利用机器学习发现新型冠状病毒的潜在中和抗体
Potential Neutralizing Antibodies Discovered for Novel Corona Virus Using Machine Learning
摘要：
在免疫系统产生抑制性抗体之前，快速和不可追踪的病毒突变夺走了成千上万人的生命。最近爆发的新型冠状病毒感染并导致全球数千人死亡。快速找到可以抑制COVID-19病毒表位的肽或抗体序列的方法将挽救数千人的生命。在本文中，我们设计了一个机器学习（ML）模型来预测可能的抑制冠状病毒的合成抗体。我们收集了1933个病毒抗体序列及其临床患者中和反应，并训练了一个ML模型来预测抗体反应。使用各种ML方法的图特征化，我们筛选了数千个假设的抗体序列，并发现了8种可能抑制COVID-19的稳定抗体。我们结合生物信息学、结构生物学和分子动力学（MD）模拟来验证能够抑制冠状病毒的候选抗体的稳定性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
