An AI-driven leap forward in peptide identification through deconvolution of chimeric spectra
Live-Stream Presentation Date and Time: Monday, July 11th | 11:10-11:30 AM Room: KOPL
Presentation Overview
Chimeric spectra are estimated to constitute >40% of DDA data, violating the assumption that one spectrum represents one peptide. Here, we describe a new intelligent search algorithm (CHIMERYS) that rethinks the analysis of tandem mass spectra from the ground up. It routinely doubles the number of peptide identifications and reaches identification rates of >80%.
Analyzing a HeLa tryptic digest (1 hour gradient) with our new algorithm identified 114k PSMs, 61k unique peptides and 7,300 unique protein groups at 1% FDR. This is a 3.5-, 2- and 1.5-fold increase compared to SequestHT, respectively, resulting on average in 2.5-fold more identified peptides per protein.
We successfully demonstrated the fidelity of our new algorithm in four experiments: I) entrapment searches focusing on FDR-estimation, II) dilution experiments focusing on expected ratio distributions, III) comparisons with multiple search engines focusing on the overlap of identifications, IV) simulation experiments focusing on the deconvolution of chimeric spectra.
Our new algorithm is compatible with older mass spectrometer generations, but profits disproportionally from the increased sensitivity of recent instruments and measurements using wider isolation windows. It substantially outperforms other search engines on data of different complexity such as body fluids and organisms from all kingdoms of life.
