Computational Prediction of Mutational Effects on SARS-CoV-2 Binding by Relative Free Energy Calculations

Zou, Junjie; Yin, Jian; Fang, Lei; Yang, Mingjun; Wang, Tianyuan; Wu, Weikun; Bellucci, Michael A.; Zhang, Peiyu

doi:10.1021/acs.jcim.0c00679

Cited by 71 publications

(68 citation statements)

References 44 publications

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“…Almost all high-importance residues were mutated in SARS-CoV-2 compared to SARS-CoV. Six of the seven mutations identified in our ML approaches were also highlighted in previous studies: Val404/Lys417, Arg426/Asn439, Tyr442/Leu455, Leu443/Phe456, Tyr484/Gln498, Thr487/Asn501, 13,14,23,24 demonstrating the ability of our approach to replicate human insight.…”

Section: Discussionsupporting

confidence: 76%

“…A number of previous molecular dynamics (MD) studies have compared the dynamics of SARS-CoV and SARS-CoV-2 complexes with ACE2, 21,[23][24][25][26] including a study of the full-length complex in the context of the cellular membrane. 27 It was shown that the SARS-CoV-2 complex is more stable, as measured by root-mean-square deviation (RMSD) and the increased number contacts between RBD and ACE2.…”

Section: Introductionmentioning

confidence: 99%

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Critical interactions for SARS-CoV-2 spike protein binding to ACE2 identified by machine learning

Pavlova

Zhang

Acharya

et al. 2021

Preprint

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Both SARS-CoV and SARS-CoV-2 bind to the human ACE2 receptor. Based on high-resolution structures, the two viruses bind in practically identical conformations, although several residues of the receptor-binding domain (RBD) differ between them. Here we have used molecular dynamics (MD) simulations, machine learning (ML), and free energy perturbation (FEP) calculations to elucidate the differences in RBD binding by the two viruses. Although only subtle differences were observed from the initial MD simulations of the two RBD-ACE2 complexes, ML identified the individual residues with the most distinctive ACE2 interactions, many of which have been highlighted in previous experimental studies. FEP calculations quantified the corresponding differences in binding free energies to ACE2, and examination of MD trajectories provided structural explanations for these differences. Lastly, the energetics of emerging SARS-CoV-2 mutations were studied, showing that the affinity of the RBD for ACE2 is increased by N501Y and E484K mutations but is slightly decreased by K417N.

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Section: Discussionsupporting

confidence: 76%

Section: Introductionmentioning

confidence: 99%

Critical interactions for SARS-CoV-2 spike protein binding to ACE2 identified by machine learning

Pavlova

Zhang

Acharya

et al. 2021

Preprint

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“…Yet, only a limited number of studies were performed to investigate critical interactions that facilitate S-ACE2 binding using MD simulations. Initial studies have constructed a homology model of SARS-CoV-2 RBD in complex with ACE2, based on the SARS-CoV crystal structure 8, 14 and performed conventional MD (cMD) simulations totaling 10 ns 15, 16 and 100 ns 17, 18 in length to estimate binding free energies 15, 16 and interaction scores 18 . More recent studies used the crystal structure of SARS-CoV-2 RBD in complex with ACE2 to perform coarse-grained 19 and all-atom 20, 21, 22, 23 MD simulations.…”

Section: Introductionmentioning

confidence: 99%

Critical Interactions Between the SARS-CoV-2 Spike Glycoprotein and the Human ACE2 Receptor

Taka

Yilmaz

Golcuk

et al. 2020

Preprint

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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enters human cells upon binding of its spike (S) glycoproteins to ACE2 receptors and causes the Coronavirus disease 2019 (COVID-19). Therapeutic approaches to prevent SARS-CoV-2 infection are mostly focused on blocking S-ACE2 binding, but critical residues that stabilize this interaction are not well understood. By performing all-atom Molecular Dynamics (MD) simulations, we identified an extended network of salt bridges, hydrophobic and electrostatic interactions, and hydrogen bonding between the receptor-binding domain (RBD) of the S protein and ACE2. Mutagenesis of these residues on the RBD was not sufficient to destabilize binding but reduced the average work to unbind the S protein from ACE2. In particular, the hydrophobic end of RBD serves as the main anchor site and unbinds last from ACE2 under force. We propose that blocking this site via neutralizing antibody or nanobody could prove an effective strategy to inhibit S-ACE2 interactions.

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“…However, there are no quantitative assessments of the energetics of the actual interacting residues between RBD and ACE2. While numerous biophysical and simulation studies on this complex have been conducted , [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40] more research effort is necessary to resolve still unanswered questions. First, it is not clear how the SARS-CoV-2 RBD recognizes and binds to ACE2, and what is the pattern of binding interaction.…”

Section: Introductionmentioning

confidence: 99%

Key Interacting Residues Between RBD of SARS-CoV-2 and ACE2 Receptor: Combination of Molecular Dynamic Simulation and Density Functional Calculation

Jawad¹,

Adhikari²,

Podgornik³

et al. 2021

Preprint

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The spike protein of SARS-CoV-2 binds to ACE2 receptor via its receptor-binding domain (RBD), with the RBD-ACE2 complex presenting an essential molecular target for vaccine development to stall the virus infection proliferation. The computational analysis at molecular, amino acid (AA) and atomic levels have been performed systematically to identify the key interacting AAs in the formation of the RBD-ACE2 complex, including the MD simulations with molecular mechanics generalized Born surface area (MM-GBSA) method to predict binding free energy (BFE) and to determine the actual interacting AAs, as well as two ab initio quantum chemical protocols based on the density functional theory (DFT) implementation. Based on MD results, Q493, Y505, Q498, N501, T500, N487, Y449, F486, K417, Y489, F456, Y495, and L455 have been identified as hotspots in RBD, while those in ACE2 are K353, K31, D30, D355, H34, D38, Q24, T27, Y83, Y41, E35, and E37. Both the electrostatic and hydrophobic interactions are the main driving force to form the AA-AA binding pairs. We confirm that Q493, N501, F486, K417, and F456 in RBD are the key residues responsible for the tight binding of SARS-CoV-2 with ACE2 compared to SARS-CoV. The DFT results reveal that N487, Q493, Y449, T500, G496, G446 and G502 in RBD form pairs via specific hydrogen bonding with Q24, H34, E35, D38, Y41, Q42 and K353 in ACE2.

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Computational Prediction of Mutational Effects on SARS-CoV-2 Binding by Relative Free Energy Calculations

Cited by 71 publications

References 44 publications

Critical interactions for SARS-CoV-2 spike protein binding to ACE2 identified by machine learning

Critical interactions for SARS-CoV-2 spike protein binding to ACE2 identified by machine learning

Critical Interactions Between the SARS-CoV-2 Spike Glycoprotein and the Human ACE2 Receptor

Key Interacting Residues Between RBD of SARS-CoV-2 and ACE2 Receptor: Combination of Molecular Dynamic Simulation and Density Functional Calculation

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