Abstract
Algorithmic search engines bridge the gap between large
tandem mass spectrometry data sets and the identification
of proteins associated with biological samples. Improvements
in these tools can greatly enhance biological
discovery. We present a new scoring scheme for comparing
tandem mass spectra with a protein sequence database.
The MASPIC (Multinomial Algorithm for Spectral
Profile-based Intensity Comparison) scorer converts an
experimental tandem mass spectrum into a m/z profile
of probability and then scores peak lists from potential
candidate peptides using a multinomial distribution model.
The MASPIC scoring scheme incorporates intensity,
spectral peak density variations, and m/z error distribution
associated with peak matches into a multinomial
distribution. The scoring scheme was validated on two
standard protein mixtures and an additional set of spectra
collected on a complex ribosomal protein mixture from
Rhodopseudomonas palustris. The results indicate a
5-15% improvement over Sequest for high-confidence
identifications. The performance gap grows as sequence
database size increases. Additional tests on spectra from
proteinase-K digest data showed similar performance
improvements demonstrating the advantages in using
MASPIC for studying proteins digested with less specific
proteases. All these investigations show MASPIC to be a
versatile and reliable system for peptide tandem mass
spectral identification.
