Identification of Modified Proteins by Tandem Mass-Spectrometry.
Departments of Mathematics, Computer Science, and Molecular Biology,
University of Southern California .
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.