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Cited by 12SARS-CoV-2 and COVID-19 ResearchProtein Structure and DynamicsComputational Drug Discovery Methods

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AI-driven prediction of SARS-CoV-2 variant binding trends from atomistic simulations

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Capponi, Sara, Wang, Shangying, Navarro, Erik J., Bianco, Simone·2021·The European Physical Journal E
DOI 10.1140/epje/s10189-021-00119-5PMC PMC8493367

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Abstract

We present a novel technique to predict binding affinity trends between two molecules from atomistic molecular dynamics simulations. The technique uses a neural network algorithm applied to a series of images encoding the distance between two molecules in time. We demonstrate that our algorithm is capable of separating with high accuracy non-hydrophobic mutations with low binding affinity from those with high binding affinity. Moreover, we show high accuracy in prediction using a small subset of the simulation, therefore requiring a much shorter simulation time. We apply our algorithm to the binding between several variants of the SARS-CoV-2 spike protein and the human receptor ACE2. Graphic abstract:

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Metadata

Authors
Capponi, S., Wang, S., Navarro, E. J., & Bianco, S.
Affiliations
University of California, San Francisco (US)
Corresponding author
Bianco, Simone
ORCID
Venue
The European Physical Journal E
Publisher
Springer Science and Business Media LLC
Published
2021-10
Volume
44
Issue
10
Pages
123-123
ISSN
  • 1292-8941 (ISSN-L)
  • 1292-895X
DOI
10.1140/epje/s10189-021-00119-5
PMC
PMC8493367
OpenAlex
W3202379675
Cited by
12
References
3
Citations by year
2026: 2 · 2025: 2 · 2024: 3 · 2023: 1 · 2022: 3 · 2021: 1
FWCI
0.63
Type
journal article
Language
en
Open access
Licence
Locations
  • The European Physical Journal E (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Springer Link (Chiba Institute of Technology) (submittedVersion)
Funders
National Science Foundation (DBI-1548297)
Topics
  • Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Keywords
SARS-CoV-2 variants, spike protein, ACE2 receptor, protein-protein binding affinity, binding affinity prediction, artificial neural networks, molecular dynamics simulation, atomistic simulation
MeSH terms
Angiotensin-Converting Enzyme 2, SARS-CoV-2, Algorithms, Artificial Intelligence, Humans, Models, Molecular, Mutation, Protein Binding, Protein Conformation, Neural Networks, Computer, Molecular Dynamics Simulation, Spike Glycoprotein, Coronavirus

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