Artificial neural network analysis for evaluation of peptide MS/MS spectra in proteomics.
The aim of the work was to explore usefulness of artificial neural network (ANN) analysis for the evaluation of proteomics data. The analysis was applied to the data generated by the widely used protein identification program Sequest, completed with several structural parameters readily calculated from peptide molecular formulas. Proteins from yeast cells were identified based on the MS/MS spectra of peptides. The constructed ANN was demonstrated to classify automatically as either "good" or "bad" the peptide MS/MS spectra otherwise classified manually. An appropriately trained ANN proves to be a high-throughput tool facilitating examination of Sequest's results. ANNs are recommended as a means of automatic processing of large amounts of MS/MS data, which normally must be considered in the analysis of complex mixtures of proteins in proteomics.
Baczek
Tomasz
T
Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Gen J Hallera 107, 80-416 Gdańsk, Poland. tbaczek@amg.gda.pl
Buciński
Adam
A
Ivanov
Alexander R
AR
Kaliszan
Roman
R
D001185:Artificial Intelligence; D013058:Mass Spectrometry / Q000379:methods / Q000592:standards; D016571:Neural Networks, Computer; D010455:Peptides / Q000032:analysis*; D011506:Proteins / Q000032:analysis; D040901:Proteomics / Q000379:methods*; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae / Q000737:chemistry / Q000254:growth & development; D029701:Saccharomyces cerevisiae Proteins / Q000032:analysis*; D020539:Sequence Analysis, Protein / Q000379:methods / Q000592:standards; D012986:Software Validation; D013997:Time Factors
D010455:Peptides; D011506:Proteins; D029701:Saccharomyces cerevisiae Proteins