ArticleProceedings of the National Academy of Sciences of the United States of America2021
Computational prediction of the effect of amino acid changes on the binding affinity between SARS-CoV-2 spike RBD and human ACE2.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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51 citing papers in PubMed, 82 citations in OpenAlex.
- EscaPRRS-ORF5: a structure-aware evolutionary framework for prioritizing immune escape-prone variants in porcine reproductive and respiratory syndrome virus.Bioinformatics (Oxford, England) · 2026Article
- Machine Learning-Driven Simulations of the SARS-CoV-2 Fitness Landscape from Deep Mutational Scanning Experiments.Journal of chemical information and modeling · 2026Article
- Modeling the Mutational Effects on Biochemical Phenotypes of SARS-CoV-2 Using Molecular Fields.Biomolecules · 2025Article
- Generative prediction of real-world prevalent SARS-CoV-2 mutation with in silico virus evolution.Briefings in bioinformatics · 2025Article
- Biophysics of SARS-CoV-2 spike protein's receptor-binding domain interaction with ACE2 and neutralizing antibodies: from computation to functional insights.Biophysical reviews · 2025Review
- Viral escape-inspired framework for structure-guided dual bait protein biosensor design.PLoS computational biology · 2025Article
- Serological Assays Reveal No Evidence of Natural SARS-CoV-2 Infection in US Cattle.Microorganisms · 2025Article
- Secondary metabolites of Alternaria alternate appraisal of their SARS-CoV-2 inhibitory and anti-inflammatory potentials.PloS one · 2025Article
- Unveiling the ghost: machine learning's impact on the landscape of virology.The Journal of general virology · 2025Review
- Binding affinity between coronavirus spike protein and human ACE2 receptor.Computational and structural biotechnology journal · 2024Review
- Biophysical principles predict fitness of SARS-CoV-2 variants.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Article
- Brain Pathology in COVID-19: Clinical Manifestations and Potential Mechanisms.Neuroscience bulletin · 2024Review
- Unraveling the Dynamics of SARS-CoV-2 Mutations: Insights from Surface Plasmon Resonance Biosensor Kinetics.Biosensors · 2024Review
- Biophysical principles predict fitness of SARS-CoV-2 variants.bioRxiv : the preprint server for biology · 2024Article
- Raman Fingerprints of SARS-CoV-2 Omicron Subvariants: Molecular Roots of Virological Characteristics and Evolutionary Directions.ACS infectious diseases · 2023Article
- Comparative Study of the Mutations Observed in the SARS-CoV-2 RBD Variants of Concern and Their Impact on the Interaction with the ACE2 Protein.The journal of physical chemistry. B · 2023Article
- Little Brown Bats (Viruses · 2023Article
- Machine Learning Guided Design of High-Affinity ACE2 Decoys for SARS-CoV-2 Neutralization.The journal of physical chemistry. B · 2023Article
- SARS-CoV-2 Spike Protein Post-Translational Modification Landscape and Its Impact on Protein Structure and Function via Computational Prediction.Research (Washington, D.C.) · 2023Article
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Authors and funding
12 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The association of the receptor binding domain (RBD) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein with human angiotensin-converting enzyme 2 (hACE2) represents the first required step for cellular entry. SARS-CoV-2 has continued to evolve with the emergence of several novel variants, and amino acid changes in the RBD have been implicated with increased fitness and potential for immune evasion. Reliably predicting the effect of amino acid changes on the ability of the RBD to interact more strongly with the hACE2 can help assess the implications for public health and the potential for spillover and adaptation into other animals. Here, we introduce a two-step framework that first relies on 48 independent 4-ns molecular dynamics (MD) trajectories of RBD-hACE2 variants to collect binding energy terms decomposed into Coulombic, covalent, van der Waals, lipophilic, generalized Born solvation, hydrogen bonding, π-π packing, and self-contact correction terms. The second step implements a neural network to classify and quantitatively predict binding affinity changes using the decomposed energy terms as descriptors. The computational base achieves a validation accuracy of 82.8% for classifying single-amino acid substitution variants of the RBD as worsening or improving binding affinity for hACE2 and a correlation coefficient of 0.73 between predicted and experimentally calculated changes in binding affinities. Both metrics are calculated using a fivefold cross-validation test. Our method thus sets up a framework for screening binding affinity changes caused by unknown single- and multiple-amino acid changes offering a valuable tool to predict host adaptation of SARS-CoV-2 variants toward tighter hACE2 binding.
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