Multi-perspectives and challenges in identifying B-cell epitopes

被引:14
作者
Kumar, Nishant [1 ]
Bajiya, Nisha [1 ]
Patiyal, Sumeet [1 ]
Raghava, Gajendra P. S. [1 ,2 ]
机构
[1] Indraprastha Inst Informat Technol, Dept Computat Biol, New Delhi, India
[2] Indraprastha Inst Informat Technol, Dept Computat Biol, Near Govind Puri Metro Stn, A302 R&D Block,Phase 3,Okhla Ind Estate, New Delhi 110020, Delhi, India
关键词
B-cell epitope prediction; class-specific epitopes; conformational B-cell epitope; databases; deep learning; linear B-cell epitope; machine learning; EXCHANGE MASS-SPECTROMETRY; ANTIGEN-ANTIBODY COMPLEX; CONFORMATIONAL EPITOPES; LIQUID-CHROMATOGRAPHY; 3-DIMENSIONAL STRUCTURE; NEUTRALIZING ANTIBODY; MONOCLONAL-ANTIBODY; SPATIAL EPITOPE; 1ST STEP; PREDICTION;
D O I
10.1002/pro.4785
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
The identification of B-cell epitopes (BCEs) in antigens is a crucial step in developing recombinant vaccines or immunotherapies for various diseases. Over the past four decades, numerous in silico methods have been developed for predicting BCEs. However, existing reviews have only covered specific aspects, such as the progress in predicting conformational or linear BCEs. Therefore, in this paper, we have undertaken a systematic approach to provide a comprehensive review covering all aspects associated with the identification of BCEs. First, we have covered the experimental techniques developed over the years for identifying linear and conformational epitopes, including the limitations and challenges associated with these techniques. Second, we have briefly described the historical perspectives and resources that maintain experimentally validated information on BCEs. Third, we have extensively reviewed the computational methods developed for predicting conformational BCEs from the structure of the antigen, as well as the methods for predicting conformational epitopes from the sequence. Fourth, we have systematically reviewed the in silico methods developed in the last four decades for predicting linear or continuous BCEs. Finally, we have discussed the overall challenge of identifying continuous or conformational BCEs. In this review, we only listed major computational resources; a complete list with the URL is available from the BCinfo website ().
引用
收藏
页数:19
相关论文
共 150 条
[1]   Current approaches to fine mapping of antigen-antibody interactions [J].
Abbott, W. Mark ;
Damschroder, Melissa M. ;
Lowe, David C. .
IMMUNOLOGY, 2014, 142 (04) :526-535
[2]  
Ahmad T. A., 2016, Trials Vaccinol, V5, P71, DOI DOI 10.1016/J.TRIVAC.2016.04.003
[3]   LBCEPred: a machine learning model to predict linear B-cell epitopes [J].
Alghamdi, Wajdi ;
Attique, Muhammad ;
Alzahrani, Ebraheem ;
Ullah, Malik Zaka ;
Khan, Yaser Daanial .
BRIEFINGS IN BIOINFORMATICS, 2022, 23 (03)
[4]   Predictive estimation of protein linear epitopes by using the program PEOPLE [J].
Alix, AJP .
VACCINE, 1999, 18 (3-4) :311-314
[5]   3-DIMENSIONAL STRUCTURE OF AN ANTIGEN-ANTIBODY COMPLEX AT 2.8-A RESOLUTION [J].
AMIT, AG ;
MARIUZZA, RA ;
PHILLIPS, SEV ;
POLJAK, RJ .
SCIENCE, 1986, 233 (4765) :747-753
[6]   Prediction of residues in discontinuous B-cell epitopes using protein 3D structures [J].
Andersen, Pernille Haste ;
Nielsen, Morten ;
Lund, Ole .
PROTEIN SCIENCE, 2006, 15 (11) :2558-2567
[7]  
Ansari Hifzur Rahman, 2010, Immunome Res, V6, P6, DOI 10.1186/1745-7580-6-6
[8]   Integrated Serologic Surveillance of Population Immunity and Disease Transmission [J].
Arnold, Benjamin F. ;
Scobie, Heather M. ;
Priest, Jeffrey W. ;
Lammie, Patrick J. .
EMERGING INFECTIOUS DISEASES, 2018, 24 (07) :1188-1194
[9]   Organism-specific training improves performance of linear B-cell epitope prediction [J].
Ashford, Jodie ;
Reis-Cunha, Joao ;
Lobo, Igor ;
Lobo, Francisco ;
Campelo, Felipe .
BIOINFORMATICS, 2021, 37 (24) :4826-4834
[10]   PROPOSAL FOR NOMENCLATURE OF ANTIGENIC SITES IN PEPTIDES AND PROTEINS [J].
ATASSI, MZ ;
SMITH, JA .
IMMUNOCHEMISTRY, 1978, 15 (08) :609-610