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Conserved Binding Sites

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...

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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

SVM and SVR-based MHC-binding prediction using a mathematical presentation of peptide sequences.

Davorka R Jandrlić¹

¹University of Belgrade, Faculty of Mechanical Engineering, Kraljice Marije 16, Belgrade, Serbia.

Computational Biology and Chemistry

|November 7, 2016

Summary

This study introduces a novel approach for predicting major histocompatibility complex (MHC)-binding peptides. The new method enhances accuracy by incorporating amino acid properties, improving T-cell epitope prediction for vaccine development.

Keywords:

Data mining Encoding scheme MHC I binding prediction Support vector machine T-cell epitope

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Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis

Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis

Published on: October 15, 2021

Area of Science:

Immunology and Bioinformatics
Computational Biology
Vaccine Development

Background:

Accurate prediction of T-cell epitopes and major histocompatibility complex (MHC)-binding peptides is crucial for understanding immune function and developing peptide-based vaccines.
Existing prediction methods have limitations, necessitating the development of more reliable and accurate models.
Peptide binding to MHC molecules is a critical and selective step in T-cell epitope identification.

Purpose of the Study:

To present a novel approach for predicting MHC-binding ligands with improved accuracy.
To develop models for both quantitative and qualitative prediction of MHC-binding ligands.
To evaluate the contribution of amino acid properties to peptide-binding affinity.

Main Methods:

Developed predictive models using support vector machine (SVM) and support vector regression (SVR).
Incorporated novel weighting schemes for amino acid frequencies, BLOSUM and VOGG substitutions, and physicochemical/molecular properties.
Utilized feature selection, various encoding, and weighting schemes for peptide analysis.

Main Results:

The developed models demonstrated performance comparable to, and in some cases exceeding, existing state-of-the-art predictors.
Physicochemical and molecular properties of amino acids were found to significantly influence peptide-binding affinity.
The new approach offers enhanced reliability for predicting MHC-binding peptides.

Conclusions:

The novel approach significantly improves the prediction of MHC-binding ligands.
The integration of amino acid properties enhances the accuracy of T-cell epitope prediction.
This advancement is vital for the future development of effective peptide-based vaccines.