ReviewMolecular & cellular proteomics : MCP2024
Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors Into Peptide Identification.
Review in Molecular & cellular proteomics : MCP, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- AI proteomics: from protein identification to virtual cells.Nature methods · 2026Review
- Instrument-Software Synergy in Proteomics: Systematic Evaluation across Mass Spectrometry Platforms, Search Engines, and Rescoring Methods.Journal of proteome research · 2026Article
- Identifying lipid-protein interactomes with functional lipid probes.Nature protocols · 2026Review
- Integration of alternative fragmentation techniques into standard LC-MS workflows using a single deep learning model enhances proteome coverage.Nature methods · 2026Article
- Benchmarking Software for DDA-PASEF Immunopeptidomics.Molecular & cellular proteomics : MCP · 2026Article
- End-to-end deep attention-based multitask pipeline for predicting uncertainty-quantified peptide properties from mass spectrometry data.Scientific reports · 2026Article
- An Integrated Method for Profiling Lipid-Protein Interactions Using Multifunctional Lipid Probes.bioRxiv : the preprint server for biology · 2026Article
- MHCquant2 refines immunopeptidomics tumor antigen discovery.Genome biology · 2025Article
- Prosit-XL: enhanced cross-linked peptide identification by fragment intensity prediction to study protein interactions and structures.Nature communications · 2025Article
- Rustims: An Open-Source Framework for Rapid Development and Processing of timsTOF Data-Dependent Acquisition Data.Journal of proteome research · 2025Article
- Peptide Property Prediction for Mass Spectrometry Using AI: An Introduction to State of the Art Models.Proteomics · 2025Review
- Perspectives in computational mass spectrometry: recent developments and key challenges.Bioinformatics advances · 2025Article
- MSCI: an open-source Python package for information content assessment of peptide fragmentation spectra.Bioinformatics advances · 2025Article
- Proteome-wide non-cleavable crosslink identification with MS Annika 3.0 reveals the structure of the C. elegans Box C/D complex.Communications chemistry · 2024Article
- The 2024 Report on the Human Proteome from the HUPO Human Proteome Project.Journal of proteome research · 2024Review
- Defining Spectral Quality in Mass Spectrometry-Based Proteomics: A Retrospective Review.Mass spectrometry reviewsReview
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rescoring of peptide spectrum matches originating from database search engines enabled by peptide property predictors is exceeding the performance of peptide identification from traditional database search engines. In contrast to the peptide spectrum match scores calculated by traditional database search engines, rescoring peptide spectrum matches generates scores based on comparing observed and predicted peptide properties, such as fragment ion intensities and retention times. These newly generated scores enable a more efficient discrimination between correct and incorrect peptide spectrum matches. This approach was shown to lead to substantial improvements in the number of confidently identified peptides, facilitating the analysis of challenging datasets in various fields such as immunopeptidomics, metaproteomics, proteogenomics, and single-cell proteomics. In this review, we summarize the key elements leading up to the recent introduction of multiple data-driven rescoring pipelines. We provide an overview of relevant post-processing rescoring tools, introduce prominent data-driven rescoring pipelines for various applications, and highlight limitations, opportunities, and future perspectives of this approach and its impact on mass spectrometry-based proteomics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.