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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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Figures

Fig. 1 a Representative snapshot of the chimeric spike receptor binding domain (RBD) (purple) in association with ACE2 (gray). Proteins are represented in New Cartoon format. Zinc (orange), and chloride (blue) ions are represented in van der Waals format. Bottom left: representation of the spike trimeric complex from [9], one chain is blue with the RBD colored purple, the other 2 chains are transparent gray and green for clarity. b, c Closeup view of the contact region between RBD and ACE2. The non-hydrophobic, non-aromatic residues that are mutated and studied in this paper are indicated. They are represented in licorice format colored according to the ion type (red for oxygen, cyan for carbon, blue for nitrogen). Hydrogen atoms are not displayed for clarity. c Same representation of panel b rotated of 90^(∘)
Fig. 1 a Representative snapshot of the chimeric spike receptor binding domain (RBD) (purple) in association with ACE2 (gray). Proteins are represented in New Cartoon format. Zinc (orange), and chloride (blue) ions are represented in van der Waals format. Bottom left: representation of the spike trimeric complex from [9], one chain is blue with the RBD colored purple, the other 2 chains are transparent gray and green for clarity. b, c Closeup view of the contact region between RBD and ACE2. The non-hydrophobic, non-aromatic residues that are mutated and studied in this paper are indicated. They are represented in licorice format colored according to the ion type (red for oxygen, cyan for carbon, blue for nitrogen). Hydrogen atoms are not displayed for clarity. c Same representation of panel b rotated of 90^(∘)CC BY · PubMed Central
Fig. 2 Conversion of the MD data to input matrices for a neural algorithm. a Representative snapshot of the chimeric RBD-ACE2 complex indicating the Cα carbon atoms used to calculate the distance between the RBD and ACE2. The color code is as in Fig. 1, and the Cα carbon atoms are represented in cyan, van der Waals format. The distances between the atoms are then represented as matrix, are normalized by each of their minima and maxima, and converted to gray-scaled images. b Representative distance matrices of the chimeric RBD-ACE2 complex simulations taken at different steps of the simulation showing the effect of data preprocessing. c Representation of the data processing to obtain machine learning inputs. We first subtract the distance matrix at t=0 from the distance matrices from the simulations at each time steps (central panel). We then use a moving average to average the subtracted matrices and use these as input to the algorithm (right-hand panel)
Fig. 2 Conversion of the MD data to input matrices for a neural algorithm. a Representative snapshot of the chimeric RBD-ACE2 complex indicating the Cα carbon atoms used to calculate the distance between the RBD and ACE2. The color code is as in Fig. 1, and the Cα carbon atoms are represented in cyan, van der Waals format. The distances between the atoms are then represented as matrix, are normalized by each of their minima and maxima, and converted to gray-scaled images. b Representative distance matrices of the chimeric RBD-ACE2 complex simulations taken at different steps of the simulation showing the effect of data preprocessing. c Representation of the data processing to obtain machine learning inputs. We first subtract the distance matrix at t=0 from the distance matrices from the simulations at each time steps (central panel). We then use a moving average to average the subtracted matrices and use these as input to the algorithm (right-hand panel)CC BY · PubMed Central
Fig. 3 Sample gray-scaled images representing the averaged matrices for some of the mutations studied in this paper at t=20ns
Fig. 3 Sample gray-scaled images representing the averaged matrices for some of the mutations studied in this paper at t=20nsCC BY · PubMed Central
Fig. 4 Structure of the convolutional neural network and ensemble prediction used in this study. a The convolutional neural network architecture. b The ensemble prediction consisting of N independently trained convolutional neural networks. The final prediction is based on the simple majority of all neural networks. c Definition of the output categories
Fig. 4 Structure of the convolutional neural network and ensemble prediction used in this study. a The convolutional neural network architecture. b The ensemble prediction consisting of N independently trained convolutional neural networks. The final prediction is based on the simple majority of all neural networks. c Definition of the output categoriesCC BY · PubMed Central
Fig. 5 The overall test accuracy of CNNs varies with the simulation time. The blue dots are the average prediction accuracy from ten independently trained CNNs. The error bars represent the 10th percentile and 80th percentile. The orange diamonds represent the prediction accuracy of the ensemble prediction, voted by 10 NN assembles
Fig. 5 The overall test accuracy of CNNs varies with the simulation time. The blue dots are the average prediction accuracy from ten independently trained CNNs. The error bars represent the 10th percentile and 80th percentile. The orange diamonds represent the prediction accuracy of the ensemble prediction, voted by 10 NN assemblesCC BY · PubMed Central
Fig. 6 RMSD calculated for the Cα carbon atoms of the RBD and ACE2 and for the contact area for the simulations reported in the panels. For clarity, data are represented every 2 ns
Fig. 6 RMSD calculated for the Cα carbon atoms of the RBD and ACE2 and for the contact area for the simulations reported in the panels. For clarity, data are represented every 2 nsCC BY · PubMed Central
Fig. 7 RMSD calculated for the Cα carbon atoms of the RBD and ACE2 and for the contact area for the simulations reported in the panels. For clarity, data are represented every 2 ns
Fig. 7 RMSD calculated for the Cα carbon atoms of the RBD and ACE2 and for the contact area for the simulations reported in the panels. For clarity, data are represented every 2 nsCC BY · PubMed Central
Fig. 8 RMSD calculated for the Cα carbon atoms of the RBD and ACE2 and for the contact area for the simulations reported in the panels. For clarity, data are represented every 2 ns
Fig. 8 RMSD calculated for the Cα carbon atoms of the RBD and ACE2 and for the contact area for the simulations reported in the panels. For clarity, data are represented every 2 nsCC BY · PubMed Central

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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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