Computational prediction of the effect of amino acid changes on the binding affinity between SARS-CoV-2 spike RBD and human ACE2 Article Swipe

View

Related Concepts

Amino acid van der Waals force Matthews correlation coefficient Molecular dynamics Chemistry Binding energy Hydrogen bond Lysine Computational biology Biophysics Biochemistry Biology Computational chemistry Molecule Computer science Artificial intelligence Physics Support vector machine Nuclear physics Organic chemistry

Chen Chen , Veda Sheersh Boorla , Deepro Banerjee , Ratul Chowdhury , Victoria S. Cavener , Ruth H. Nissly , Abhinay Gontu , Nina R. Boyle , Kurt J. Vandegrift , Meera Surendran Nair , Suresh V. Kuchipudi , Costas D. Maranas ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.1073/pnas.2106480118 · OA: W3202850396

Significance SARS-CoV-2 infection proceeds through the binding of viral surface spike protein to the human ACE2 protein. The global spread of the infection has led to the emergence of fitter and more transmissible variants with increased adaptation both in human and nonhuman hosts. Molecular simulations of the binding event between the spike and ACE2 proteins offer a route to assess potential increase or decrease in infectivity by measuring the change in binding strength. We trained a neural network model that accurately maps simulated binding energies to experimental changes in binding strength upon amino acid changes in the spike protein. This computational workflow can be used to a priori assess currently circulating and prospectively future viral variants for their affinity for hACE2.