MS-Fit

MS-Fit performs peptide mass fingerprinting by comparing experimental peptide mass lists from mass spectrometry to theoretical peptide masses derived from protein databases to identify proteins following gel electrophoresis.

Key Features:

• Peptide mass fingerprinting: Matches experimental peptide mass values to theoretical peptide masses derived from protein sequences for protein identification.
• Enzymatic cleavage modeling: Calculates theoretical peptide masses using specific cleavage patterns of the enzyme used for digestion.
• Mass spectrometry integration: Uses peptide mass lists produced by mass spectrometry of enzymatically digested proteins, including in-gel digests after gel electrophoresis.
• Database comparison: Compares experimental mass lists against entries in protein databases to find candidate proteins.
• Matching algorithms: Employs algorithms to identify closest matches and report multiple potential matches for analyzed peptides.
• High-throughput applicability: Supports analysis workflows suitable for studies requiring large-scale or multiple sample comparisons.
• Bioinformatics suite integration: Functions as a component compatible with bioinformatics suites such as ProteinProspector for upstream or downstream analyses.

Scientific Applications:

• Protein identification after gel electrophoresis: Identifies proteins separated by gel electrophoresis through analysis of peptide mass fingerprints.
• Proteomics research: Facilitates characterization of protein composition and inference of protein function in complex samples.
• Precise protein characterization: Enables discrimination of candidate proteins by matching observed peptide masses to theoretical digests.
• High-throughput proteomic surveys: Applies to studies that require analysis of many samples or large mass lists for proteome-scale investigations.
• Integration into analysis pipelines: Serves as a computational step for database-based protein identification within broader proteomics workflows.

Methodology:

Compute theoretical peptide masses from protein database sequences using enzyme-specific cleavage rules and compare those theoretical masses to experimental peptide mass lists from mass spectrometry using matching algorithms to identify and rank candidate proteins.