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Related Concept Videos

Antigens Involved in Adaptive Immunity01:26

Antigens Involved in Adaptive Immunity

An antigen is any substance the immune system identifies as foreign and potentially harmful to the body, prompting an immune response. Antigens have two functional properties: immunogenicity and reactivity. Immunogenicity is the ability of an antigen to stimulate a specific immune response. At the same time, reactivity describes the antigen's ability to react with the cells and antibodies produced in response to it.
Complete Antigens
Complete antigens possess both immunogenicity and reactivity.
Antigen Processing Pathways01:31

Antigen Processing Pathways

MHC molecules are key players in the immune response, enabling T cells to recognize and respond to specific antigens. They are present on the surface of all nucleated cells in the body and are instrumental in presenting antigens to T cells and activating them. T cells recognize the MHC-antigen complex and initiate an immune response. MHC class I and MHC class II are two main types of MHC molecules, each associated with a distinct antigen processing pathway.
MHC Class I: Presenting Endogenous...
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...
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...
Protein-protein Interfaces02:04

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

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Related Experiment Video

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A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes
07:59

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

Published on: March 25, 2014

Optimally-connected hidden markov models for predicting MHC-binding peptides.

Chenhong Zhang1, Mikelis G Bickis, Fang-Xiang Wu

  • 1Department of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5C9, Canada. chz155@mail.usask.ca

Journal of Bioinformatics and Computational Biology
|November 14, 2006
PubMed
Summary

Optimally-connected Hidden Markov Models (ocHMMs) offer improved prediction of major histocompatibility complex (MHC) binding peptides. This novel method balances computational efficiency with pattern recognition, outperforming existing profile HMM approaches.

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Stability and Structure of Bat Major Histocompatibility Complex Class I with Heterologous β2-Microglobulin
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A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes
07:59

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Published on: March 25, 2014

Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis
09:32

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

Published on: October 15, 2021

Stability and Structure of Bat Major Histocompatibility Complex Class I with Heterologous β2-Microglobulin
11:17

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

Published on: March 10, 2021

Area of Science:

  • Computational biology
  • Immunoinformatics
  • Machine learning for bioinformatics

Background:

  • Hidden Markov Models (HMMs) are used for predicting major histocompatibility complex (MHC) binding peptides.
  • Fully-connected HMMs (fcHMMs) offer high prediction potential but require intensive computation.
  • Profile HMMs (pHMMs) are computationally efficient but may miss patterns by merging states.

Purpose of the Study:

  • To introduce optimally-connected HMMs (ocHMMs) as a method for MHC-binding peptide prediction.
  • To address limitations of fcHMMs and pHMMs in terms of computational cost and pattern recognition.
  • To develop a novel initialization strategy for ocHMMs using multiple property grouping.

Main Methods:

  • Proposed optimally-connected HMMs (ocHMMs) with reduced connectivity compared to fcHMMs.
  • Introduced a novel 'multiple property grouping' approach for initializing ocHMM parameters.
  • Compared ocHMM performance against a pHMM implementation (HMMER) on specific MHC alleles (HLA-A*0201, HLA-B*3501).

Main Results:

  • ocHMMs avoid merging overlapping patterns, unlike pHMMs.
  • Topological reductions in ocHMMs significantly decrease model connectivity.
  • Adjustable heuristic approaches allowed ocHMMs to achieve higher predictive accuracy than HMMER.

Conclusions:

  • ocHMMs present a promising alternative for MHC-binding peptide prediction.
  • The 'multiple property grouping' initialization enhances ocHMM performance.
  • ocHMMs demonstrate superior predictive accuracy and efficiency compared to existing HMM methods.