Potential Neutralizing Antibodies Discovered for Novel Corona Virus Using Machine Learning

Rishikesh Magar; Prakarsh Yadav; Amir Barati Farimani

The majority of the data in the training set is composed of HIV antibody-antigen complex (1887 samples). Most of the samples for the HIV training set were obtained from the Compile, Analyze and Tally NAb panels (CATNAP) database from the Los Alamos National Laboratory (LANL) 28, 29 . From CATNAP, data was collected for monoclonal antibodies, 2F5, 4E10 and 10E8, which bind with GP41 30-32 . Using CATNAP's functionality for identifying epitope alignment, we selected FASTA sequence of the antigen corresponding to the site of alignment, in the antibody. We To make the dataset more diverse and train a more robust ML model, we included more available antibody-antigen sequences and their neutralization potential. To do this, we compiled the sequences of Influenza, Dengue, Ebola, SARS, Hepatitis, etc. 26,33-86 by searching the keywords of "virus, antibody" on RCSB server 87 and selected the neutralizing complex by reading their corresponding publications. Furthermore, for each neutralizing complex, the contact residues at the interface of antibody and antigen were selected. To select the antigen contact sequences, all amino acids within 5Ã… of corresponding antibody were chosen. (Supporting Information) To select the antibody contact sequences, all amino acids within 5Ã… of the antigen were chosen. In total, 102 sequences of antibody-antigen complexes were mined and added to the 1831 samples, resulting in total number of 1933 training samples.