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

Abstract

Predicting chromatographic retention times (RTs) has proven beneficial in proteomics but has not yet been achieved for crosslinked peptides. Here, the authors develop an RT prediction tool for crosslinked peptides and leverage predicted RTs to increase identifications in crosslinking mass spectrometry studies.