Potential Neutralizing Antibodies Discovered for Novel Corona Virus Using Machine Learning
This work addresses the urgent need for rapid antibody discovery to combat COVID-19, representing an incremental application of existing ML methods to a new dataset.
The authors tackled the problem of rapidly identifying inhibitory antibodies for COVID-19 by developing a machine learning model that predicted 8 stable antibodies from thousands of hypothetical sequences, verified through bioinformatics and simulations.
The fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody. Recent outbreak of novel coronavirus infected and killed thousands of people in the world. Rapid methods in finding peptides or antibody sequences that can inhibit the viral epitopes of COVID-19 will save the life of thousands. In this paper, we devised a machine learning (ML) model to predict the possible inhibitory synthetic antibodies for Corona virus. We collected 1933 virus-antibody sequences and their clinical patient neutralization response and trained an ML model to predict the antibody response. Using graph featurization with variety of ML methods, we screened thousands of hypothetical antibody sequences and found 8 stable antibodies that potentially inhibit COVID-19. We combined bioinformatics, structural biology, and Molecular Dynamics (MD) simulations to verify the stability of the candidate antibodies that can inhibit the Corona virus.