A method for predicting linear and conformational B-cell epitopes in an antigen from its primary sequence

表位 序列(生物学) 抗原 相似性(几何) 计算生物学 人工智能 B细胞 计算机科学 模式识别(心理学) 抗体 免疫学 生物 化学 生物化学 图像(数学)
作者
Nishant Kumar,Sadhana Tripathi,Neelam Sharma,Sumeet Patiyal,Leimarembi Devi Naorem,Gajendra P. S. Raghava
出处
期刊:Computers in Biology and Medicine [Elsevier BV]
卷期号:170: 108083-108083
标识
DOI:10.1016/j.compbiomed.2024.108083
摘要

B-cell is an essential component of the immune system that plays a vital role in providing the immune response against any pathogenic infection by producing antibodies. Existing methods either predict linear or conformational B-cell epitopes in an antigen. In this study, a single method was developed for predicting both types (linear/conformational) of B-cell epitopes. The dataset used in this study contains 3875 B-cell epitopes and 3996 non-B-cell epitopes, where B-cell epitopes consist of both linear and conformational B-cell epitopes. Our primary analysis indicates that certain residues (like Asp, Glu, Lys, and Asn) are more prominent in B-cell epitopes. We developed machine-learning based methods using different types of sequence composition and achieved the highest AUROC of 0.80 using dipeptide composition. In addition, models were developed on selected features, but no further improvement was observed. Our similarity-based method implemented using BLAST shows a high probability of correct prediction with poor sensitivity. Finally, we developed a hybrid model that combines alignment-free (dipeptide based random forest model) and alignment-based (BLAST-based similarity) models. Our hybrid model attained a maximum AUROC of 0.83 with an MCC of 0.49 on the independent dataset. Our hybrid model performs better than existing methods on an independent dataset used in this study. All models were trained and tested on 80 % of the data using a cross-validation technique, and the final model was evaluated on 20 % of the data, called an independent or validation dataset. A webserver and standalone package named "CLBTope" has been developed for predicting, designing, and scanning B-cell epitopes in an antigen sequence available at (https://webs.iiitd.edu.in/raghava/clbtope/).
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