pFind Studio

pFind Studio integrates pFind 2.0, pNovo+, and pQuant to identify peptides and proteins from tandem mass spectrometry (MS/MS) data, perform de novo peptide sequencing, and quantify proteins for mass spectrometry-based proteomics analyses.

Key Features:

• pFind 2.0: A modular platform with an open API and four automated workflows supporting customizable algorithm modules, automated target-decoy database search with user-specified false positive rate (FPR) and automatic FPR-based filtering, advanced preprocessing, peptide-scoring, validation, protein inference, and a toolbox for indexing protein databases optimized for large-scale parallel and distributed searching, with reported peptide-identification performance superior to SEQUEST and Mascot at 1% FPR.
• pNovo+: De novo peptide sequencing from MS/MS spectra without relying on protein databases, leveraging complementary higher energy collisional dissociation (HCD) and electron transfer dissociation (ETD) fragmentation to improve accuracy on tryptic peptides and employing the pDAG algorithm to find paths in directed acyclic graphs without antisymmetry restrictions, achieving over threefold speed improvements versus other de novo programs.
• pQuant: Quantitation that mitigates signal interference from coeluting ions with similar m/z by identifying pairs of least-interfered isotopic chromatograms per peptide, calculating heavy/light peptide ratios with 99.75% confidence intervals, and deriving protein ratios and confidence intervals via kernel density estimation, with benchmarking on SILAC-labeled HeLa and 14N/15N-labeled E. coli showing more peptides quantified and greater accuracy than Census and MaxQuant.

Scientific Applications:

• Peptide and protein identification: Database searching with target-decoy FPR control, preprocessing, peptide-scoring, validation, and protein inference for MS/MS-based identification.
• De novo peptide sequencing: Sequence extraction from HCD and ETD MS/MS spectra without database reliance for identification of novel or modified peptides.
• Protein quantitation: Relative abundance measurement using heavy/light peptide ratios, 99.75% confidence intervals, and kernel density estimation for labeled experiments such as SILAC and 14N/15N.
• Algorithm development and benchmarking: Modular API and workflow framework for testing and comparing algorithms and for large-scale parallel/distributed database indexing and searching, with comparative performance data versus SEQUEST and Mascot.

Methodology:

Automated target-decoy database search with user-specified FPR and automatic filtering; preprocessing, peptide-scoring, validation, and protein inference; protein-database indexing optimized for parallel and distributed searching; use of complementary HCD and ETD spectra for de novo sequencing; pDAG path-finding in directed acyclic graphs without antisymmetry restrictions; identification of least-interfered isotopic chromatogram pairs, calculation of heavy/light peptide ratios with 99.75% confidence intervals, and kernel density estimation to derive protein ratios and confidence intervals.