PT  - JOURNAL ARTICLE
AU  - Robbin Bouwmeester
AU  - Ralf Gabriels
AU  - Niels Hulstaert
AU  - Lennart Martens
AU  - Sven Degroeve
TI  - DeepLC can predict retention times for peptides that carry as-yet unseen modifications
AID  - 10.1101/2020.03.28.013003
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.03.28.013003
4099  - http://biorxiv.org/content/early/2020/03/29/2020.03.28.013003.short
4100  - http://biorxiv.org/content/early/2020/03/29/2020.03.28.013003.full
AB  - The inclusion of peptide retention time prediction promises to remove peptide identification ambiguity in complex LC-MS identification workflows. However, due to the way peptides are encoded in current prediction models, accurate retention times cannot be predicted for modified peptides. This is especially problematic for fledgling open modification searches, which will benefit from accurate retention time prediction for modified peptides to reduce identification ambiguity. We here therefore present DeepLC, a novel deep learning peptide retention time predictor utilizing a new peptide encoding based on atomic composition that allows the retention time of (previously unseen) modified peptides to be predicted accurately. We show that DeepLC performs similarly to current state-of-the-art approaches for unmodified peptides, and, more importantly, accurately predicts retention times for modifications not seen during training. DeepLC is available under the permissive Apache 2.0 open source license and comes with a user-friendly graphical user interface, as well as a Python package on PyPI, Bioconda, and BioContainers for effortless workflow integration.