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

被引:146
作者
Petritis, Konstantinos
Kangas, Lars J.
Yan, Bo
Monroe, Matthew E.
Strittmatter, Eric F.
Qian, Wei-Jun
Adkins, Joshua N.
Moore, Ronald J.
Xu, Ying
Lipton, Mary S.
II, David G. Camp
Smith, Richard D.
机构
[1] Pacific NW Natl Lab, Div Biol Sci, Richland, WA 99352 USA
[2] Pacific NW Natl Lab, Computat Sci & Math Div, Richland, WA 99352 USA
[3] Pacific NW Natl Lab, Environm & Mol Sci Lab, Richland, WA 99352 USA
[4] Univ Georgia, Computat Syst Biol Lab, Dept Biochem & Mol Biol, Athens, GA 30601 USA
关键词
D O I
10.1021/ac060143p
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
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 similar to 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 similar to 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.
引用
收藏
页码:5026 / 5039
页数:14
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