Citation: Chen, H.; Wang, J.; Liu, Y.; Ling, I.Q.E.; Shih, C.C.; Wu, D.; Fu, Z.; Lee, R.T.C.; Xu, M.; Chow,V.T.; et al. MADE: A Computational Tool for Predicting Vaccine Effectiveness for the Influenza A(H3N2) Virus Adapted to Embryonated Eggs. Vaccines 2022, 10, 907. https:// doi.org/10.3390/vaccines10060907 Academic Editor: Elena A. Govorkova Received: 27 April 2022 Accepted: 31 May 2022 Published: 6 June 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Article MADE: A Computational Tool for Predicting Vaccine Effectiveness for the Influenza A(H3N2) Virus Adapted to Embryonated Eggs Hui Chen 1,† , Junqiu Wang 2,3,† , Yunsong Liu 2,4,† , Ivy Quek Ee Ling 5 , Chih Chuan Shih 5 , Dafei Wu 2 , Zhiyan Fu 6 , Raphael Tze Chuen Lee 7 , Miao Xu 8 , Vincent T. Chow 9 , Sebastian Maurer-Stroh 7,10,11,12 , Da Zhou 3, *, Jianjun Liu 1,13, * and Weiwei Zhai 1,2,14, * 1 Human Genomics, Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore 138672, Singapore; chenh1@gis.a-star.edu.sg 2 Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; qiuqiu.wang@warwick.ac.uk (J.W.); liuyunsong@ioz.ac.cn (Y.L.); wudf@ioz.ac.cn (D.W.) 3 School of Mathematical Science, Xiamen University, Xiamen 361005, China 4 University of the Chinese Academy of Sciences, Beijing 100049, China 5 Bioinformatics Core, Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore 138672, Singapore; quekel@gis.a-star.edu.sg (I.Q.E.L.); shihcc@gis.a-star.edu.sg (C.C.S.) 6 IHiS—Integrated Health Information Systems, Singapore 554910, Singapore; zhiyan.fu@ihis.com.sg 7 Bioinformatics Institute, Agency for Science, Technology and Research, Singapore 138671, Singapore; leetc@bii.a-star.edu.sg (R.T.C.L.); sebastianms@bii.a-star.edu.sg (S.M.-S.) 8 State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China; xumiao@sysucc.org.cn 9 NUHS Infectious Diseases Translational Research Program, Department of Microbiology & Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117545, Singapore; micctk@nus.edu.sg 10 School of Biological Sciences (SBS), Nanyang Technological University (NTU), Singapore 637551, Singapore 11 National Public Health Laboratory (NPHL), Ministry of Health (MOH), Singapore 308442, Singapore 12 Department of Biological Sciences, National University of Singapore (NUS), Singapore 117543, Singapore 13 Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore 14 Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China * Correspondence: zhouda@xmu.edu.cn (D.Z.); liuj3@gis.a-star.edu.sg (J.L.); weiweizhai@ioz.ac.cn (W.Z.) † These authors contributed equally to this work. Abstract: Seasonal Influenza H3N2 virus poses a great threat to public health, but its vaccine efficacy remains suboptimal. One critical step in influenza vaccine production is the viral passage in embryonated eggs. Recently, the strength of egg passage adaptation was found to be rapidly increasing with time driven by convergent evolution at a set of functionally important codons in the hemagglutinin (HA1). In this study, we aim to take advantage of the negative correlation between egg passage adaptation and vaccine effectiveness (VE) and develop a computational tool for selecting the best candidate vaccine virus (CVV) for vaccine production. Using a probabilistic approach known as mutational mapping, we characterized the pattern of sequence evolution driven by egg passage adaptation and developed a new metric known as the adaptive distance (AD) which measures the overall strength of egg passage adaptation. We found that AD is negatively correlated with the influenza H3N2 vaccine effectiveness (VE) and ~75% of the variability in VE can be explained by AD. Based on these findings, we developed a computational package that can Measure the Adaptive Distance and predict vaccine Effectiveness (MADE). MADE provides a powerful tool for the community to calibrate the effect of egg passage adaptation and select more reliable strains with minimum egg-passaged changes as the seasonal A/H3N2 influenza vaccine. Vaccines 2022, 10, 907. https://doi.org/10.3390/vaccines10060907 https://www.mdpi.com/journal/vaccines