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Meng J, Liu J, Song W, Li H, Wang J, Zhang L, Peng Y, Wu A, Jiang T. PREDAC-CNN: predicting antigenic clusters of seasonal influenza A viruses with convolutional neural network. Brief Bioinform 2024; 25:bbae033. [PMID: 38343322 PMCID: PMC10859661 DOI: 10.1093/bib/bbae033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 01/13/2024] [Accepted: 01/18/2024] [Indexed: 02/15/2024] Open
Abstract
Vaccination stands as the most effective and economical strategy for prevention and control of influenza. The primary target of neutralizing antibodies is the surface antigen hemagglutinin (HA). However, ongoing mutations in the HA sequence result in antigenic drift. The success of a vaccine is contingent on its antigenic congruence with circulating strains. Thus, predicting antigenic variants and deducing antigenic clusters of influenza viruses are pivotal for recommendation of vaccine strains. The antigenicity of influenza A viruses is determined by the interplay of amino acids in the HA1 sequence. In this study, we exploit the ability of convolutional neural networks (CNNs) to extract spatial feature representations in the convolutional layers, which can discern interactions between amino acid sites. We introduce PREDAC-CNN, a model designed to track antigenic evolution of seasonal influenza A viruses. Accessible at http://predac-cnn.cloudna.cn, PREDAC-CNN formulates a spatially oriented representation of the HA1 sequence, optimized for the convolutional framework. It effectively probes interactions among amino acid sites in the HA1 sequence. Also, PREDAC-CNN focuses exclusively on physicochemical attributes crucial for the antigenicity of influenza viruses, thereby eliminating unnecessary amino acid embeddings. Together, PREDAC-CNN is adept at capturing interactions of amino acid sites within the HA1 sequence and examining the collective impact of point mutations on antigenic variation. Through 5-fold cross-validation and retrospective testing, PREDAC-CNN has shown superior performance in predicting antigenic variants compared to its counterparts. Additionally, PREDAC-CNN has been instrumental in identifying predominant antigenic clusters for A/H3N2 (1968-2023) and A/H1N1 (1977-2023) viruses, significantly aiding in vaccine strain recommendation.
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Affiliation(s)
- Jing Meng
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, Jiangsu, China
| | - Jingze Liu
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, Jiangsu, China
| | - Wenkai Song
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Honglei Li
- Beijing Cloudna Technology Company, Limited, Beijing 100029, China
| | | | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Yousong Peng
- College of Biology, Hunan University, Changsha 410082, China
| | - Aiping Wu
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, Jiangsu, China
| | - Taijiao Jiang
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, Jiangsu, China
- Guangzhou National Laboratory, Guangzhou 510005, China
- State Key Laboratory of Respiratory Disease, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, 510120, China
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Lu C, Cai Z, Zou Y, Zhang Z, Chen W, Deng L, Du X, Wu A, Yang L, Wang D, Shu Y, Jiang T, Peng Y. FluPhenotype-a one-stop platform for early warnings of the influenza A virus. Bioinformatics 2020; 36:3251-3253. [PMID: 32049310 DOI: 10.1093/bioinformatics/btaa083] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 01/06/2020] [Accepted: 02/04/2020] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Newly emerging influenza viruses keep challenging global public health. To evaluate the potential risk of the viruses, it is critical to rapidly determine the phenotypes of the viruses, including the antigenicity, host, virulence and drug resistance. RESULTS Here, we built FluPhenotype, a one-stop platform to rapidly determinate the phenotypes of the influenza A viruses. The input of FluPhenotype is the complete or partial genomic/protein sequences of the influenza A viruses. The output presents five types of information about the viruses: (i) sequence annotation including the gene and protein names as well as the open reading frames, (ii) potential hosts and human-adaptation-associated amino acid markers, (iii) antigenic and genetic relationships with the vaccine strains of different HA subtypes, (iv) mammalian virulence-related amino acid markers and (v) drug resistance-related amino acid markers. FluPhenotype will be a useful bioinformatic tool for surveillance and early warnings of the newly emerging influenza A viruses. AVAILABILITY AND IMPLEMENTATION It is publicly available from: http://www.computationalbiology.cn : 18888/IVEW. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Congyu Lu
- College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha 410082, China
| | - Zena Cai
- College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha 410082, China
| | | | - Zheng Zhang
- College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha 410082, China
| | - Wenjun Chen
- College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha 410082, China
| | - Lizong Deng
- Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005, China
- Suzhou Institute of Systems Medicine, Suzhou 215123, China
| | - Xiangjun Du
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangdong 510275, China
| | - Aiping Wu
- Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005, China
- Suzhou Institute of Systems Medicine, Suzhou 215123, China
| | - Lei Yang
- National Institute for Viral Disease Control and Prevention, China CDC, Beijing, China
| | - Dayan Wang
- National Institute for Viral Disease Control and Prevention, China CDC, Beijing, China
| | - Yuelong Shu
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangdong 510275, China
| | - Taijiao Jiang
- Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005, China
- Suzhou Institute of Systems Medicine, Suzhou 215123, China
| | - Yousong Peng
- College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha 410082, China
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Peng Y, Wu A, Meng J, Yang L, Wang D, Shu Y, Jiang T. Automated recommendation of the seasonal influenza vaccine strain with PREDAC. BIOSAFETY AND HEALTH 2020. [DOI: 10.1016/j.bsheal.2020.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
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PREDAV-H1: a user-friendly web server for predicting antigenic variants of influenza H1N1 viruses. SCIENCE CHINA. LIFE SCIENCES 2018; 62:426-427. [PMID: 30377901 DOI: 10.1007/s11427-018-9375-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Accepted: 08/15/2018] [Indexed: 10/28/2022]
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Morris DH, Gostic KM, Pompei S, Bedford T, Łuksza M, Neher RA, Grenfell BT, Lässig M, McCauley JW. Predictive Modeling of Influenza Shows the Promise of Applied Evolutionary Biology. Trends Microbiol 2018; 26:102-118. [PMID: 29097090 PMCID: PMC5830126 DOI: 10.1016/j.tim.2017.09.004] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Revised: 09/06/2017] [Accepted: 09/19/2017] [Indexed: 01/16/2023]
Abstract
Seasonal influenza is controlled through vaccination campaigns. Evolution of influenza virus antigens means that vaccines must be updated to match novel strains, and vaccine effectiveness depends on the ability of scientists to predict nearly a year in advance which influenza variants will dominate in upcoming seasons. In this review, we highlight a promising new surveillance tool: predictive models. Based on data-sharing and close collaboration between the World Health Organization and academic scientists, these models use surveillance data to make quantitative predictions regarding influenza evolution. Predictive models demonstrate the potential of applied evolutionary biology to improve public health and disease control. We review the state of influenza predictive modeling and discuss next steps and recommendations to ensure that these models deliver upon their considerable biomedical promise.
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Affiliation(s)
- Dylan H Morris
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.
| | - Katelyn M Gostic
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA, USA
| | - Simone Pompei
- Institute for Theoretical Physics, University of Cologne, Cologne, Germany
| | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Marta Łuksza
- Institute for Advanced Study, Princeton, NJ, USA
| | - Richard A Neher
- Biozentrum, University of Basel and Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA; Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Michael Lässig
- Institute for Theoretical Physics, University of Cologne, Cologne, Germany
| | - John W McCauley
- Worldwide Influenza Centre, Francis Crick Institute, London, UK
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