Article
Immunology
Tatiana I. I. Shashkova, Dmitriy Umerenkov, Mikhail Salnikov, Pavel V. V. Strashnov, Alina V. V. Konstantinova, Ivan Lebed, Dmitriy N. N. Shcherbinin, Marina N. N. Asatryan, Olga L. L. Kardymon, Nikita V. V. Ivanisenko
Summary: This paper presents the development of a model for predicting conformational B-cell epitopes using pretrained deep learning models. The model, called SEMA, achieved the best performance on an independent test set and can quantitatively rank immunodominant regions.
FRONTIERS IN IMMUNOLOGY
(2022)
Article
Biochemical Research Methods
Emmi Jokinen, Jani Huuhtanen, Satu Mustjoki, Markus Heinonen, Harri Lahdesmaki
Summary: TCRGP is a novel computational method that predicts TCR recognition of specific epitopes with higher accuracy than existing methods. By quantifying epitope-specific TCRs and identifying HBV-epitope specific T cells in hepatocellular carcinoma patients, TCRGP offers a valuable tool for analyzing publicly available TCR data.
PLOS COMPUTATIONAL BIOLOGY
(2021)
Article
Multidisciplinary Sciences
A. C. Gomes, I. A. Baraniak, A. Lankina, Z. Moulder, P. Holenya, C. Atkinson, G. Tang, T. Mahungu, F. Kern, P. D. Griffiths, M. B. Reeves
Summary: Vaccination against CMV infection is highly prioritized, and a recombinant form of gB protein with MF59 adjuvant has shown partial protection in a clinical trial. However, neutralizing responses against known antigenic domains of gB were limited. We discovered that vaccination induces an antibody response against a region of gB called AD-6, which is detected in a majority of vaccine recipients but a minority of naturally infected individuals. The AD-6 antibody binds to gB and infected cells, preventing cell-cell spread of CMV but not directly neutralizing the virus. This finding has the potential to explain part of the protection provided by gB vaccines against CMV following transplantation.
NATURE COMMUNICATIONS
(2023)
Article
Cell Biology
Catherine Riou, Roanne Keeton, Thandeka Moyo-Gwete, Tandile Hermanus, Prudence Kgagudi, Richard Baguma, Ziyaad Valley-Omar, Mikhail Smith, Houriiyah Tegally, Deelan Doolabh, Arash Iranzadeh, Lynn Tyers, Hygon Mutavhatsindi, Marius B. Tincho, Ntombi Benede, Gert Marais, Lionel R. Chinhoyi, Mathilda Mennen, Sango Skelem, Elsa du Bruyn, Cari Stek, Tulio de Oliveira, Carolyn Williamson, Penny L. Moore, Robert J. Wilkinson, Ntobeko A. B. Ntusi, Wendy A. Burgers
Summary: This study evaluated neutralizing antibody and T cell responses in patients infected with the Beta variant and those infected before its emergence. It found that CD4 and CD8 T cell responses to Beta were preserved overall, despite loss of recognition of immunogenic CD4 epitopes.
SCIENCE TRANSLATIONAL MEDICINE
(2022)
Article
Microbiology
Jian Li, Junfang Yan, Yanni Gao, Xing Liu, Haifeng Sun, Juan Bai, Ping Jiang
Summary: In this study, monoclonal antibodies specific to SVA 3AB or 3C proteins were generated and their corresponding B-cell epitopes were identified. These epitopes are conserved in different SVA strains and are exposed on the surface of 3AB or 3C proteins, potentially serving as important targets for detection methods and vaccine development.
VETERINARY MICROBIOLOGY
(2023)
Article
Food Science & Technology
Ankita Mishra, Ashok Kumar
Summary: Nonspecific lipid transfer proteins (nsLTPs) are panallergens found widely across different plants. This study used in-silico methods to identify B-cell epitopes for LTPs from various legumes, showing significant homology between chickpea and mung-bean LTPs and other allergenic LTPs. The findings suggest potential cross-reactivity among LTPs from different plant sources based on shared B-cell epitopes.
Article
Immunology
Xin Zhang, Shuli Sang, Qing Guan, Haoxia Tao, Yanchun Wang, Chunjie Liu
Summary: In this study, HspA from Helicobacter pylori was truncated to identify antigen immunodominant peptides. Two novel B-cell epitopes were identified and their antigenicity and immunogenicity were verified. These epitopes could be potential targets for the diagnosis, treatment, and immune prevention of H. pylori infection.
Article
Biochemical Research Methods
Emmi Jokinen, Alexandru Dumitrescu, Jani Huuhtanen, Vladimir Gligorijevic, Satu Mustjoki, Richard Bonneau, Markus Heinonen, Harri Lahdesmaki
Summary: T cells use T cell receptors (TCRs) to recognize epitopes presented by major histocompatibility complexes and initiate an immune response through activation and proliferation. The TCRconv deep learning model accurately predicts recognition between TCRs and epitopes, providing insight into T cell dynamics and phenotypes in diseases such as COVID-19.
Article
Biochemistry & Molecular Biology
Pan Wang, Peiyang Ding, Qiang Wei, Hongliang Liu, Yunchao Liu, Qingmei Li, Yunrui Xing, Ge Li, Enmin Zhou, Gaiping Zhang
Summary: This study reports the generation and characterization of monoclonal antibodies against the spike receptor-binding domain (RBD) of MERS-CoV. Two novel linear epitopes were identified on the outermost surface of RBD. The findings may contribute to a better understanding of RBD's function and the development of diagnostic methods.
INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES
(2022)
Article
Virology
Chi-Hua Tung, Yi-Sheng Chang, Kai-Po Chang, Yen-Wei Chu
Summary: NIgPred is a prediction tool developed to predict specific antibody epitopes by integrating various features and utilizing machine-learning approaches, with a focus on enhancing prediction accuracy through considering peptide-characteristic correlation and autocorrelation features. The tool outperformed currently available tools in predicting IgE and IgG epitopes, achieving a 100% prediction accuracy for IgG epitopes on a coronavirus dataset.
Article
Biochemistry & Molecular Biology
Geyan Liu, Kang Wang, Zhen Yang, Xiaoyu Tang, Yungfu Chang, Ke Dai, Xinwei Tang, Bangdi Hu, Yiwen Zhang, Sanjie Cao, Xiaobo Huang, Qigui Yan, Rui Wu, Qin Zhao, Senyan Du, Xintian Wen, Yiping Wen
Summary: In this study, three monoclonal antibodies 5D11, 2H81, and 4F2 against recombinant HbpA were generated. Among them, 5D11 showed strong binding affinity and can potentially be used for serological diagnostic tools for G. parasuis.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Microbiology
Yue Qi, Peijie Zheng, Guohua Huang
Summary: This article introduces a deep learning method called DeepLBCEPred for predicting linear B-cell epitopes, and demonstrates its superior performance through empirical experiments. The authors have also developed a user-friendly web application for linear BCEs prediction, which is freely available for all scientific researchers.
FRONTIERS IN MICROBIOLOGY
(2023)
Article
Immunology
Shuqin Gu, Zhipeng Liu, Li Lin, Shihong Zhong, Yanchen Ma, Xiaoyi Li, Guofu Ye, Chunhua Wen, Yongyin Li, Libo Tang
Summary: Identification of functional cure-related B-cell linear epitopes of chronic HBV infection provides potential vaccine candidates to elicit neutralizing antibodies for treating HBV infection.
FRONTIERS IN IMMUNOLOGY
(2021)
Article
Biochemical Research Methods
Wajdi Alghamdi, Muhammad Attique, Ebraheem Alzahrani, Malik Zaka Ullah, Yaser Daanial Khan
Summary: The identification of B-cell epitopes is crucial for therapeutics, vaccine development, and antibody production. Experimental approaches are challenging and time-consuming, leading to the development of computational methods. LBCEPred, a python-based web-tool, outperforms existing sequence-based models in predicting B-cell epitopes.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Yuansong Zeng, Zhuoyi Wei, Qianmu Yuan, Sheng Chen, Weijiang Yu, Yutong Lu, Jianzhao Gao, Yuedong Yang
Summary: Drawing on the breakthrough of AlphaFold2 in protein structure prediction, we propose a novel graph-based model, GraphBepi, for accurate B-cell epitope prediction. By utilizing the predicted structure from AlphaFold2, GraphBepi constructs the protein graph and captures both sequence and spatial information through edge-enhanced deep graph neural networks (EGNN) and bidirectional long short-term memory neural networks (BiLSTM). The combined representations are input into a multilayer perceptron to predict B-cell epitopes. Comprehensive tests demonstrate that GraphBepi outperforms state-of-the-art methods in terms of AUC and AUPR.
