PEPMatch: a tool to identify short peptide sequence matches in large sets of proteins

• Published in: BMC bioinformatics Vol. 24; no. 1; pp. 485 - 14
• Main Authors: Marrama, Daniel, Chronister, William D., Westernberg, Luise, Vita, Randi, Koşaloğlu-Yalçın, Zeynep, Sette, Alessandro, Nielsen, Morten, Greenbaum, Jason A., Peters, Bjoern
• Format: Journal Article
• Language: English
• Published: London BioMed Central 18.12.2023; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Allergens; Amino acids; Analysis; Antigenic determinants; Benchmarking; Benchmarks; Bioinformatics; Biomedical and Life Sciences; BLAST comparison; Cancer; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Datasets; Food allergies; Health aspects; Immune system; Immunological tolerance; Immunology; K-mer mapping; Life Sciences; Ligands; Lymphocytes; Lymphocytes T; Matching; Medical research; Microarrays; Milk; Peptide matching; Peptide T; Peptides; Proteins; Proteomes; Python; Recall; Residues; Sequence searching; Severe acute respiratory syndrome coronavirus 2; Software; Source code; T cell receptors; T cells; T-cell epitopes; United States; Immunology; BLAST comparison; Peptide matching; K-mer mapping; Benchmarking; Sequence searching; T-cell epitopes
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-023-05606-4

Background Numerous tools exist for biological sequence comparisons and search. One case of particular interest for immunologists is finding matches for linear peptide T cell epitopes, typically between 8 and 15 residues in length, in a large set of protein sequences. Both to find exact matches or matches that account for residue substitutions. The utility of such tools is critical in applications ranging from identifying conservation across viral epitopes, identifying putative epitope targets for allergens, and finding matches for cancer-associated neoepitopes to examine the role of tolerance in tumor recognition. Results We defined a set of benchmarks that reflect the different practical applications of short peptide sequence matching. We evaluated a suite of existing methods for speed and recall and developed a new tool, PEPMatch. The tool uses a deterministic k -mer mapping algorithm that preprocesses proteomes before searching, achieving a 50-fold increase in speed over methods such as the Basic Local Alignment Search Tool (BLAST) without compromising recall. PEPMatch’s code and benchmark datasets are publicly available. Conclusions PEPMatch offers significant speed and recall advantages for peptide sequence matching. While it is of immediate utility for immunologists, the developed benchmarking framework also provides a standard against which future tools can be evaluated for improvements. The tool is available at https://nextgen-tools.iedb.org , and the source code can be found at https://github.com/IEDB/PEPMatch .