Two complementary methods for predicting peptides binding major histocompatibility complex molecules

Gulukota, Kamalakar; Sidney, John; Sette, Alessandro; DeLisi, Charles

doi:10.1006/jmbi.1997.0937

Cited by 242 publications

(206 citation statements)

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“…Therefore, determination of peptides that bind to MHCI molecules is important and has been approached by several methods, including quantitative matrices [26 -28], neural networks [29,30], and peptide threading [31,32]. Although a direct comparison between the various methods is not straightforward due to the different criteria followed by authors to assess the power of their predictors, it seems that overall quantitative matrices and neural networks yield similarly good results, whereas peptide threading remains under development.…”

Section: Discussionmentioning

confidence: 99%

Prediction of MHC class I binding peptides using profile motifs

2002

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ABSTRACT:Peptides that bind to a given major histocompatibility complex (MHC) molecule share sequence similarity. Therefore, a position specific scoring matrix (PSSM) or profile derived from a set of peptides known to bind to a specific MHC molecule would be a suitable predictor of whether other peptides might bind, thus anticipating possible T-cell epitopes within a protein. In this approach, the binding potential of any peptide sequence (query) to a given MHC molecule is linked to its similarity to a group of aligned peptides known to bind to that MHC, and can be obtained by comparing the query to the PSSM. This article describes the derivation of alignments and profiles from a collection of peptides known to bind a specific MHC, compatible with the structural and molecular basis of the peptide-MHC class I (MHCI) interaction. Moreover, in order to apply these profiles to the prediction of peptide-MHCI binding, we have developed a new search algorithm (RANKPEP) that ranks all possible peptides from an input protein using the PSSM coefficients. The predictive power of the method was evaluated by running RANKPEP on proteins known to bear MHCI K b -and D b -restricted T-cell epitopes. Analysis of the results indicates that Ͼ 80% of these epitopes are among the top 2% of scoring peptides. Prediction of peptide-MHC binding using a variety of MHCI-specific PSSMs is available on line at our RANK-PEP web server (www.mifoundation.org/Tools/rankpep.html). In addition, the RANKPEP server also allows the user to enter additional profiles, making the server a powerful and versatile computational biology benchmark for the prediction of peptide-MHC binding. Human Immunology 63, 701-709 (2002).

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Section: Discussionmentioning

confidence: 99%

Prediction of MHC class I binding peptides using profile motifs

2002

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“…Under the conditions utilized, where [label]<[HLA class I] and IC 50 ≥ [HLA class I], the measured IC 50 values are reasonable approximations of true Kd values. 64,65 Each competitor peptide is tested at six different concentrations covering a 100,000-fold dose range, and in three or more independent experiments. As a positive control, the unlabeled version of the radiolabeled probe is also tested in each experiment.…”

Section: Methodsmentioning

confidence: 99%

Predicting T cell recognition of MHC class I restricted neoepitopes

et al. 2018

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Epitopes that arise from a somatic mutation, also called neoepitopes, are now known to play a key role in cancer immunology and immunotherapy. Recent advances in high-throughput sequencing have made it possible to identify all mutations and thereby all potential neoepitope candidates in an individual cancer. However, most of these neoepitope candidates are not recognized by T cells of cancer patients when tested in vivo or in vitro, meaning they are not immunogenic. Especially in patients with a high mutational load, usually hundreds of potential neoepitopes are detected, highlighting the need to further narrow down this candidate list. In our study, we assembled a dataset of known, naturally processed, immunogenic neoepitopes to dissect the properties that make these neoepitopes immunogenic. The tools to use and thresholds to apply for prioritizing neoepitopes have so far been largely based on experience with epitope identification in other settings such as infectious disease and allergy. Here, we performed a detailed analysis on our dataset of curated immunogenic neoepitopes to establish the appropriate tools and thresholds in the cancer setting. To this end, we evaluated different predictors for parameters that play a role in a neoepitope’s immunogenicity and suggest that using binding predictions and length-rescaling yields the best performance in discriminating immunogenic neoepitopes from a background set of mutated peptides. We furthermore show that almost all neoepitopes had strong predicted binding affinities (as expected), but more surprisingly, the corresponding non-mutated peptides had nearly as high affinities. Our results provide a rational basis for parameters in neoepitope filtering approaches that are being commonly used.Abbreviations: SNV: single nucleotide variant; nsSNV: nonsynonymous single nucleotide variant; ROC: receiver operating characteristic; AUC: area under ROC curve; HLA: human leukocyte antigen; MHC: major histocompatibility complex; PD-1: Programmed cell death protein 1; PD-L1 or CTLA-4: cytotoxic T-lymphocyte associated protein 4

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“…Peptides were typically tested at six different concentrations covering a 100,000-fold range, and in 3 or more independent assays. Under the conditions used, where [ (46,47).…”

Section: Mhc Peptide-binding Assaysmentioning

confidence: 99%