Sequence-structure-function relationships in class I MHC: A local frustration perspective

Serçinoğlu, Onur; Ozbek, Pemra

doi:10.1371/journal.pone.0232849

Cited by 27 publications

(25 citation statements)

References 103 publications

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“…In addition, and as for pocket A, the last pocket also closes the C-terminal end of the cleft, with large aromatic residues at position 80, 81, 84, 123, 143 (as observed in HLA-A*02:01). The Tyr84 is conserved in about a third of HLA-I molecules [ 4 ], and replaced by Phe84 in ∼10% of HLA-I, and can be used as a switch that opens to enable the binding of longer peptides [ 14 , 15 ]. Interestingly, a large bulky residue at position 84 is also observed in lipid and metabolite antigen binding MHC-like molecules CD1 and MR1, respectively.…”

Section: Hla Pockets Descriptionmentioning

confidence: 99%

“…Additionally, the use of this method requires each HLA to be classified within a HLA supertype. The work laid out by Sette & Sidney was further expanded to classify 750 HLA's in total, but by 2007 the number of HLA-I's that had been discovered had reached ∼1500 and now >22 000 [ 4 ]. This later rendition incorporated binding data into the HLA supertype classification and reshuffled some HLAs into different supertypes.…”

Section: Hla Supertypes Classificationmentioning

confidence: 99%

“…The peptides presented by HLA molecules can be derived from host proteins (self-peptides) or from a pathogenic source such as viruses and bacteria. HLA is the most polymorphic molecule in humans, with >22 000 HLA alleles reported to date [ 4 ]. This allows HLAs to present a wide range of pathogenic-derived peptides to T cells (reviewed in [ 5 ]) and can also help to limit any pathogenic mutant escape from the immune system at a population level.…”

Section: Introductionmentioning

confidence: 99%

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The pockets guide to HLA class I molecules

Nguyen

Szeto

Gras

2021

Biochemical Society Transactions

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Human leukocyte antigens (HLA) are cell-surface proteins that present peptides to T cells. These peptides are bound within the peptide binding cleft of HLA, and together as a complex, are recognised by T cells using their specialised T cell receptors. Within the cleft, the peptide residue side chains bind into distinct pockets. These pockets ultimately determine the specificity of peptide binding. As HLAs are the most polymorphic molecules in humans, amino acid variants in each binding pocket influences the peptide repertoire that can be presented on the cell surface. Here, we review each of the 6 HLA binding pockets of HLA class I (HLA-I) molecules. The binding specificity of pockets B and F are strong determinants of peptide binding and have been used to classify HLA into supertypes, a useful tool to predict peptide binding to a given HLA. Over the years, peptide binding prediction has also become more reliable by using binding affinity and mass spectrometry data. Crystal structures of peptide-bound HLA molecules provide a means to interrogate the interactions between binding pockets and peptide residue side chains. We find that most of the bound peptides from these structures conform to binding motifs determined from prediction software and examine outliers to learn how these HLAs are stabilised from a structural perspective.

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Section: Hla Pockets Descriptionmentioning

confidence: 99%

Section: Hla Supertypes Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The pockets guide to HLA class I molecules

Nguyen

Szeto

Gras

2021

Biochemical Society Transactions

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“…In 2020, the immuno polymorphism database ( 40 ) contains >200 reported HLA-A29 alleles, but only the most common alleles— HLA-A*29:02 , HLA-A*29:01 , and HLA-A*29:10— have been reported in cases with birdshot ( 41 ). Structurally, HLA-A*29:01 (D102H) and HLA-A*29:10 (E177K) differ from HLA-A*29:02 at single amino acids positions in the external alpha 2 domain of HLA-A29 ( Figure 1A ), but these positions do not influence the expression, conformation, or interaction of the HLA-A complex with T cells ( 46 – 48 ). In other words, these alleles can be considered functionally similar.…”

Section: The Hla-a29 Protein Structurementioning

confidence: 99%

HLA-A29 and Birdshot Uveitis: Further Down the Rabbit Hole

Kuiper

Venema

2020

Front. Immunol.

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HLA class I alleles constitute established risk factors for non-infectious uveitis and preemptive genotyping of HLA class I alleles is standard practice in the diagnostic work-up. The HLA-A29 serotype is indispensable to Birdshot Uveitis (BU) and renders this enigmatic eye condition a unique model to better understand how the antigen processing and presentation machinery contributes to non-infectious uveitis or chronic inflammatory conditions in general. This review will discuss salient points regarding the protein structure of HLA-A29 and how key amino acid positions impact the peptide binding preference and interaction with T cells. We discuss to what extent the risk genes ERAP1 and ERAP2 uniquely affect HLA-A29 and how the discovery of a HLA-A29-specific submotif may impact autoantigen discovery. We further provide a compelling argument to solve the long-standing question why BU only affects HLA-A29-positive individuals from Western-European ancestry by exploiting data from the 1000 Genomes Project. We combine novel insights from structural and immunopeptidomic studies and discuss the functional implications of genetic associations across the HLA class I antigen presentation pathway to refine the etiological basis of Birdshot Uveitis.

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“…Each of these alleles has a specific binding preference for different peptides. Regardless of the highly polymorphic nature of MHC sequence, the MHC structure has an “Ultra-conserved” fold ( 17 ), which is present in nearly all jawed vertebrate species ( 12 , 14 ). In MHC-I molecules, the peptide binding groove is formed by an α -chain, which has two domains denoted as G-ALPHA1 and G-ALPHA2 in IMGT nomenclature ( 18 ) ( Figure 1A ).…”

Section: Introductionmentioning

confidence: 99%

PANDORA: A Fast, Anchor-Restrained Modelling Protocol for Peptide: MHC Complexes

et al. 2022

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Deeper understanding of T-cell-mediated adaptive immune responses is important for the design of cancer immunotherapies and antiviral vaccines against pandemic outbreaks. T-cells are activated when they recognize foreign peptides that are presented on the cell surface by Major Histocompatibility Complexes (MHC), forming peptide:MHC (pMHC) complexes. 3D structures of pMHC complexes provide fundamental insight into T-cell recognition mechanism and aids immunotherapy design. High MHC and peptide diversities necessitate efficient computational modelling to enable whole proteome structural analysis. We developed PANDORA, a generic modelling pipeline for pMHC class I and II (pMHC-I and pMHC-II), and present its performance on pMHC-I here. Given a query, PANDORA searches for structural templates in its extensive database and then applies anchor restraints to the modelling process. This restrained energy minimization ensures one of the fastest pMHC modelling pipelines so far. On a set of 835 pMHC-I complexes over 78 MHC types, PANDORA generated models with a median RMSD of 0.70 Å and achieved a 93% success rate in top 10 models. PANDORA performs competitively with three pMHC-I modelling state-of-the-art approaches and outperforms AlphaFold2 in terms of accuracy while being superior to it in speed. PANDORA is a modularized and user-configurable python package with easy installation. We envision PANDORA to fuel deep learning algorithms with large-scale high-quality 3D models to tackle long-standing immunology challenges.

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Sequence-structure-function relationships in class I MHC: A local frustration perspective

Cited by 27 publications

References 103 publications

The pockets guide to HLA class I molecules

The pockets guide to HLA class I molecules

HLA-A29 and Birdshot Uveitis: Further Down the Rabbit Hole

PANDORA: A Fast, Anchor-Restrained Modelling Protocol for Peptide: MHC Complexes

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