., Naveca, F. G., Nascimento, V., Nascimento, F., … Screaton, G. R. (2021). Antibody evasion by the P.1 strain of SARS- CoV-2. Cell, 184(11), 2939–2954.e9. https://doi.org/10.1016/j.cell.2021.
03.055
Gan, H. H., Twaddle, A., Marchand, B., & Gunsalus, K. C. (2021). Structural modeling of the SARS-CoV-2 Spike/human ACE2 complex interface can identify high-affinity variants associated with increased
transmissibility.Journal of Molecular Biology,433(15), 167051.https://
doi.org/10.1016/j.jmb.2021.167051
Greaney, A. J., Starr, T. N., Gilchuk, P., Zost, S. J., Binshtein, E., Loes, A. N., Hilton, S. K., Huddleston, J., Eguia, R., Crawford, K. H., Dingens, A. S., Nargi, R. S., Sutton, R. E., Suryadevara, N., Rothlauf, P. W., Liu, Z., Whelan, S. P., Carnahan, R. H., Crowe, J. E., & Bloom, J. D. (2021).
Complete mapping of mutations to the SARS-CoV-2 spike receptor- binding domain that escape antibody recognition. Cell Host &
Microbe,29(1), 44–57 e49.https://doi.org/10.1016/j.chom.2020.11.007 Guerois, R., Nielsen, J. E., & Serrano, L. (2002). Predicting changes in the
stability of proteins and protein complexes: A study of more than 1000 mutations.Journal of Molecular Biology,320(2), 369–387.https://
doi.org/10.1016/S0022-2836(02)00442-4S0022-2836(02)00442-4 [pii]
Hoffmann, M., Kleine-Weber, H., Schroeder, S., Kr€uger, N., Herrler, T., Erichsen, S., Schiergens, T. S., Herrler, G., Wu, N.-H., Nitsche, A., M€uller, M. A., Drosten, C., & P€ohlmann, S. (2020). SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor.Cell,181(2), 271–280 e278.https://doi.org/10.1016/
j.cell.2020.02.052
Kyriakidis, N. C., Lopez-Cortes, A., Gonzalez, E. V., Grimaldos, A. B., &
Prado, E. O. (2021). SARS-CoV-2 vaccines strategies: A comprehensive review of phase 3 candidates.NPJ Vaccines, 6(1), 28.https://doi.org/
10.1038/s41541-021-00292-w
Lan, J., Ge, J., Yu, J., Shan, S., Zhou, H., Fan, S., Zhang, Q., Shi, X., Wang, Q., Zhang, L., & Wang, X. (2020). Structure of the SARS-CoV-2 spike receptor-binding domain bound to the ACE2 receptor. Nature, 581(7807), 215–220.https://doi.org/10.1038/s41586-020-2180-5[pii]
Laskowski, R. A., & Swindells, M. B. (2011). LigPlotþ: Multiple ligand-pro- tein interaction diagrams for drug discovery. Journal of Chemical Information and Modeling, 51(10), 2778–2786.https://doi.org/10.1021/
ci200227u
Laurini, E., Marson, D., Aulic, S., Fermeglia, A., & Pricl, S. (2021).
Computational mutagenesis at the SARS-CoV-2 spike protein/angio- tensin-converting enzyme 2 binding interface: Comparison with experimental evidence.ACS Nano, 15(4), 6929–6948. https://doi.org/
10.1021/acsnano.0c10833
Li, Q., Wu, J., Nie, J., Zhang, L., Hao, H., Liu, S., Zhao, C., Zhang, Q., Liu, H., Nie, L., Qin, H., Wang, M., Lu, Q., Li, X., Sun, Q., Liu, J., Zhang, L., Li, X., Huang, W., & Wang, Y. (2020). The impact of mutations in SARS- CoV-2 spike on viral infectivity and antigenicity. Cell, 182(5), 1284– 1294.e9.https://doi.org/10.1016/j.cell.2020.07.012
Mannar, D., Saville, J. W., Zhu, X., Srivastava, S. S., Berezuk, A. M., Tuttle, K. S., Marquez, A. C., Sekirov, I., & Subramaniam, S. (2022). SARS-CoV-2 Omicron variant: Antibody evasion and cryo-EM structure of spike protein-ACE2 complex.Science,375(6582), 760–764.https://doi.org/10.
1126/science.abn7760
Moreira, R. A., Chwastyk, M., Baker, J. L., Guzman, H. V., & Poma, A. B.
(2020). Quantitative determination of mechanical stability in the novel coronavirus spike protein.Nanoscale,12(31), 16409–16413.https://doi.
org/10.1039/D0NR03969A
Schymkowitz, J., Borg, J., Stricher, F., Nys, R., Rousseau, F., & Serrano, L.
(2005). The FoldX web server: An online force field. Nucleic Acids Research,33(Web Server), W382–W388. 10.1093/nar/gki387
Shang, J., Wan, Y., Luo, C., Ye, G., Geng, Q., Auerbach, A., & Li, F. (2020).
Cell entry mechanisms of SARS-CoV-2. Proceedings of the National Academy of Sciences, 117(21), 11727–11734. 2003138117 [pii]https://
doi.org/10.1073/pnas.2003138117
Starr, T. N., Greaney, A. J., Addetia, A., Hannon, W. W., Choudhary, M. C., Dingens, A. S., Li, J. Z., & Bloom, J. D. (2021). Prospective mapping of viral mutations that escape antibodies used to treat COVID-19.
Science,371(6531), 850–854. ]https://doi.org/10.1126/science.abf9302 Starr, T. N., Greaney, A. J., Hilton, S. K., Ellis, D., Crawford, K. H., Dingens,
A. S., Navarro, M. J., Bowen, J. E., Tortorici, M. A., Walls, A. C., King, N. P., Veesler, D., & Bloom, J. D. (2020). Deep mutational scanning of SARS-CoV-2 receptor binding domain reveals constraints on folding and ACE2 binding. Cell, 182(5), 1295–1310.e20. https://doi.org/10.
1016/j.cell.2020.08.012
Tai, W., He, L., Zhang, X., Pu, J., Voronin, D., Jiang, S., Zhou, Y., & Du, L.
(2020). Characterization of the receptor-binding domain (RBD) of 2019 novel coronavirus: Implication for development of RBD protein
as a viral attachment inhibitor and vaccine. Cellular & Molecular Immunology, 17(6), 613–620.[pii] https://doi.org/10.1038/s41423-020- 0400-4
Teng, S., Madej, T., Panchenko, A., & Alexov, E. (2009). Modeling effects of human single nucleotide polymorphisms on protein-protein inter- actions. Biophysical Journal,96(6), 2178–2188. S0006-3495(09)00325-7 [pii]https://doi.org/10.1016/j.bpj.2008.12.3904
Teng, S., Sobitan, A., Rhoades, R., Liu, D., & Tang, Q. (2021). Systemic effects of missense mutations on SARS-CoV-2 spike glycoprotein sta- bility and receptor-binding affinity. Briefings in Bioinformatics, 22(2), 1239–1253. 5917277 [pii]https://doi.org/10.1093/bib/bbaa233 Tian, F., Tong, B., Sun, L., Shi, S., Zheng, B., Wang, Z., Dong, X., & Zheng,
P. (2021). N501Y mutation of spike protein in SARS-CoV-2 strengthens its binding to receptor ACE2.eLife,10, e69091. [pii]https://doi.org/10.
7554/eLife.69091
Vedithi, S. C., Rodrigues, C. H., Portelli, S., Skwark, M. J., Das, M., Ascher, D. B., Blundell, T. L., & Malhotra, S. (2020). Computational saturation mutagenesis to predict structural consequences of systematic muta- tions in the beta subunit of RNA polymerase in Mycobacterium lep- rae.Computational and Structural Biotechnology Journal, 18, 271–286.
S2001-0370(19)30291-0 [pii]https://doi.org/10.1016/j.csbj.2020.01.002 Walls, A. C., Park, Y. J., Tortorici, M. A., Wall, A., McGuire, A. T., & Veesler,
D. (2020). Structure, function, and antigenicity of the SARS-CoV-2 spike glycoprotein. Cell, 181(2), 281–292.e6. https://doi.org/10.1016/j.
cell.2020.02.058
Wang, R., Hozumi, Y., Yin, C., & Wei, G. W. (2020). Decoding SARS-CoV-2 transmission and evolution and ramifications for COVID-19 diagnosis, vaccine, and medicine.Journal of Chemical Information and Modeling, 60(12), 5853–5865.https://doi.org/10.1021/acs.jcim.0c00501
Wang, Q., Zhang, Y., Wu, L., Niu, S., Song, C., Zhang, Z., Lu, G., Qiao, C., Hu, Y., Yuen, K.-Y., Wang, Q., Zhou, H., Yan, J., & Qi, J. (2020).
Structural and functional basis of SARS-CoV-2 entry by using human ACE2. Cell, 181(4), 894–904.e9. https://doi.org/10.1016/j.cell.2020.03.
045
Wibmer, C. K., Ayres, F., Hermanus, T., Madzivhandila, M., Kgagudi, P., Oosthuysen, B., Lambson, B. E., de Oliveira, T., Vermeulen, M., van der Berg, K., Rossouw, T., Boswell, M., Ueckermann, V., Meiring, S., von Gottberg, A., Cohen, C., Morris, L., Bhiman, J. N., & Moore, P. L. (2021).
SARS-CoV-2 501Y.V2 escapes neutralization by South African COVID- 19 donor plasma.Nature Medicine,27(4), 622–625.[pii]https://doi.org/
10.1038/s41591-021-01285-x
Wrapp, D., Wang, N., Corbett, K. S., Goldsmith, J. A., Hsieh, C.-L., Abiona, O., Graham, B. S., & McLellan, J. S. (2020). Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation. Science, 367(6483), 1260–1263. ]https://doi.org/10.1126/science.abb2507
Wrobel, A. G., Benton, D. J., Xu, P., Roustan, C., Martin, S. R., Rosenthal, P. B., Skehel, J. J., & Gamblin, S. J. (2020). SARS-CoV-2 and bat RaTG13 spike glycoprotein structures inform on virus evolution and furin- cleavage effects.Nature Structural & Molecular Biology,27(8), 763–767.
https://doi.org/10.1038/s41594-020-0468-7[pii]
Wu, F., Zhao, S., Yu, B., Chen, Y.-M., Wang, W., Song, Z.-G., Hu, Y., Tao, Z.- W., Tian, J.-H., Pei, Y.-Y., Yuan, M.-L., Zhang, Y.-L., Dai, F.-H., Liu, Y., Wang, Q.-M., Zheng, J.-J., Xu, L., Holmes, E. C., & Zhang, Y.-Z. (2020). A new coronavirus associated with human respiratory disease in China.
Nature, 579(7798), 265–269.[pii] 2008 [pii] https://doi.org/10.1038/
s41586-020-2008-3
Wu, F., Zhao, S., Yu, B., Chen, Y.-M., Wang, W., … Zhang, Y.-Z. (2020).
Complete genome characterisation of a novel coronavirus associated with severe human respiratory disease in Wuhan, China. bioRxiv 2020.01.24.919183.https://doi.org/10.1101/2020.01.24.919183 Xie, X., Liu, Y., Liu, J., Zhang, X., Zou, J., Fontes-Garfias, C. R., Xia, H.,
Swanson, K. A., Cutler, M., Cooper, D., Menachery, V. D., Weaver, S. C., Dormitzer, P. R., & Shi, P.-Y. (2021). Neutralization of SARS-CoV-2 spike 69/70 deletion, E484K and N501Y variants by BNT162b2 vaccine-eli- cited sera. Nature Medicine, 27(4), 620–621. https://doi.org/10.1038/
s41591-021-01270-4[pii]
Xu, S., Wang, Y., Wang, Y., Zhang, C., Hong, Q., Gu, C., Xu, R., Wang, T., Yang, Y., Zang, J., Zhou, Y., Li, Z., Liu, Q., Zhou, B., Bai, L., Zhu, Y., Deng, Q., Wang, H., Lavillette, D., … Huang, Z. (2022). Mapping cross-variant neutralizing sites on the SARS-CoV-2 spike protein.
Emerging Microbes & Infections, 11(1), 351–367. https://doi.org/10.
1080/22221751.2021.2024455
Xue, B., Li, R., Ma, H., Rahaman, A., & Kumar, V. (2022). Comprehensive mapping of mutations in the C9ORF72 that affect folding and binding to SMCR8 protein.Process Biochemistry,121, 312–321.https://doi.org/
10.1016/j.procbio.2022.07.013
Xue, L. C., Rodrigues, J. P., Kastritis, P. L., Bonvin, A. M., & Vangone, A.
(2016). PRODIGY: A web server for predicting the binding affinity of protein-protein complexes.Bioinformatics, 32(23), 3676–3678.https://
doi.org/10.1093/bioinformatics/btw514
Xue, X., Shi, J., Xu, H., Qin, Y., Yang, Z., Feng, S., Liu, D., Jian, L., Hua, L., Wang, Y., Zhang, Q., Huang, X., Zhang, X., Li, X., Chen, C., Guo, J., Tang, W., & Liu, J. (2021). Dynamics of binding ability prediction between spike protein and human ACE2 reveals the adaptive strategy of SARS-CoV-2 in humans.Scientific Reports, 11(1), 3187. https://doi.
org/10.1038/s41598-021-82938-23187[pii] 82938 [pii]
Zhang, L., Jackson, C. B., Mou, H., Ojha, A., Peng, H., Quinlan, B. D., Rangarajan, E. S., Pan, A., Vanderheiden, A., Suthar, M. S., Li, W., Izard,
T., Rader, C., Farzan, M., & Choe, H. (2020). SARS-CoV-2 spike-protein D614G mutation increases virion spike density and infectivity.Nature Communications, 11(1), 6013. https://doi.org/10.1038/s41467-020- 19808-4[pii]
Zhang, J., Xiao, T., Cai, Y., Lavine, C. L., Peng, H., Zhu, H., Anand, K., Tong, P., Gautam, A., Mayer, M. L., Walsh, R. M., Rits-Volloch, S., Wesemann, D. R., Yang, W., Seaman, M. S., Lu, J., & Chen, B. (2021). Membrane fusion and immune evasion by the spike protein of SARS-CoV-2 Delta variant.Science (New York, N.Y.),374(6573), 1353–1360.https://doi.org/
10.1126/science.abl9463
Zhou, T., Tsybovsky, Y., Gorman, J., Rapp, M., Cerutti, G., Chuang, G.-Y., Katsamba, P. S., Sampson, J. M., Sch€on, A., Bimela, J., Boyington, J. C., Nazzari, A., Olia, A. S., Shi, W., Sastry, M., Stephens, T., Stuckey, J., Teng, I.-T., Wang, P., … Kwong, P. D. (2020). Cryo-EM structures of SARS-CoV-2 spike without and with ACE2 reveal a pH-dependent switch to mediate endosomal positioning of receptor-binding domains. Cell Host & Microbe. 28(6), 867–879.e5. https://doi.org/10.
1016/j.chom.2020.11.004
