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DeepLC can predict retention times for peptides that carry as-yet unseen modifications


The inclusion of peptide retention time prediction promises to remove peptide identification ambiguity in complex liquid chromatography–mass spectrometry 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 searches, which will benefit from accurate retention time prediction for modified peptides to reduce identification ambiguity. We present DeepLC, a deep learning peptide retention time predictor using 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. Moreover, we show that DeepLC’s ability to predict retention times for any modification enables potentially incorrect identifications to be flagged in an open search of a wide variety of proteome data.

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Fig. 1: Scatter plots of predicted against observed on three of the largest data sets.
Fig. 2: Prediction performance in terms of three metric for all data sets.
Fig. 3: Learning curves for each of the three selected data sets.
Fig. 4: The modification that was excluded for training is shown on the horizontal axis, and the vertical axis shows the retention time error (experimental − predicted) when the modification was either not encoded (red) or encoded during the predictions (blue).
Fig. 5: Each amino acid that was excluded for training is shown as a circle, where the size of the circle and color indicates the remaining training peptides and chemical property, respectively.
Fig. 6: Predicted retention time analysis for open results of human tissue data.

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Data availability

Data from the following projects were used to train and evaluate DeepLC: HeLa hf42, ProteomeTools44, SWATH Library41, Plasma lumos 1h54, DIA HF43, HeLa lumos 2h54, Pancreas55, Xbridge56, ATLANTIS SILICA56, LUNA SILICA56, LUNA hydrophilic interaction chromatography56, strong ion exchange56, Yeast 2h57, HeLa lumos 1h54, Yeast 1h57, Arabidopsis58, Yeast DeepRT59, ProteomeTools PTM30, Plasma lumos 2h54 and HeLa DeepRT60. The files of each data set and open search results are available on Zenodo at The raw files the open search was performed on are available at PRIDE repository under the identifier PXD000561 (ref. 32).

Code availability

The following Python (v.3.6) libraries were used in DeepLC: Pandas (v.0.25.1)61, TensorFlow (v.1.14.0)62, Pyteomics (v.4.1.2)63, SciPy (v.1.4.0)64, matplotlib (v.3.1.3)65, seaborn (v.0.10.0)66 and Numpy (v.1.17.3)67. Other software used for DeepLC are: ThermoRawFileParser48 (v.1.2.0), FileZilla (v.3.48.1), MS-GF+ (ref. 49) (v.2019.08.26), Percolator68 (v3.4) and open-pFind26 (v.3.1.5). Code used to prepare the data sets, calibrate retention times, generate DeepLC models, make predictions and to reproduce the figures is available on Zenodo at

The DeepLC tool including a GUI (Supplementary Fig. 17) is available for download from the following repositories and package indexes:

• GUI:

• Python package:

• Bioconda package:

• Biocontainers docker image:

• Streamlit webserver:

• Source code:


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R.B. acknowledges funding from the Marie Sklodowska-Curie EU Framework for Research and Innovation Horizon 2020 MASSTRPLAN (grant no. 675132) and Vlaams Agentschap Innoveren en Ondernemen under project number HBC.2020.2205. R.G. acknowledges funding from the Research Foundation Flanders (FWO) (grant no. 1S50918N). S.D. and L.M. acknowledge funding from the European Union’s Horizon 2020 Programme (grant nos. H2020-INFRAIA-2018-1 and 823839). N.H. and L.M. acknowledge funding from the Research Foundation Flanders (FWO) (grant nos. G042518N and G028821N). L.M. acknowledges funding from Ghent University Concerted Research Action (grant no. BOF21-GOA-033).

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Authors and Affiliations



R.B., R.G. and S.D. conceived the study. R.B., R.G., L.M. and S.D. designed the experiments, analyzed the results and wrote the paper. R.G. made the tool available in Python package repositories. N.H. and R.B. built the graphical user interface.

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Correspondence to Lennart Martens.

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The authors declare no competing interests.

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Peer review information Nature Methods thanks Lukas Reiter and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Arunima Singh was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–17 and Tables 1–4.

Reporting Summary

Supplementary Table 5

Concatenation of all peptides used for training, validation and evaluation. For each peptide, the randomly assigned split group is provided.

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Bouwmeester, R., Gabriels, R., Hulstaert, N. et al. DeepLC can predict retention times for peptides that carry as-yet unseen modifications. Nat Methods 18, 1363–1369 (2021).

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