Regular Article - Living Systems
AI-driven prediction of SARS-CoV-2 variant binding trends from atomistic simulations
IBM Almaden Research Center, 650 Harry Rd, 95120, San Jose, CA, USA
2 Center for Cellular Construction, 94158, San Francisco, CA, USA
3 Graduate Program in Biophysics, University of California, San Francisco, 94158, San Francisco, CA, USA
Accepted: 24 August 2021
Published online: 6 October 2021
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.
© The Author(s) 2021
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