Predicting the antigenic evolution of SARS-COV-2 with deep learning
文献类型：期刊论文
| 作者 | Han, Wenkai2,3; Chen, Ningning2,3; Xu, Xinzhou4,5; Sahil, Adil2,3; Zhou, Juexiao2,3; Li, Zhongxiao2,3; Zhong, Huawen3; Gao, Elva1; Zhang, Ruochi6; Wang, Yu6 |
| 刊名 | NATURE COMMUNICATIONS
 |
| 出版日期 | 2023-06-13 |
| 卷号 | 14期号:1页码:14 |
| DOI | 10.1038/s41467-023-39199-6 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| 出版者 | NATURE PORTFOLIO |
| WOS记录号 | WOS:001061547600016 |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/21147]  |
| 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, KAUST Sch, Thuwal 239556900, Saudi Arabia 2.King Abdullah Univ Sci & Technol KAUST, Elect & Math Sci & Engn Div, Comp Sci Program, Comp, Thuwal 239556900, Saudi Arabia 3.King Abdullah Univ Sci & Technol KAUST, Computat Biosci Res Ctr, Thuwal 239556900, Saudi Arabia 4.Chinese Univ Hong Kong, Dept Chem Pathol, Fac Med, Hong Kong, Peoples R China 5.Chinese Univ Hong Kong, Li Ka Shing Inst Hlth Sci, Hong Kong, Peoples R China 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. |
入库方式： OAI收割
来源：计算技术研究所
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