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

Autor:	Chen C; Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA., Boorla VS; Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA., Chowdhury R; Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA., Nissly RH; Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802, USA.; Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802, USA., Gontu A; Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802, USA.; Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802, USA., Chothe SK; Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802, USA.; Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802, USA., LaBella L; Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802, USA., Jakka P; Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802, USA.; Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802, USA., Ramasamy S; Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802, USA.; Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802, USA., Vandegrift KJ; Department of Biology, The Pennsylvania State University, University Park, PA 16802, USA.; Center for Infectious Disease Dynamics, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA., Nair MS; Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802, USA.; Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802, USA., Kuchipudi SV; Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802, USA.; Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802, USA.; Center for Infectious Disease Dynamics, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA., Maranas CD; Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA.
Jazyk:	angličtina
Zdroj:	BioRxiv : the preprint server for biology [bioRxiv] 2022 Mar 23. Date of Electronic Publication: 2022 Mar 23.
DOI:	10.1101/2022.03.22.485413
Abstrakt:	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 0.85 between predicted and experimentally calculated binding affinities in five-fold cross-validation tests and predicts improved binding affinity for most circulating variants. We pro-actively used the CNN model to exhaustively screen for novel RBD variants with combinations of up to four single amino acid substitutions and suggested candidates with the highest improvements in RBD-ACE2 binding affinity for human and animal ACE2 receptors. We found that the binding affinity of RBD variants against animal ACE2s follows similar trends as those against human ACE2. White-tailed deer ACE2 binds to RBD almost as tightly as human ACE2 while cattle, pig, and chicken ACE2s bind weakly. The model allows testing whether adaptation of the virus for increased binding with other animals would cause concomitant increases in binding with hACE2 or decreased fitness due to adaptation to other hosts. Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Competing financial interests The authors declare no competing financial interests.
Databáze:	MEDLINE
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