Chromatographic retention time prediction for posttranslationally modified peptides

PROTEOMICS ◽  
2012 ◽  
Vol 12 (8) ◽  
pp. 1151-1159 ◽  
Author(s):  
Luminita Moruz ◽  
An Staes ◽  
Joseph M. Foster ◽  
Maria Hatzou ◽  
Evy Timmerman ◽  
...  
Keyword(s):  
Retention Time ◽  
Chromatographic Retention ◽  
Retention Time Prediction ◽  
Time Prediction ◽  
Modified Peptides

scholarly journals The METLIN small molecule dataset for machine learning-based retention time prediction

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Xavier Domingo-Almenara ◽  
Carlos Guijas ◽  
Elizabeth Billings ◽  
J. Rafael Montenegro-Burke ◽  
Winnie Uritboonthai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Small Molecule ◽  
Retention Time ◽  
Chromatographic Retention ◽  
Retention Time Prediction ◽  
Molecular Identity ◽  
Time Prediction ◽  
Wide Range ◽  
Small Molecule Analysis ◽  
Deep Learning Model

AbstractMachine learning has been extensively applied in small molecule analysis to predict a wide range of molecular properties and processes including mass spectrometry fragmentation or chromatographic retention time. However, current approaches for retention time prediction lack sufficient accuracy due to limited available experimental data. Here we introduce the METLIN small molecule retention time (SMRT) dataset, an experimentally acquired reverse-phase chromatography retention time dataset covering up to 80,038 small molecules. To demonstrate the utility of this dataset, we deployed a deep learning model for retention time prediction applied to small molecule annotation. Results showed that in 70$$\%$$% of the cases, the correct molecular identity was ranked among the top 3 candidates based on their predicted retention time. We anticipate that this dataset will enable the community to apply machine learning or first principles strategies to generate better models for retention time prediction.

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

2020 ◽  
Author(s):  
Robbin Bouwmeester ◽  
Ralf Gabriels ◽  
Niels Hulstaert ◽  
Lennart Martens ◽  
Sven Degroeve
Keyword(s):  
Retention Time ◽  
Prediction Models ◽  
Peptide Identification ◽  
Retention Time Prediction ◽  
Time Prediction ◽  
Current State ◽  
Workflow Integration ◽  
Modified Peptides ◽  
User Friendly ◽  
Python Package

AbstractThe 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.

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

2021 ◽  
Author(s):  
Lennart Martens ◽  
Robbin Bouwmeester ◽  
Ralf Gabriels ◽  
Niels Hulstaert ◽  
Sven Degroeve
Keyword(s):  
Retention Time ◽  
Prediction Models ◽  
Peptide Identification ◽  
Retention Time Prediction ◽  
Time Prediction ◽  
Current State ◽  
Workflow Integration ◽  
Modified Peptides ◽  
User Friendly ◽  
Cell Proteome

Abstract 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. Moreover, we show that DeepLC’s ability to predict retention times for any modification enables potentially incorrect identifications to be flagged in an open modification search of CD8-positive T-cell proteome data. 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.

scholarly journals HPLC Retention time prediction for metabolome analysis

2010 ◽  
Vol 5 (6) ◽  
pp. 255-258 ◽  
Author(s):  
Takashi Hagiwara ◽  
Seiji Saito ◽  
Yoshifumi Ujiie ◽  
Kensaku Imai ◽  
Masanori Kakuta ◽  
...  
Keyword(s):  
Retention Time ◽  
Metabolome Analysis ◽  
Retention Time Prediction ◽  
Time Prediction ◽  
Hplc Retention Time

Enhanced metabolite annotation via dynamic retention time prediction: Steroidogenesis alterations as a case study

2017 ◽  
Vol 1071 ◽  
pp. 11-18 ◽  
Author(s):  
Giuseppe Marco Randazzo ◽  
David Tonoli ◽  
Petra Strajhar ◽  
Ioannis Xenarios ◽  
Alex Odermatt ◽  
...  
Keyword(s):  
Retention Time ◽  
Retention Time Prediction ◽  
Time Prediction ◽  
Metabolite Annotation
Sign in / Sign up

Export Citation Format

Share Document