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Related Concept Videos

Ligand Binding Sites02:40

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
Conserved Binding Sites01:49

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 analyses the...
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...

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Related Experiment Video

Updated: Jun 7, 2026

Detection of Human Leukocyte Antigen Biomarkers in Breast Cancer Utilizing Label-free Biosensor Technology
08:27

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Published on: March 24, 2015

HLAPepBinder: An Ensemble Model for The Prediction Of HLA-Peptide Binding.

Mahsa Saadat1, Fatemeh Zare-Mirakabad1, Ali Masoudi-Nejad2

  • 1Computational Biology Research Center (CBRC), Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran.

Iranian Journal of Biotechnology
|April 14, 2025
PubMed
Summary

HLAPepBinder, a novel consensus approach, integrates nine predictors using ensemble machine learning for accurate human leukocyte antigen (HLA)-peptide binding predictions. This method streamlines model selection and enhances immunotherapy development.

Keywords:
HLA class IHLA-peptide bindingImmunotherapy, Random ForestT cell epitope

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Analysis of HBV-Specific CD4 T-cell Responses and Identification of HLA-DR-Restricted CD4 T-Cell Epitopes Based on a Peptide Matrix

Published on: October 20, 2021

Area of Science:

  • Immunology and computational biology
  • Development of predictive models for immune responses

Background:

  • Human leukocyte antigens (HLAs) are crucial for immune response and cancer immunotherapy.
  • Accurate prediction of HLA-peptide binding is essential for identifying immunogenic epitopes and advancing personalized medicine.
  • Current challenges include selecting optimal prediction tools and integrating results from multiple predictors.

Purpose of the Study:

  • To develop a novel consensus approach for predicting HLA-peptide binding.
  • To address the limitations of existing methods in integrating multiple prediction tools.
  • To provide a streamlined and reliable solution for biologists in model selection.

Main Methods:

  • Developed HLAPepBinder, an automatic pipeline using ensemble machine learning (random forest).
  • Integrated predictions from nine HLA-peptide binding predictors.
  • Eliminated the need for manual model selection by combining predictor strengths.

Main Results:

  • HLAPepBinder outperforms traditional methods and complex deep learning models in prediction accuracy and resource efficiency.
  • Demonstrated superior performance compared to transformer-based models like TranspHLA.
  • Successfully identified 26 HLA-peptide pairs with high prediction confidence, validated through literature review, sequence analysis, and molecular docking.

Conclusions:

  • HLAPepBinder offers a practical and high-performing alternative for HLA-peptide binding predictions.
  • The model is accessible and effective even in limited computational environments.
  • Validated predictions contribute to advancements in immunotherapy and vaccine development.