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Updated: Oct 29, 2025

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Published on: March 25, 2014

Predicting MHC-peptide binding affinity by differential boundary tree.

Peiyuan Feng¹, Jianyang Zeng^1,2, Jianzhu Ma³

¹Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.

Bioinformatics (Oxford, England)

|July 12, 2021

Summary

This summary is machine-generated.

DBTpred accurately predicts peptide-MHC binding affinity, outperforming deep learning methods. This model aids neoantigen identification by highlighting critical residue mutations influencing binding.

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Stability and Structure of Bat Major Histocompatibility Complex Class I with Heterologous β2-Microglobulin

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Published on: March 10, 2021

Area of Science:

Immunoinformatics
Computational Biology
Genomics

Background:

Peptide-Major Histocompatibility Complex (MHC) binding prediction is crucial for neoantigen identification.
Existing computational methods often yield high false-positive rates.
Deep learning methods struggle to capture the impact of single residue mutations on binding affinity.

Purpose of the Study:

To develop a novel computational model for accurate prediction of peptide-MHC binding affinity.
To improve the identification of critical residue mutations affecting binding affinity.
To provide an interpretable model for understanding peptide-MHC interactions.

Main Methods:

Development of a differential boundary tree-based model named DBTpred.
Implementation of a parallel training algorithm to enhance computational efficiency.
Analysis of statistical properties of differential boundary trees and prediction paths.

Main Results:

DBTpred demonstrates superior accuracy in predicting MHC class I binding affinity compared to state-of-the-art deep learning methods.
The parallel training algorithm enables efficient application to large-scale datasets.
DBTpred offers intuitive interpretations, identifying key residue mutations that significantly alter binding affinity.

Conclusions:

DBTpred is an accurate and efficient tool for predicting peptide-MHC binding affinity.
The model provides valuable insights into the impact of mutations on binding, aiding neoantigen discovery.
DBTpred is freely available as a Python package for broader research application.