Predicting the antigenic evolution of SARS-COV-2 with deep learning

doi:10.1038/s41467-023-39199-6

	Predicting the antigenic evolution of SARS-COV-2 with deep learning
	Han, Wenkai 1,2; Chen, Ningning 1,2; Xu, Xinzhou 3,4; Sahil, Adil 1,2; Zhou, Juexiao 1,2; Li, Zhongxiao 1,2; Zhong, Huawen 2; Gao, Elva 5; Zhang, Ruochi 6; Wang, Yu 6; Sun, Shiwei 7,8; Cheung, Peter Pak-Hang 3,4; Gao, Xin 1,2
	2023-06-13
发表期刊	NATURE COMMUNICATIONS
卷号	14 期号:1 页码:14
摘要	The relentless evolution of SARS-CoV-2 poses a significant threat to public health, as it adapts to immune pressure from vaccines and natural infections. Gaining insights into potential antigenic changes is critical but challenging due to the vast sequence space. Here, we introduce the Machine Learning-guided Antigenic Evolution Prediction (MLAEP), which combines structure modeling, multi-task learning, and genetic algorithms to predict the viral fitness landscape and explore antigenic evolution via in silico directed evolution. By analyzing existing SARS-CoV-2 variants, MLAEP accurately infers variant order along antigenic evolutionary trajectories, correlating with corresponding sampling time. Our approach identified novel mutations in immunocompromised COVID-19 patients and emerging variants like XBB1.5. Additionally, MLAEP predictions were validated through in vitro neutralizing antibody binding assays, demonstrating that the predicted variants exhibited enhanced immune evasion. By profiling existing variants and predicting potential antigenic changes, MLAEP aids in vaccine development and enhances preparedness against future SARS-CoV-2 variants.
DOI	10.1038/s41467-023-39199-6
收录类别	SCI
语种	英语
资助项目	King Abdullah University of Science and Technology (KAUST)[FCC/1/1976-44-01] ; King Abdullah University of Science and Technology (KAUST)[FCC/1/1976-45-01] ; King Abdullah University of Science and Technology (KAUST)[URF/1/4663-01-01] ; King Abdullah University of Science and Technology (KAUST)[REI/1/5202-01-01] ; King Abdullah University of Science and Technology (KAUST)[REI/1/4940-01-01] ; King Abdullah University of Science and Technology (KAUST)[REI/1/5234-01-01] ; King Abdullah University of Science and Technology (KAUST)[REI/1/5414-01-01] ; King Abdullah University of Science and Technology (KAUST)[RGC/3/481601-01] ; University Grants Committee's Collaborative Research Fund[C6036-21GF] ; Chinese University of Hong Kong's Research Committee Research Fellowship
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:001061547600016
出版者	NATURE PORTFOLIO
引用统计	被引频次：22[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/21148
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Sun, Shiwei; Cheung, Peter Pak-Hang; Gao, Xin
作者单位	1.King Abdullah Univ Sci & Technol KAUST, Elect & Math Sci & Engn Div, Comp Sci Program, Comp, Thuwal 239556900, Saudi Arabia 2.King Abdullah Univ Sci & Technol KAUST, Computat Biosci Res Ctr, Thuwal 239556900, Saudi Arabia 3.Chinese Univ Hong Kong, Dept Chem Pathol, Fac Med, Hong Kong, Peoples R China 4.Chinese Univ Hong Kong, Li Ka Shing Inst Hlth Sci, Hong Kong, Peoples R China 5.King Abdullah Univ Sci & Technol KAUST, KAUST Sch, Thuwal 239556900, Saudi Arabia 6.Syneron Technol, Guangzhou 510000, Peoples R China 7.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 8.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Han, Wenkai,Chen, Ningning,Xu, Xinzhou,et al. Predicting the antigenic evolution of SARS-COV-2 with deep learning[J]. NATURE COMMUNICATIONS,2023,14(1):14.
APA	Han, Wenkai.,Chen, Ningning.,Xu, Xinzhou.,Sahil, Adil.,Zhou, Juexiao.,...&Gao, Xin.(2023).Predicting the antigenic evolution of SARS-COV-2 with deep learning.NATURE COMMUNICATIONS,14(1),14.
MLA	Han, Wenkai,et al."Predicting the antigenic evolution of SARS-COV-2 with deep learning".NATURE COMMUNICATIONS 14.1(2023):14.