High-throughput spectral acquisition raises the problem of dealing with genetic variation and peptide modifications within a population of related proteins. A method that cross-correlates and clusters related spectra in large collections of uncharacterized spectra (i.e. from normal and diseased individuals) would be very valuable in functional proteomics. This problem is far from being simple since very similar peptides may have very different spectra. We introduce a new notion of spectral similarity that allows one to identify related spectra even if the corresponding peptides have multiple modifications/mutations. Based on this notion we developed a new algorithm for mutation-tolerant database search as well as a method for cross-correlating related uncharacterized spectra.
 

This is a joint work with Vlado Dancik and Chris Tang at Millennium Pharmaceutical and Zufar Mulyukov at USC.