Author: Capponi, Sara; Wang, Shangying; Navarro, Erik J.; Bianco, Simone
Title: AI-driven prediction of SARS-CoV-2 variant binding trends from atomistic simulations Cord-id: 63ojof50 Document date: 2021_10_6
ID: 63ojof50
Snippet: 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
Document: 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: [Image: see text]
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