ProteinProphet

ProteinProphet computes protein-level probabilities to validate protein identifications from tandem mass spectrometry (MS/MS) database search results using peptides assigned from proteolytic digests.

Key Features:

• Probabilistic Framework: Calculates probabilities of protein presence by analyzing peptide assignments to MS/MS spectra.
• Handling Peptide Ambiguity: Apportions peptides that map to multiple proteins in the sequence database among corresponding protein entries.
• Expectation-Maximization Algorithm: Uses the expectation-maximization algorithm to derive a minimal list of proteins sufficient to explain observed peptide assignments.
• Validation Across Complex Samples: Validated on spectra from 18 purified proteins and complex biological samples including H. influenzae and Halobacterium.
• Discrimination Power: Produces probabilities that discriminate between correct and incorrect protein identifications to reduce false positives.
• Predictable Sensitivity and Error Rates: Provides predictable sensitivity and controlled false positive identification error rates for filtering large proteomic datasets.
• Standardization and Comparability: Generates consistent results that facilitate publication and comparison of large-scale protein identification datasets.

Scientific Applications:

• Large-Scale Proteomic Studies: Quantifying and validating protein identifications in high-throughput proteomics datasets.
• Complex Biological Samples: Analyzing proteomes of organisms and complex microbial communities such as H. influenzae and Halobacterium.
• Data Standardization: Harmonizing reporting and comparison of protein identification results across experiments.

Methodology:

Computes protein presence probabilities from peptides assigned to MS/MS spectra obtained from proteolytic digests, apportions peptides shared among multiple proteins in the sequence database, and applies an expectation-maximization algorithm to derive a minimal protein list.