ProteinART

ProteinART applies combinatorial optimization and a peptide detectability metric to infer the most probable set of proteins from peptides identified by tandem mass spectrometry in shotgun proteomics.

Key Features:

• Combinatorial optimization: Uses combinatorial optimization techniques to search for protein sets that explain observed peptides.
• Peptide detectability: Incorporates a peptide detectability metric to weight peptides by their likelihood of being observed by tandem mass spectrometry.
• Heuristic algorithm: Implements a heuristic algorithm that integrates peptide detectability into the protein inference process.
• Integration of peptide identifications and reference database: Combines confidently identified peptides with a reference protein database to assign peptides to proteins.
• Reformulation of minimum protein set problem: Reformulates the traditional minimum protein set approach to avoid assuming the smallest explanatory protein set.
• Handling redundant and homologous sequences: Explicitly addresses redundancy and homology among protein sequences that create ambiguous peptide-to-protein mappings.
• Evaluation on datasets: Evaluated using both synthetic and real proteomics datasets.
• Improved performance over greedy methods: Demonstrates superior performance compared to greedy algorithms for minimum protein set determination.

Scientific Applications:

• Protein inference in shotgun proteomics: Assigns peptides from tandem mass spectrometry data back to their originating proteins.
• Interpretation of peptide identifications: Improves interpretation of confidently identified peptides in the context of a reference protein database.
• Enhanced protein identification accuracy: Increases reliability of protein identification for studies of biological processes and disease mechanisms.

Methodology:

Reformulates the minimum protein set problem and applies combinatorial optimization via a heuristic algorithm that incorporates peptide detectability, with evaluation on synthetic and real proteomics datasets.