A CNN model for predicting binding affinity changes between SARS-CoV-2 spike RBD variants and ACE2 homologues.

ABSTRACT: The cellular entry of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) involves the association of its receptor binding domain (RBD) with human angiotensin converting enzyme 2 (hACE2) as the first crucial step. Efficient and reliable prediction of RBD-hACE2 binding affinity changes upon amino acid substitutions can be valuable for public health surveillance and monitoring potential spillover and adaptation into non-human species. Here, we introduce a convolutional neural network (CNN) model trained on protein sequence and structural features to predict experimental RBD-hACE2 binding affinities of 8,440 variants upon single and multiple amino acid substitutions in the RBD or ACE2. The model achieves a classification accuracy of 83.28% and a Pearson correlation coefficient of 0.8

SUBMITTER: Chen C 

PROVIDER: S-EPMC8963690 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature