论文标题
使用机器学习发现了针对新型Corona病毒发现的潜在中和抗体
Potential Neutralizing Antibodies Discovered for Novel Corona Virus Using Machine Learning
论文作者
论文摘要
在免疫系统可以产生抑制性抗体之前，快速且无法追踪的病毒突变会使成千上万的人生命。最近爆发的新型冠状病毒感染并杀死了世界上成千上万的人。在寻找可以抑制Covid-19的病毒表位的肽或抗体序列的快速方法将挽救数千种寿命。在本文中，我们设计了一个机器学习（ML）模型，以预测电晕病毒的可能抑制性合成抗体。我们收集了1933年的病毒抗体序列及其临床患者中和反应，并训练了ML模型以预测抗体反应。使用图形特征与多种ML方法，我们筛选了数千种假设抗体序列，并发现了8种稳定的抗体，这些抗体可能会抑制COVID-19。我们结合了生物信息学，结构生物学和分子动力学（MD）模拟，以验证可以抑制电晕病毒的候选抗体的稳定性。
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.
