INFERYS rescoring: Boosting peptide identifications and scoring confidence of database search results

Abstract

Database search engines for bottom‐up proteomics largely ignore peptide fragment ion intensities during the automated scoring of tandem mass spectra against protein databases. Recent advances in deep learning allow the accurate prediction of peptide fragment ion intensities. Using these predictions to calculate additional intensity‐based scores helps to overcome this drawback.Here, we describe a processing workflow termed INFERYS™ rescoring for the intensity‐based rescoring of Sequest HT search engine results in Thermo Scientific™ Proteome Discoverer™ 2.5 software. The workflow is based on the deep learning platform INFERYS capable of predicting fragment ion intensities, which runs on personal computers without the need for graphics processing units. This workflow calculates intensity‐based scores comparing peptide spectrum matches from Sequest HT and predicted spectra. Resulting scores are combined with classical search engine scores for input to the false discovery rate estimation tool Percolator.We demonstrate the merits of this approach by analyzing a classical HeLa standard sample and exemplify how this workflow leads to a better separation of target and decoy identifications, in turn resulting in increased peptide spectrum match, peptide and protein identification numbers. On an immunopeptidome dataset, this workflow leads to a 50% increase in identified peptides, emphasizing the advantage of intensity‐based scores when analyzing low‐intensity spectra or analytes with very similar physicochemical properties that require vast search spaces.Overall, the end‐to‐end integration of INFERYS rescoring enables simple and easy access to a powerful enhancement to classical database search engines, promising a deeper, more confident and more comprehensive analysis of proteomic data from any organism by unlocking the intensity dimension of tandem mass spectra for identification and more confident scoring.

Wide Window Acquisition and AI-based data analysis to reach deep proteome coverage for a wide sample range, including single cell proteomic inputs

“…CHIMERYS™ was recently developed by MSAID and makes use of accurate fragment spectrum predictions trained on millions of spectra, which allows to utilize additional spectral properties such as relative signal intensities for drastically improved identification rates and accuracy. 12…”

“…CHIMERYS TM was recently developed by MSAID and makes use of accurate fragment spectrum predictions trained on millions of spectra, which allows to utilize additional spectral properties such as relative signal intensities for drastically improved identification rates and accuracy. 12 Implementation of all these innovations allowed us to reach unprecedented sensitivity, proteome coverage and improved throughput, which potentially reduces missing values, and allows novel biological insights. In our analytical platform, we combine recent technological innovations in liquid chromatography, mass spectrometry and data analysis to further improve the depth of analysis as illustrated in Figure 1A.…”

Prosit-TMT: Deep Learning Boosts Identification of TMT-Labeled Peptides

“…We and others have shown that the incorporation of expected fragment ion intensities and retention times into database scoring can improve the number and confidence of peptide identifications. ,,, This approach is often referred to as rescoring. Briefly, raw mass spectrometry data is searched initially without filtering for a desired false discovery rate (FDR).…”

Deep Single-Shot NanoLC-MS Proteome Profiling with a 1500 Bar UHPLC System, Long Fully Porous Columns, and HRAM MS

“…Acquired Orbitrap Exploris 480 .raw DDA files were processed with Proteome Discoverer 2.5 software (Thermo Fisher Scientific) using a two-step Sequest HT search algorithm and INFERYS rescoring node. 19 Further analysis was done using R scripts. 20 The Skyline software package was used to extract and analyze peptide peak areas.…”