Abstract 3883: Variant peptide identification system for bottom-up proteomics: Finding hidden sequence alterations in MS data

In database search, acquired spectra are matched with theoretical spectra of database peptides. Peptides from large databases are likely to match to more than one spectrum, but only a small proportion of peptide interpretations are correct. Incorrect interpretations are likely to arise for two reasons: random matches and non-random matches to homologous peptides. The former is often eliminated by selection of an appropriate similarity threshold or by utilization of the statistical significance of a spectral match. The match to homologous peptide poses a challenge as there is often large similarity in their fragmentation spectra as well. Non-random, but incorrect matches occur surprisingly often in the identification of peptide sequence variants. For this reason, a high identification score does not imply correctness of interpretation. Increasing spectral score threshold enhances specificity in general, but results in significant loss of sensitivity. Therefore, a single spectral criterion is inadequate for distinguishing between homologous peptides. In this study, we reconceive the system for elimination of incorrect interpretations. We propose a system that uses multidimensional LC/MS information in combination with a priori knowledge of sample content to filter out unlikely interpretations. The system calculates retention time of candidate peptides, performs isotopic analysis of precursors and constructs preliminary protein assembly to increase separation between correct and incorrect matches. The approach is used for reevaluation of database search results and is generally applicable to the analysis of standard bottom-up proteomic data. The system was used for identification of alterations in MS2 proteomics data and validated against RNA-Seq data from the same sample. The results demonstrate that non-spectral data can be used to efficiently eliminate peptide interpretations that have no correspondence in the RNA and as such are likely false positives. The approach is sensitive and yields a large proportion of altered peptides that have RNA-Seq support. The method can potentially help overcome the problem of the large database search present, e.g., in proteogenomic studies, particularly important in cancer research and diagnostics. In summary, general prior knowledge of sample content and the use of LC and MS1 data improve on the MS2-based identification of peptides. Citation Format: Marian Hajduch, Miroslav Hruska, Lakshman Varanasi, Jiri Voller, Petr Dzubak. Variant peptide identification system for bottom-up proteomics: Finding hidden sequence alterations in MS data. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3883.