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 the peptide to MHC is the most selective step,
so predicting the binding affinity of the peptide to MHC is the principal step in predicting T cell
epitopes. The 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 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 peptides and MHC.
Results:
Although the methods in predicting peptide-MHC binding based on the 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 into 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.
Towards an Ensemble Machine Learning Model of Random Subspace Based Functional Tree Classifier for Snow Avalanche Susceptibility Mapping
