Improvement of Peptides Identification in Proteomics with the Use of New Analytical and Bioinformatic Strategies

Completion of the Human Genome Project enabled a better understanding of biological functions of organisms. However, these studies still provide a limited insight into the cellular processes. Nowadays, a comprehensive analysis and characterization of all expressed proteins, called proteomics, is the point of the interest. One of the important issues in proteomics is finding of analytical and bioinformatic strategies allowing unambiguous protein identification based on the searching of the peptide sequence databases. Some examples of bioinformatic strategies for analytical data processing obtained with the use of separation techniques and mass spectrometry analysis are given to demonstrate their usefulness in proteomics. First, the application of learning algorithms for the reliable evaluation of MS/MS spectra of peptides, which were separated and processed with reversed-phase liquid chromatography-tandem mass spectrometry is discussed. Detailed considerations of the use of artificial neural networks analysis to classify automatically peptide MS/MS spectra is provided and analyzed in the aspect of utility of another learning algorithms. Moreover, the usefulness of predictions of the reversed-phase liquid chromatography retention times of peptides in proteomic research is reported. In that case, quantitative structure-retention relationships (QSRR) analysis is considered in the view of the other approaches used in this field. Finally, the contribution of analytical information from the pI-based separation methods is considered as the additional source of peptide database matching constraint.