ionbot: a novel, innovative and sensitive machine learning approach to LC-MS/MS peptide identification

Abstract

Mass spectrometry-based proteomics generates vast amounts of signal data that require computational interpretation to obtain peptide identifications. Dozens of algorithms for this task exist, but all exploit only part of the acquired data to judge a peptide-to-spectrum match (PSM), ignoring important information such as the observed retention time and fragment ion peak intensity pattern. Moreover, only few identification algorithms allow open modification searches that can substantially increase peptide identifications. We here therefore introduce ionbot, a novel open modification search engine that is the first to fully merge machine learning with peptide identification. This core innovation brings the ability to include a much larger range of experimental data into PSM scoring, and even to adapt this scoring to the specifics of the data itself. As a result, ionbot substantially increases PSM confidence for open searches, and even enables a further increase in peptide identification rate of up to 30% by also considering highly plausible, lower-ranked, co-eluting matches for a fragmentation spectrum. Moreover, the exclusive use of machine learning for scoring also means that any future improvements to predictive models for peptide behavior will also result in more sensitive and accurate peptide identification.

MSBooster: Improving Peptide Identification Rates using Deep Learning-Based Features

“…A notable benefit of MSBooster is that the whole peptide library does not need to be predicted; only high-scoring candidates identified by MSFragger are evaluated using MSBooster, saving a large amount of time, especially for nonspecific searches. Furthermore, although not fully explored in this work, MSBooster can be used to process the MSFragger output for DDA data with multiple PSMs reported per spectrum, potentially assisting with the identification of co-fragmenting precursors present in DDA data [68].…”

Machine learning on large-scale proteomics data identifies tissue- and cell type-specific proteins

“…Reprocessing the raw data with ionbot 16 using uniform search settings enabled straightforward re-analysis and comparison. However, this approach comes with two main pitfalls.…”

“…These were selected to only contain samples from human origin without any pre-enrichment of organelles, modified proteins, or protein complexes. These selected projects consisted of 15,146 raw files in total, which were locally reprocessed using ionbot (version 0.6.2) in an open modification search against a protein database containing 75,141 proteins from both Swiss-Prot and TrEMBL (September 2020) as well as common contaminants with S-carbamidomethylation of cysteine and oxidation of methionine as variable modifications 16 . The raw files were manually annotated for their corresponding tissue and cell type of origin by either the metadata present in PRIDE, or by manual curation of the publication linked to the project.…”

Trans-synaptic dwelling of SARS-CoV-2 particles perturbs neural synapse organization and function

“…Raw mass spectrometry files were converted to mgf format using ThermoRawFileParser v1.3.4 ( 41 ) and searched against the human complement of the UniProtKB/SwissProt protein database containing 20,397 proteins, with added common contaminants and 17 proteins from the Severe acute respiratory syndrome coronavirus 2 proteome (UP000464024; June 2022). The search was performed using the ionbot search engine (v0.7.0) ( 42 ) with open modification search settings and three expected modifications: (i) carbamidomethylation of cysteine; (ii) oxidation of methionine; (iii) and N-terminal acetylation. Results were filtered on 1% FDR using a q-value below 0.01.…”