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Title: Improved peptide elution time prediction for reversed-phase liquid chromatography-MS by incorporating peptide sequence information

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 {approx}345,000 non-redundant 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 {approx}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 et al., Anal. Chem. 2003, 75, 1039-1048) and other previously reported models.

Authors:
; ; ; ; ; ; ; ; ; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Laboratory (PNNL), Richland, WA (US), Environmental Molecular Sciences Laboratory (EMSL)
Sponsoring Org.:
USDOE
OSTI Identifier:
889053
Report Number(s):
PNNL-SA-50263
20496; KP1102010; TRN: US200619%%345
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Analytical Chemistry
Additional Journal Information:
Journal Volume: 78; Journal Issue: 14
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; ACCURACY; AMINO ACIDS; ARCHITECTURE; CHROMATOGRAPHY; FORECASTING; NEURAL NETWORKS; PEPTIDES; RESIDUES; RETENTION; TRAINING; Environmental Molecular Sciences Laboratory

Citation Formats

Petritis, Konstantinos, Kangas, Lars J, Yan, Bo, Monroe, Matthew E, Strittmatter, Eric F, Qian, Weijun, Adkins, Joshua N, Moore, Ronald J, Xu, Ying, Lipton, Mary S, Camp, David G, and Smith, Richard D. Improved peptide elution time prediction for reversed-phase liquid chromatography-MS by incorporating peptide sequence information. United States: N. p., 2006. Web. doi:10.1021/ac060143p.
Petritis, Konstantinos, Kangas, Lars J, Yan, Bo, Monroe, Matthew E, Strittmatter, Eric F, Qian, Weijun, Adkins, Joshua N, Moore, Ronald J, Xu, Ying, Lipton, Mary S, Camp, David G, & Smith, Richard D. Improved peptide elution time prediction for reversed-phase liquid chromatography-MS by incorporating peptide sequence information. United States. https://doi.org/10.1021/ac060143p
Petritis, Konstantinos, Kangas, Lars J, Yan, Bo, Monroe, Matthew E, Strittmatter, Eric F, Qian, Weijun, Adkins, Joshua N, Moore, Ronald J, Xu, Ying, Lipton, Mary S, Camp, David G, and Smith, Richard D. Sat . "Improved peptide elution time prediction for reversed-phase liquid chromatography-MS by incorporating peptide sequence information". United States. https://doi.org/10.1021/ac060143p.
@article{osti_889053,
title = {Improved peptide elution time prediction for reversed-phase liquid chromatography-MS by incorporating peptide sequence information},
author = {Petritis, Konstantinos and Kangas, Lars J and Yan, Bo and Monroe, Matthew E and Strittmatter, Eric F and Qian, Weijun and Adkins, Joshua N and Moore, Ronald J and Xu, Ying and Lipton, Mary S and Camp, David G and Smith, Richard D},
abstractNote = {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 {approx}345,000 non-redundant 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 {approx}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 et al., Anal. Chem. 2003, 75, 1039-1048) and other previously reported models.},
doi = {10.1021/ac060143p},
url = {https://www.osti.gov/biblio/889053}, journal = {Analytical Chemistry},
number = 14,
volume = 78,
place = {United States},
year = {2006},
month = {7}
}