報告題目：Deep learning based on biologically interpretable genome representation predicts two types of human adaptation of SARS-CoV-2 variants
報 告 人：李靖 副研究員 中國人民解放軍軍事科學院軍事醫學研究院
報告時間：2022年6月1日 上午 9:30-10:30
報告地點：騰訊會議 ID：332-771-131
或點擊鍊接直接加入會議：https://meeting.tencent.com/dm/J4Po8ukf8PFj
校内聯系人：趙世舜 zhaoss@jlu.edu.cn
報告摘要：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.
報告人簡介：李靖 中國人民解放軍軍事科學院軍事醫學研究院, 五所, 副研究員，主要從事病毒宿主适應性的“AI計算預測+BIO實驗驗證”研究。2003年畢業于内蒙古醫科大學臨床醫學系，獲學士學位；2008年畢業于中國人民解放軍軍事科學院軍事醫學研究院(原軍事醫學科學院), 微生物學專業, 獲博士學位。作為負責人，在研或完成國家自然科學基金課題2項，國家重點研發、國家傳染病重大專項課題及軍隊課題或子課題等6項。累計發表SCI論文28篇，流感病毒宿主适應性機器學習預測文章在頂級生物進化期刊Molecular Biology and Evolution封面、頭條（2020第四期頭條、2020第5期封面）發表，新冠病毒宿主适應性深度學習預測文章在頂級進化期刊Briefings in Bioinformatics和病毒學專業期刊Viruses發表。獲得專利8項，參與研究獲軍隊科技進步一等獎2項。
