Many research questions in fields such as personalized medicine, drug screens or systems
biology depend on obtaining consistent and quantitatively accurate proteomics data from …

The recent development of machine learning methods to identify peptides in complex mass
spectrometric data constitutes a major breakthrough in proteomics. Longstanding methods …

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

Lactylation was initially discovered on human histones. Given its nascence, its occurrence
on nonhistone proteins and downstream functional consequences remain elusive. Here we …

Abstract Machine learning and in particular deep learning (DL) are increasingly important in
mass spectrometry (MS)-based proteomics. Recent DL models can predict the retention …

Abstract Genome‐, transcriptome‐and proteome‐wide measurements provide insights into
how biological systems are regulated. However, fundamental aspects relating to which …

In mass-spectrometry-based proteomics, the identification and quantification of peptides and
proteins heavily rely on sequence database searching or spectral library matching. The lack …

Characterizing the human leukocyte antigen (HLA) bound ligandome by mass spectrometry
(MS) holds great promise for develo** vaccines and drugs for immune-oncology. Still, the …

De novo peptide sequencing, which does not rely on a comprehensive target sequence
database, provides us with a way to identify novel peptides from tandem mass spectra …

Peptide fragmentation spectra are routinely predicted in the interpretation of mass-
spectrometry-based proteomics data. However, the generation of fragment ions has not …