报告题目：Deep learning based on biologically interpretable genome representation predicts two types of human adaptation of SARS-CoV-2 variants
报 告 人：李靖 副研究员 中国人民解放军军事科学院军事医学研究院
报告时间：2022年6月1日 上午 9:30-10:30
报告地点：腾讯会议 ID：332-771-131
或点击链接直接加入会议：https://meeting.tencent.com/dm/J4Po8ukf8PFj
校内联系人：赵世舜 zhaoss@jlu.edu.cn
报告摘要：Explosively emerging SARS-CoV-2 variants challenge current nomenclature schemes based on genetic diversity and biological significance. Genomic composition-based machine learning methods have recently performed well in identifying phenotype– genotype relationships. We introduced a framework involving dinucleotide (DNT) composition representation (DCR) to parse the general human adaptation of RNA viruses and applied a three-dimensional convolutional neural network (3D CNN) analysis to learn the human adaptation of other existing coronaviruses (CoVs) and predict the adaptation of SARS-CoV-2 variants of concern (VOCs). A markedly separable, linear DCR distribution was observed in two major genes, receptor-binding glycoprotein and RNA-dependent RNA polymerase (RdRp) of six families of single-stranded (ssRNA) viruses. Additionally, there was a general host-specific distribution of both the spike proteins and RdRps of CoVs. The 3D CNN based on spike DCR predicted a dominant type II adaptation of most Beta, Delta and Omicron VOCs, with high transmissibility and low pathogenicity. Type I adaptation with opposite transmissibility and pathogenicity was predicted for SARS-CoV-2 Alpha VOCs (77%) and Kappa variants of interest (58%). The identified adaptive determinants included D1118H and A570D mutations and local DNTs. Thus, the 3D CNN model based on DCR features predicts SARS- CoV-2, a major type II human adaptation and is qualified to predict variant adaptation in real time, facilitating the risk-assessment of emerging SARS-CoV-2 variants and COVID-19 control.
报告人简介：李靖 中国人民解放军军事科学院军事医学研究院, 五所, 副研究员，主要从事病毒宿主适应性的“AI计算预测+BIO实验验证”研究。2003年毕业于内蒙古医科大学临床医学系，获学士学位；2008年毕业于中国人民解放军军事科学院军事医学研究院(原军事医学科学院), 微生物学专业, 获博士学位。作为负责人，在研或完成国家自然科学基金课题2项，国家重点研发、国家传染病重大专项课题及军队课题或子课题等6项。累计发表SCI论文28篇，流感病毒宿主适应性机器学习预测文章在顶级生物进化期刊Molecular Biology and Evolution封面、头条（2020第四期头条、2020第5期封面）发表，新冠病毒宿主适应性深度学习预测文章在顶级进化期刊Briefings in Bioinformatics和病毒学专业期刊Viruses发表。获得专利8项，参与研究获军队科技进步一等奖2项。
