Assessment of Binding Affinity in the Complexes of CoV-S-Protein’s RBD and the ACE2 Using Convolutional Neural Networks

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Abstract

The experimentally obtained structures of 48 complexes of the ACE2 receptor with the S protein’s RBD of the coronaviruses SARS-CoV and SARS-CoV-2 (including mutant forms of the latter) were assessed and the dissociation constant was calculated for them. Prediction of binding affinity was carried out using ProBAN, a neural network algorithm, previously developed by the authors, and a number of other algorithms for estimating the Gibbs free energy such as Prodigy, FoldX, DFIRE and RosettaDock. A comparison of the evaluation results shows that ProBAN has the best prediction quality (Pearson correlation − 0.56, MAE − 0.7 kcal/mol) of all the analyzed algorithms. The results obtained suggest better quality of affinity prediction for other protein-protein complexes. Information about the complexes under study and prediction results are available in the repository at the link: https://github.com/EABogdanova/ProBAN_RBD-ACE2.

About the authors

E. A Bogdanova

Lomonosov Moscow State University

Moscow, 119991 Russia

A. V Chernukhin

D.I. Mendeleev University of Chemical Technology of Russia

Moscow, 125047 Russia

K. V Shaitan

Lomonosov Moscow State University

Moscow, 119991 Russia

V. N Novoseletsky

Shenzhen MSU-BIT University, International University Park Road

Email: novoseletsky@smbu.edu.cn
Shenzhen, 518172, People’s Republic of China

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