Class II HLA-peptide binding prediction using structural principles

Arumugam, Mohanapriya; Lulu, Sajitha; Kayathri, Rajarathinam; Kangueane, Pandjassarame

doi:10.1016/j.humimm.2008.12.012

Cited by 11 publications

(9 citation statements)

References 30 publications

(50 reference statements)

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“…In response to these limitations, Zhao et al [88] and Schueler-Furman [89] developed novel knowledge-based potential matrices, which allowed to predict peptide binding to most MHCI molecules using to peptide threading. Recently, Mohanapriya et al [90] also used peptide threading to predict peptide binding to MHCII molecules. Docking or computer-simulated ligand binding has also been used to predict MHC-peptide binding.…”

Section: Modeling-based Methodsmentioning

confidence: 99%

Prediction of MHC-Peptide Binding: A Systematic and Comprehensive Overview

Lafuente¹,

Reche

2009

CPD

133

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T cell immune responses are driven by the recognition of peptide antigens (T cell epitopes) that are bound to major histocompatibility complex (MHC) molecules. T cell epitope immunogenicity is thus contingent on several events, including appropriate and effective processing of the peptide from its protein source, stable peptide binding to the MHC molecule, and recognition of the MHC-bound peptide by the T cell receptor. Of these three hallmarks, MHC-peptide binding is the most selective event that determines T cell epitopes. Therefore, prediction of MHC-peptide binding constitutes the principal basis for anticipating potential T cell epitopes. The tremendous relevance of epitope identification in vaccine design and in the monitoring of T cell responses has spurred the development of many computational methods for predicting MHC-peptide binding that improve the efficiency and economics of T cell epitope identification. In this report, we will systematically examine the available methods for predicting MHC-peptide binding and discuss their most relevant advantages and drawbacks.

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Section: Modeling-based Methodsmentioning

confidence: 99%

Prediction of MHC-Peptide Binding: A Systematic and Comprehensive Overview

Lafuente¹,

Reche

2009

CPD

133

View full text Add to dashboard Cite

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“…It accommodates peptides of length 12-35 [13]. The peptides bound to the groove have an extended conformation in class II unlike class I.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, the peptide binding specificity is determined by the side chains influenced by polymorphism of the MHC alleles. Mohanapriya and colleagues (2009) defined virtual pockets using HERP extracted from HLA-peptide structural complexes [13]. The hypothesis is that the 25 residues at the HERP forming the virtual pockets are deterministic of peptide binding and its specificity.…”

Section: Discussionmentioning

confidence: 99%

“…These virtual binding pockets accommodate the side chains of amino acid residues of antigenic peptides. Mohanapriya and colleagues (2009) defined nine virtual pockets using 15 non-redundant class II HLApeptide crystal structures [13]. The HLA amino acid residues lying in these virtual binding pockets show polymorphism which determines the specificity and sensitivity.…”

Section: Methodsmentioning

confidence: 99%

“…The HLA amino acid residues lying in these virtual binding pockets show polymorphism which determines the specificity and sensitivity. These residue positions forming the virtual pockets are called highly essential residue positions (HERP) as defined elsewhere by Mohanapriya and colleagues (2009) [13]. The number of HERP defined for the beta chain of class II molecules is twenty-five.…”

Section: Methodsmentioning

confidence: 99%

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A HLA-DRB supertype chart with potential overlapping peptide binding function

Arumugam¹,

Nandagond²,

Shapshak³

et al. 2010

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HLA-DRB alleles are class II alleles that are associated with CD4+ T-cell immune response. DRB alleles are polymorphic and currently there are about 622 named in the IMGT/HLA sequence database. Each allele binds short peptides with high sensitivity and specificity. However, it has been suggested that majority of HLA alleles can be covered within few HLA supertypes, where different members of a supertype bind similar peptides showing distinct repertoires. Definition of DRB supertypes using binding data is limited to few (about 29) known alleles (≪ 5% of all known DRB alleles). Hence, we describe a strategy using structurally defined virtual pockets to group all known DRB alleles with regard to their overlapping peptide binding specificity.

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