A predictive model for identifying proteins by a single peptide match.
BACKGROUND
Tandem mass-spectrometry of trypsin digests, followed by database searching, is one of the most popular approaches in high-throughput proteomics studies. Peptides are considered identified if they pass certain scoring thresholds. To avoid false positive protein identification, > or = 2 unique peptides identified within a single protein are generally recommended. Still, in a typical high-throughput experiment, hundreds of proteins are identified only by a single peptide. We introduce here a method for distinguishing between true and false identifications among single-hit proteins. The approach is based on randomized database searching and usage of logistic regression models with cross-validation. This approach is implemented to analyze three bacterial samples enabling recovery 68-98% of the correct single-hit proteins with an error rate of < 2%. This results in a 22-65% increase in number of identified proteins. Identifying true single-hit proteins will lead to discovering many crucial regulators, biomarkers and other low abundance proteins.


BACKGROUND
Supplementary data are available at Bioinformatics online.
Higdon
Roger
R
The BIATECH Institute, Bothell, WA 98011, USA.
Kolker
Eugene
E
D000465:Algorithms; D000595:Amino Acid Sequence; D003198:Computer Simulation; D003628:Database Management Systems; D030562:Databases, Protein; D016247:Information Storage and Retrieval / Q000379:methods*; D016015:Logistic Models; D013058:Mass Spectrometry / Q000379:methods*; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated; D010449:Peptide Mapping / Q000379:methods*; D011506:Proteins / Q000032:analysis* / Q000737:chemistry; D012044:Regression Analysis; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*
D011506:Proteins