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
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.