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A review on the methods of peptide-MHC binding prediction

Author(s):

Yang Liu, Xia-hui Ouyang, Zhi-Xiong Xiao, Le Zhang and Yang Cao*   Pages 1 - 14 ( 14 )

Abstract:


Background: T lymphocyte achieves an immune response by recognizing antigen peptides (also known as T cell epitopes) through major histocompatibility complex (MHC) molecules. The immunogenicity of T cell epitopes depends on their source and stability in combination with MHC molecules. The binding of peptide to MHC is the most selective step, so predicting the binding affinity of peptide to MHC is the principal step in predicting T cell epitopes. Identification of epitopes is of great significance in the research of vaccine design and T cell immune response.

Objective: The traditional method for identifying epitopes is to synthesize and test the binding activity of peptide by experimental methods, which is not only time-consuming, but also expensive. In silico methods for predicting peptide-MHC binding emerge to pre-select candidate peptides for experimental testing, which greatly saves time and costs. By summarizing and analyzing these methods, we hope to have a better insight and provide guidance for the future directions.

Methods: Up to now, a number of methods have been developed to predict the binding ability of peptides to MHC based on various principles. Some of them employ matrix models or machine learning models based on the sequence characteristic embedded in peptides or MHC to predict the binding ability of peptides to MHC. Some others utilize the three-dimensional structural information of peptides or MHC, for example, by extracting three-dimensional structural information to construct a feature matrix or machine learning model, or directly using protein structure prediction, molecular docking to predict the binding mode of peptide and MHC.

Results: Although the methods in predicting peptide-MHC binding based on feature matrix or machine learning model can achieve high-throughput prediction, the accuracy of which depends heavily on the sequence characteristic of confirmed binding peptides. In addition, it cannot provide insights of the mechanism of antigen specificity. Therefore, such methods have certain limitations in practical applications. Methods in predicting peptide-MHC binding based on structural prediction or molecular docking are computationally intensive compared to the methods based on feature matrix or machine learning model and the challenge is how to predict a reliable structural model.

Conclusion: This paper reviews the principles, advantages and disadvantages of the methods of peptide-MHC binding prediction and discussed the future directions to achieve more accurate predictions.

Keywords:

Peptide, major histocompatibility complex (MHC), T cell epitope, prediction

Affiliation:

Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, Life Science and Engineering College, Northwest Minzhu University, Lanzhou 730030, Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, College of Computer Science, Sichuan University, Chengdu 610065, Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065



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