PepExplorer

PepExplorer analyzes de novo peptide sequencing data to assemble homologous protein lists and identify proteins from mass spectrometry experiments when sequence databases are incomplete.

Key Features:

• De Novo Sequencing Integration: Infers peptide sequences directly from experimental mass spectra without requiring pre-existing protein sequence databases.
• Pattern Recognition and Protein Assembly: Employs pattern recognition and sequence alignment to assemble homologous protein lists for biological interpretation of de novo peptides.
• Data Compatibility: Accepts outputs from various de novo sequencing tools used in mass spectrometry workflows.
• False-Discovery Rate Control: Maintains a global false-discovery rate (FDR) while converging on a list of protein identifications.
• Neural Network Utilization: Uses a radial basis function neural network that scores precursor charge states, de novo sequencing scores, peptide lengths, and alignment scores to select similar protein candidates from a target-decoy database.
• Alignment Algorithm: Implements a modified Smith-Waterman algorithm for sequence alignment.
• Validation and Effectiveness: Recovered most ProLuCID identifications from Pyrococcus furiosus mass spectra against the NCBI RefSeq database, including tests with amino-acid-swapped sequence databases at 1% FDR.
• Application in Proteomic Research: Enabled a comprehensive proteomic assessment of Bothrops jararaca plasma that identified natural inhibitors of snake toxins.
• Integration with PatternLab for Proteomics: Integrates with PatternLab for Proteomics to provide access to downstream quantitative and differential proteomics analyses.

Scientific Applications:

• Novel protein identification: Identifying proteins and peptides when traditional peptide-spectrum matching is limited by incomplete or absent database entries.
• Homologue assembly and interpretation: Assembling homologous protein lists from de novo peptides to support biological interpretation of complex datasets.
• Method benchmarking and validation: Validating de novo-based identifications against database search results such as ProLuCID using Pyrococcus furiosus spectra and controlled database manipulations.
• Complex-sample proteomics: Profiling complex biological samples, exemplified by detection of natural toxin inhibitors in Bothrops jararaca plasma.

Methodology:

Performs de novo peptide inference from mass spectra, applies pattern recognition and a modified Smith-Waterman sequence alignment, scores candidates with a radial basis function neural network using precursor charge, de novo scores, peptide length, and alignment scores against a target-decoy database derived from phylogenetically related species, and applies global FDR control; validation used ProLuCID identifications on Pyrococcus furiosus spectra including amino-acid-swapped database tests.