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

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.

SUBMITTER: Capponi S

PROVIDER: S-EPMC8493367 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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

Capponi Sara S Wang Shangying S Navarro Erik J EJ Bianco Simone S

The European physical journal. E, Soft matter 20211006 10

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 ...[more]

PMID: 34613523

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