Identification and characterisation of peptide binding motifs of six autoimmune disease‐associated human leukocyte antigen‐class I molecules including <i><scp>HLA</scp>‐B*39:06</i>

Eichmann, Martin; Ru, Arnoud de; Veelen, Peter A. van; Peakman, Mark; Kronenberg‐Versteeg, Deborah

doi:10.1111/tan.12413

Cited by 28 publications

(31 citation statements)

References 45 publications

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“…HLA-B*38:01 is the HLA-B allele most associated with protection from type 1 diabetes (Howson et al 2009; Nejentsev et al 2007). The vastly different disease association of these two alleles is surprising as both are members of the B27-supertype and, as such, are predicted to have similar peptide repertoires (Eichmann et al 2014; Falk et al 1995; Sidney et al 2008b). However, while HLA-B*39:06 and HLA-B*38:01 share 97.6% amino acid sequence identity, their sequence differences appear at residues predicted to be involved in peptide binding pockets.…”

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confidence: 99%

“…A previous study by Eichmann et al (Eichmann et al 2014) reported basic primary anchor motifs for B*38:01 and B*39:06 based on sequence analyses of eluted peptides. In that study, B*38:01 was found to preferentially bind ligands with H (and secondarily, Q) in position 2 and L (and secondarily I and F) at the C-terminus, a motif that is largely in agreement with one defined by Falk et al (Falk et al 1995), also based on peptide elution.…”

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confidence: 99%

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Characterization of the peptide binding specificity of the HLA class I alleles B38:01 and B39:06

et al. 2016

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B*38:01 and B*39:06 are present with phenotypic frequencies <2% in the general population, but are of interest as B*39:06 is the B allele most associated with type 1 diabetes susceptibility and 38:01 is most protective. A previous study derived putative main anchor motifs for both alleles based on peptide elution data. The present study has utilized panels of single amino acid substitution peptide libraries to derive detailed quantitative motifs accounting for both primary and secondary influences on peptide binding. From these analyses, both alleles were confirmed to utilize the canonical position 2/C-terminus main anchor spacing. B*38:01 preferentially bound peptides with the positively charged or polar residues H, R and Q in position 2, and the large hydrophobic residues I, F, L, W and M at the C-terminus. B*39:06 had a similar preference for R in position two, but also well tolerated M, Q and K. A more dramatic contrast between the two alleles was noted at the C-terminus, where the specificity of B*39:06 was clearly for small residues, with A as most preferred, followed by G, V, S, T, and I. Detailed position-by-position and residue-by-residue coefficient values were generated from the panels to provide detailed quantitative B*38:01 and B*39:06 motifs. It is hoped that these detailed motifs will facilitate the identification of T cell epitopes recognized in the context of two class I alleles associated with dramatically different dispositions towards type 1 diabetes, offering potential avenues for investigation of the role of CD8 T cells in this disease.

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Characterization of the peptide binding specificity of the HLA class I alleles B38:01 and B39:06

et al. 2016

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“…Outside of a few exceptions where super-bulging peptides of up to 14 amino acids have been observed to bind to the MHC (Burrows et al, 2006), the closed conformation of the MHC class I binding cleft limits the length of bound peptides to 8-11 amino acids. Recent elution studies have attempted to quantify the length distribution of naturally processed and presented peptides (Bassani-Sternberg et al, 2015;Eichmann et al, 2014) and found that generally 9mer peptides were optimal. However, allele-specific preferences exist, for instance in the relatively high fraction of 8mer peptides found in complex with HLA-B*18:01 (Eichmann et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Recent elution studies have attempted to quantify the length distribution of naturally processed and presented peptides (Bassani-Sternberg et al, 2015;Eichmann et al, 2014) and found that generally 9mer peptides were optimal. However, allele-specific preferences exist, for instance in the relatively high fraction of 8mer peptides found in complex with HLA-B*18:01 (Eichmann et al, 2014). Similarly, the murine allele H-2-Kb is known to have a comparable preference for 8 and 9mers (Deres et al, 1992) and HLA-B*44:03 a relative tendency toward longer peptides such as 10 and 11mers (Rist et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Gapped sequence alignment using artificial neural networks: application to the MHC class I system

2015

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Motivation: Many biological processes are guided by receptor interactions with linear ligands of variable length. One such receptor is the MHC class I molecule. The length preferences vary depending on the MHC allele, but are generally limited to peptides of length 8-11 amino acids. On this relatively simple system, we developed a sequence alignment method based on artificial neural networks that allows insertions and deletions in the alignment. Results: We show that prediction methods based on alignments that include insertions and deletions have significantly higher performance than methods trained on peptides of single lengths. Also, we illustrate how the location of deletions can aid the interpretation of the modes of binding of the peptide-MHC, as in the case of long peptides bulging out of the MHC groove or protruding at either terminus. Finally, we demonstrate that the method can learn the length profile of different MHC molecules, and quantified the reduction of the experimental effort required to identify potential epitopes using our prediction algorithm. Availability and implementation: The NetMHC-4.0 method for the prediction of peptide-MHC class I binding affinity using gapped sequence alignment is publicly available at:

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“…In our list of 806 CSGs, RAVEN predicted potential highly affine peptides for 9-mers, which usually show optimal binding to most MHC class I molecules, 30,33 and for HLA-A02:01, which is the most common MHC-I in Caucasians 34 with an allele frequency of 0.2755. 35 RAVEN automatically crosschecked these peptides by a text search algorithm with ApacheLucene 36,37 against the human reference-proteome (UniProt release 2015_06) to exclude sequence identity with non-specifically expressed proteins.…”

Section: Resultsmentioning

confidence: 99%

Systematic identification of cancer-specific MHC-binding peptides with RAVEN

Baldauf¹,

Gerke²,

Kirschner

et al. 2018

OncoImmunology

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Immunotherapy can revolutionize anti-cancer therapy if specific targets are available. Immunogenic peptides encoded by cancer-specific genes (CSGs) may enable targeted immunotherapy, even of oligo-mutated cancers, which lack neo-antigens generated by protein-coding missense mutations. Here, we describe an algorithm and user-friendly software named RAVEN (Rich Analysis of Variable gene Expressions in Numerous tissues) that automatizes the systematic and fast identification of CSG-encoded peptides highly affine to Major Histocompatibility Complexes (MHC) starting from transcriptome data. We applied RAVEN to a dataset assembled from 2,678 simultaneously normalized gene expression microarrays comprising 50 tumor entities, with a focus on oligo-mutated pediatric cancers, and 71 normal tissue types. RAVEN performed a transcriptome-wide scan in each cancer entity for gender-specific CSGs, and identified several established CSGs, but also many novel candidates potentially suitable for targeting multiple cancer types. The specific expression of the most promising CSGs was validated in cancer cell lines and in a comprehensive tissue-microarray. Subsequently, RAVEN identified likely immunogenic CSG-encoded peptides by predicting their affinity to MHCs and excluded sequence identity to abundantly expressed proteins by interrogating the UniProt protein-database. The predicted affinity of selected peptides was validated in T2-cell peptide-binding assays in which many showed binding-kinetics like a very immunogenic influenza control peptide. Collectively, we provide an exquisitely curated catalogue of cancer-specific and highly MHC-affine peptides across 50 cancer types, and a freely available software (https://github.com/JSGerke/RAVENsoftware) to easily apply our algorithm to any gene expression dataset. We anticipate that our peptide libraries and software constitute a rich resource to advance anti-cancer immunotherapy.

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Identification and characterisation of peptide binding motifs of six autoimmune disease‐associated human leukocyte antigen‐class I molecules including HLA‐B39:06*

Cited by 28 publications

References 45 publications

Characterization of the peptide binding specificity of the HLA class I alleles B38:01 and B39:06

Characterization of the peptide binding specificity of the HLA class I alleles B38:01 and B39:06

Gapped sequence alignment using artificial neural networks: application to the MHC class I system

Systematic identification of cancer-specific MHC-binding peptides with RAVEN

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Identification and characterisation of peptide binding motifs of six autoimmune disease‐associated human leukocyte antigen‐class I molecules including HLA‐B*39:06

Cited by 28 publications

References 45 publications

Characterization of the peptide binding specificity of the HLA class I alleles B*38:01 and B*39:06

Characterization of the peptide binding specificity of the HLA class I alleles B*38:01 and B*39:06

Gapped sequence alignment using artificial neural networks: application to the MHC class I system

Systematic identification of cancer-specific MHC-binding peptides with RAVEN

Contact Info

Product

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Identification and characterisation of peptide binding motifs of six autoimmune disease‐associated human leukocyte antigen‐class I molecules including HLA‐B39:06*

Characterization of the peptide binding specificity of the HLA class I alleles B38:01 and B39:06

Characterization of the peptide binding specificity of the HLA class I alleles B38:01 and B39:06