The ability to map a proteomic fingerprint to transcriptomic data would master the
understanding of how gene expression translates into actual phenotype. In contrast to …

Rescoring of peptide spectrum matches originating from database search engines enabled
by peptide property predictors is exceeding the performance of peptide identification from …

The growing trend toward high-throughput proteomics demands rapid liquid
chromatography–mass spectrometry (LC–MS) cycles that limit the available time to gather …

Peptide identification in liquid chromatography-tandem mass spectrometry (LC-MS/MS)
experiments relies on computational algorithms for matching acquired MS/MS spectra …

Multiplexed and label-free mass spectrometry–based approaches with single-cell resolution
have attributed surprising heterogeneity to presumed homogenous cell populations. Even …

Abstract Machine learning (ML) and deep learning (DL) models for peptide property
prediction such as Prosit have enabled the creation of high quality in silico reference …

Comprehensive proteomic analysis is essential to elucidate molecular pathways and protein
functions. Despite tremendous progress in proteomics, current studies still suffer from limited …

Generating top-down tandem mass spectra (MS/MS) from complex mixtures of proteoforms
benefits from improvements in fractionation, separation, fragmentation, and mass analysis …

Unlike for DNA and RNA, accurate and high-throughput sequencing methods for proteins
are lacking, hindering the utility of proteomics in applications where the sequences are …

Bottom-up mass spectrometry-based proteomics is challenged by the task of identifying the
peptide that generates a tandem mass spectrum. Traditional methods that rely on known …