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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 algo...

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Detalles Bibliográficos
Autores principales: Capponi, Sara, Wang, Shangying, Navarro, Erik J., Bianco, Simone
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8493367/
https://www.ncbi.nlm.nih.gov/pubmed/34613523
http://dx.doi.org/10.1140/epje/s10189-021-00119-5
Descripción
Sumario: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]