Author: Rishikesh Magar; Prakarsh Yadav; Amir Barati Farimani
Title: Potential Neutralizing Antibodies Discovered for Novel Corona Virus Using Machine Learning Document date: 2020_3_20
ID: fn7l93wh_14
Snippet: In order to find potential antibody candidates for COVID-19, 2589 different mutant strains of antibody sequences were generated based on the sequence of SARS neutralizing antibodies. The reason we selected these antibodies as initial scaffolds is that the genome of COVID-19 4 is 79.8% identical to "Tor2" isolate of SARS (Accession number: AY274119) 96 The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.....
Document: In order to find potential antibody candidates for COVID-19, 2589 different mutant strains of antibody sequences were generated based on the sequence of SARS neutralizing antibodies. The reason we selected these antibodies as initial scaffolds is that the genome of COVID-19 4 is 79.8% identical to "Tor2" isolate of SARS (Accession number: AY274119) 96 The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.03.14.992156 doi: bioRxiv preprint the amino acids in the binding region of antibody. (see Supporting Information for COVID-19 antigen and antibody interactions) To find out the binding region of these antibodies for sequence generation, all amino acids within 5Ã… of their respective antigen were chosen. To assess the biological feasibility of these mutant sequences, we scored each mutation by using the BLOSUM62 matrix 97 .
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