A fast coarse filtering method for peptide identification by mass spectrometry.
BACKGROUND
We reformulate the problem of comparing mass-spectra by mapping spectra to a vector space model. Our search method leverages a metric space indexing algorithm to produce an initial candidate set, which can be followed by any fine ranking scheme.


RESULTS
We consider three distance measures integrated into a multi-vantage point index structure. Of these, a semi-metric fuzzy-cosine distance using peptide precursor mass constraints performs the best. The index acts as a coarse, lossless filter with respect to the SEQUEST and ProFound scoring schemes, reducing the number of distance computations and returned candidates for fine filtering to about 0.5% and 0.02% of the database respectively. The fuzzy cosine distance term improves specificity over a peptide precursor mass filter, reducing the number of returned candidates by an order of magnitude. Run time measurements suggest proportional speedups in overall search times. Using an implementation of ProFound's Bayesian score as an example of a fine filter on a test set of Escherichia coli protein fragmentation spectra, the top results of our sample system are consistent with that of SEQUEST.
Ramakrishnan
Smriti R
SR
Department of Computer Sciences, The University of Texas at Austin Austin, Texas 78712, USA. smriti@cs.utexas.edu
Mao
Rui
R
Nakorchevskiy
Aleksey A
AA
Prince
John T
JT
Willard
Willard S
WS
Xu
Weijia
W
Marcotte
Edward M
EM
Miranker
Daniel P
DP
D000465:Algorithms; D030562:Databases, Protein; D004926:Escherichia coli / Q000378:metabolism; D005822:Genetic Vectors; D013058:Mass Spectrometry / Q000379:methods*; D010449:Peptide Mapping / Q000379:methods*; D010455:Peptides / Q000737:chemistry*; D011381:Programming Languages; D011506:Proteins / Q000737:chemistry; D040901:Proteomics / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods; D012984:Software
D010455:Peptides; D011506:Proteins