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Researchers Developed a Computational Approach to Pinpoint the Epitopes of COVID-19 RBD Domain
Date and Time: 2021-06-11 17:31:40

Antibodies play an important role in clinical research and biotechnology, with their specificity determined by the interaction with the antigen’s epitope region, as a special type of protein-protein interaction (PPI) interface. Predicting epitopes using computational approaches is increasingly an important task to aid the time-consuming wet-lab experiments. On May 11th, Hou Qingzhen, an associate professor from the Department of Public Health has published a paper entitled "SeRenDIP-CE: Sequence-based Interface Prediction for Conformational Epitopes" in Bioinformatics which is a leading journal in computational biology field. Hou Qingzhen said the ubiquitous availability of protein sequence data allows us to predict epitopes from sequence in order to focus expensive wet-lab work toward the most promising epitope regions. This research extends sequence-based predictors previously developed by Hou for homodimer and heterodimer PPI interfaces to predict epitope residues that have the potential to bind an antibody. The predictor has been successfully applied to the epitope prediction of the COVID-19 RBD domain. They also provide an epitope prediction web server, which is simple to use and only requires a single antigen sequence as input, which will help make the method immediately applicable in a wide range of biomedical and biomolecular research.




As the head of the Bioinformatics Center of the National Institute of Health Data Science of China, Hou has dedicated himself to applying bioinformatics methods to the biomedical big data field and exploring the relationship between the structure and function of biological macromolecules. In the past five years, his research has been published in the journals of Bioinformatics multiple times, including antigen epitope prediction (Bioinformatics,2021, btab321), protein folding and stability study (Bioinformatics,2021,btab034), solubility prediction (Bioinformatics,2020,btz773) and protein interaction site study (Bioinformatics,2017,btx005; Bioinformatics,2019,btz428).

This study was supported by the Young Scholars Program of Shandong University (21320082064101).

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