IntroSpect: motif-guided immunopeptidome database building tool to improve the sensitivity of HLA binding peptide identification

Abstract

Although database search tools originally developed for shotgun proteome have been widely used in immunopeptidomic mass spectrometry identifications, they have been reported to achieve undesirably low sensitivities and/or high false positive rates as a result of the hugely inflated search space caused by the lack of specific enzymic digestions in immunopeptidome. To overcome such a problem, we have developed a motif-guided immunopeptidome database building tool named IntroSpect, which is designed to first learn the peptide motifs from high confidence hits in the initial search and then build a targeted database for refined search. Evaluated on three representative HLA class I datasets, IntroSpect can improve the sensitivity by an average of 80% comparing to conventional searches with unspecific digestions while maintaining a very high accuracy (~96%) as confirmed by synthetic validation experiments. A distinct advantage of IntroSpect is that it does not depend on any external HLA data so that it performs equally well on both well-studied and poorly-studied HLA types, unlike a previously developed method SpectMHC. We have also designed IntroSpect to keep a global FDR that can be conveniently controlled, similar to conventional database search engines. Finally, we demonstrate the practical value of IntroSpect by discovering neoantigens from MS data directly. IntroSpect is freely available at https://github.com/BGI2016/IntroSpect.

The Importance of Being Presented: Target Validation by Immunopeptidomics for Epitope-Specific Immunotherapies

“…MS-Rescue ( 84 ) and DeepRescore ( 85 ) are machine learning-based algorithms which increase both sensitivity and reliability of peptide identifications using information such as peptide binding motifs, retention times and mass spectra predictions. Moreover, tools to create HLA-specific, and therefore smaller, peptide databases exist ( 86 , 87 ) and can be used to decrease the probability of false-positive identifications. Additionally, the quality of an immunopeptidomics dataset can be assessed a posteriori by performing HLA binding predictions as well as peptide sequence clustering ( 36 ) or by correlating observed retention times with calculated hydrophobicity indices ( 88 ) or predicted retention times ( 89 ), respectively.…”