ArticleAnalytical chemistry2006
Improved peptide elution time prediction for reversed-phase liquid chromatography-MS by incorporating peptide sequence information.
Article in Analytical chemistry, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
- Increasing mass spectrometry throughput using time-encoded sample multiplexing.bioRxiv : the preprint server for biology · 2025Article
- Toward an Integrated Machine Learning Model of a Proteomics Experiment.Journal of proteome research · 2023Review
- Prediction of peptides retention behavior in reversed-phase liquid chromatography based on their hydrophobicity.Journal of separation science · 2023Review
- Deep learning neural network tools for proteomics.Cell reports methods · 2021Review
- [Research progress and application of retention time prediction method based on deep learning].Se pu = Chinese journal of chromatography · 2021Article
- Prediction of Chromatographic Elution Order of Analytical Mixtures Based on Quantitative Structure-Retention Relationships and Multi-Objective Optimization.Molecules (Basel, Switzerland) · 2020Article
- DART-ID increases single-cell proteome coverage.PLoS computational biology · 2019Article
- Article
- Visualization and application of amino acid retention coefficients obtained from modeling of peptide retention.Journal of separation science · 2018Article
- Multiple mechanisms drive phage infection efficiency in nearly identical hosts.The ISME journal · 2018Article
- Peptide Retention in Hydrophilic Strong Anion Exchange Chromatography Is Driven by Charged and Aromatic Residues.Analytical chemistry · 2018Article
- DeepPep: Deep proteome inference from peptide profiles.PLoS computational biology · 2017Article
- Locus-specific Retention Predictor (LsRP): A Peptide Retention Time Predictor Developed for Precision Proteomics.Scientific reports · 2017Article
- Predicting Electrophoretic Mobility of Tryptic Peptides for High-Throughput CZE-MS Analysis.Analytical chemistry · 2017Article
- A Priori Intrinsic PTM Size Parameters for Predicting the Ion Mobilities of Modified Peptides.Journal of the American Society for Mass Spectrometry · 2017Article
- Scientific workflow optimization for improved peptide and protein identification.BMC bioinformatics · 2015Article
- Intelligent data acquisition blends targeted and discovery methods.Journal of proteome research · 2014Article
- Proteomic analysis of saliva in HIV-positive heroin addicts reveals proteins correlated with cognition.PloS one · 2014Article
- High and low doses of ionizing radiation induce different secretome profiles in a human skin model.PloS one · 2014Article
- In-source fragmentation and the sources of partially tryptic peptides in shotgun proteomics.Journal of proteome research · 2013Article
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12 authors.
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Abstract
We describe an improved artificial neural network (ANN)-based method for predicting peptide retention times in reversed-phase liquid chromatography. In addition to the peptide amino acid composition, this study investigated several other peptide descriptors to improve the predictive capability, such as peptide length, sequence, hydrophobicity and hydrophobic moment, and nearest-neighbor amino acid, as well as peptide predicted structural configurations (i.e., helix, sheet, coil). An ANN architecture that consisted of 1052 input nodes, 24 hidden nodes, and 1 output node was used to fully consider the amino acid residue sequence in each peptide. The network was trained using approximately 345,000 nonredundant peptides identified from a total of 12,059 LC-MS/MS analyses of more than 20 different organisms, and the predictive capability of the model was tested using 1303 confidently identified peptides that were not included in the training set. The model demonstrated an average elution time precision of approximately 1.5% and was able to distinguish among isomeric peptides based upon the inclusion of peptide sequence information. The prediction power represents a significant improvement over our earlier report (Petritis, K.; Kangas, L. J.; Ferguson, P. L.; Anderson, G. A.; Pasa-Tolic, L.; Lipton, M. S.; Auberry, K. J.; Strittmatter, E. F.; Shen, Y.; Zhao, R.; Smith, R. D. Anal. Chem. 2003, 75, 1039-1048) and other previously reported models.
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