Potential Neutralizing Antibodies Discovered for Novel Corona Virus Using Machine Learning

Rishikesh Magar; Prakarsh Yadav; Amir Barati Farimani

With incredibly high infectivity and mortality rate, COVID-19 has become a global scare. 8, 9 To compound the problem, there are no proven therapeutics to aid the suffering patients 2, 8, [10] [11] [12] [13] [14] . Only viable treatment at the moment is symptomatic and there is a desperate need for developing therapeutics to counter COVID-19. Recently, the proteomics sequences of 'WH-Human 1' coronavirus became available through Metagenomic RNA sequencing of a patient in Wuhan. 4, 15 WH-Human 1 is 89.1% similar to a group of SARS-like coronaviruses. 4 With this sequence available, it is possible to find potential inhibitory antibodies by scanning thousands of antibody sequences and discovering the neutralizing ones [16] [17] [18] . However, this requires very expensive and time-consuming experimentation to discover the inhibitory responses to Corona virus in a timely manner. In addition, computational and physics-based models require the bound crystal structure of antibody-antigen complex, however; only a few of these structures have become available. 19, 20, 21, 22 In the case of COVID-19, the bound antigen-antibody crystal structure is not available to-date 23, 24 . Given this challenge and the fact that ML models require a large amount of data, the ML approach should rely on the sequences of the antibody-antigen rather than the crystal structures 25 .