Application of Combinatorial Methods to Protein Identification in Peptide Mass Fingerprinting

Abstract

Peptide Mass Fingerprinting (PMF) for long has been a widely used and reliable method for protein identification. However it faced several problems, the most important of which is inability of classical methods to deal with protein mixtures. To cope with this problem, more costly experimental techniques are employed. We investigate, whether it is possible to extract more information from PMF by more thorough data analysis. To do this, we propose a novel method to remove noise from the data and show how the results can be interpreted in a different way. We also provide simulation results suggesting our method can be used for analysis of small mixtures.