Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors Into Peptide Identification

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. [Display omitted] •Overview of common post-processors and data-driven rescoring pipelines.•Rescoring integrates advanced peptide property predictions into database searching.•Data-driven rescoring enhances match discrimination, boosting identification rates.•Overview of challenges, opportunities, and perspective of data-driven rescoring. Rescoring peptide spectrum matches by integrating predicted peptide properties, such as MS/MS intensity and retention time, substantially boosts the number of confidently identified peptides over traditional database search engines. This approach facilitates data analysis of challenging datasets in various fields, including immunopeptidomics, metaproteomics, and single-cell proteomics. Here, we introduce well-known post-processing rescoring tools and recently developed data-driven rescoring pipelines, followed by discussing their imitations, potential, and future perspectives.