Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry.

ABSTRACT: Crosslinking mass spectrometry has developed into a robust technique that is increasingly used to investigate the interactomes of organelles and cells. However, the incomplete and noisy information in the mass spectra of crosslinked peptides limits the numbers of protein-protein interactions that can be confidently identified. Here, we leverage chromatographic retention time information to aid the identification of crosslinked peptides from mass spectra. Our Siamese machine learning model xiRT achieves highly accurate retention time predictions of crosslinked peptides in a multi-dimensional separation of crosslinked E. coli lysate. Importantly, supplementing the search engine score with retention time features leads to a substantial increase in protein-protein interactions without affectin

SUBMITTER: Giese SH 

PROVIDER: S-EPMC8163845 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature