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Technology Description	An artificial neural network that facilitates prediction of reverse-phased liquid chromatography retention times.
Category	Software
PRC	Pacific Northwest National Laboratory
PubMed ID	16841926
Author	Konstantinos Petritis, Lars J. Kangas, Bo Yan, Matthew E. Monroe, Eric F. Strittmatter, Wei-Jun Qian, Joshua N. Adkins, Ronald J. Moore, Ying Xu, Mary S. Lipton, David G. Camp, II, and Richard D. Smith
Publication Description	The authors describe an artificial neural network-based method for predicting peptide retention times in reversed-phase liquid chromatography. They developed an empirical model that demonstrated an average elution time precision of ~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 their earlier report.
Methodology	The network was trained using ~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 network considers near-neighbor residue contributions, quasi-sequence order, secondary structure content, and hydrophobic moment.