This paper describes the pFind 2.0 software package for peptide and protein identification via tandem mass spectrometry. Firstly, the most important feature of pFind 2.0 is that it offers a modularized and customized platform for third parties to test and compare their algorithms. The developers can create their own modules following the open application programming interface (API) standards and then add it into workflows in place of the default modules. In addition, to accommodate different requirements, the package provides four automated workflows adopting different algorithm modules, executing processes and result reports. Based on this design, pFind 2.0 provides an automated target-decoy database search strategy: The user can just specify a certain false positive rate (FPR) and start searching. Then the system will return the protein identification results automatically filtered by such an estimated FPR. Secondly, pFind 2.0 is also of high accuracy and high speed. Many pragmatic preprocessing, peptide-scoring, validation, and protein inference algorithms have been incorporated. To speed up the searching process, a toolbox for indexing protein databases is developed for high-throughput applications and all modules are implemented under a new architecture designed for large-scale parallel and distributed searching. An experiment on a public dataset shows that pFind 2.0 can identify more peptides than SEQUEST and Mascot at the 1% FPR. It is also demonstrated that this version of pFind 2.0 has better usability and higher speed than its previous versions. The software and more detailed supplementary information can both be accessed at http://pfind.ict.ac.cn/.

De novo peptide sequencing has improved remarkably in the past decade as a result of better instruments and computational algorithms. However, de novo sequencing can correctly interpret only ∼30% of high-and medium-quality spectra generated by collision-induced dissociation (CID), which is much less than database search. This is mainly due to incomplete fragmentation and overlap of different ion series in CID spectra. In this study, we show that higher-energy collisional dissociation (HCD) is of great help to de novo sequencing because it produces high mass accuracy tandem mass spectrometry (MS/MS) spectra without the low-mass cutoff associated with CID in ion trap instruments. Besides, abundant internal and immonium ions in the HCD spectra can help differentiate similar peptide sequences. Taking advantage of these characteristics, we developed an algorithm called pNovo for efficient de novo sequencing of peptides from HCD spectra. pNovo gave correct identifications to 80% or more of the HCD spectra identified by database search. The number of correct full-length peptides sequenced by pNovo is comparable with that obtained by database search. A distinct advantage of de novo sequencing is that deamidated peptides and peptides with amino acid mutations can be identified efficiently without extra cost in computation. In summary, implementation of the HCD characteristics makes pNovo an excellent tool for de novo peptide sequencing from HCD spectra.

Core fucosylation (CF) patterns of some glycoproteins are more sensitive and specific than evaluation of their total respective protein levels for diagnosis of many diseases, such as cancers. Global profiling and quantitative characterization of CF glycoproteins may reveal potent biomarkers for clinical applications. However, current techniques are unable to reveal CF glycoproteins precisely on a large scale. Here we developed a robust strategy that integrates molecular weight cutoff, neutral loss-dependent MS 3 , database-independent candidate spectrum filtering, and optimization to effectively identify CF glycoproteins. The rationale for spectrum treatment was innovatively based on computation of the mass distribution in spectra of CF glycopeptides. The efficacy of this strategy was demonstrated by implementation for plasma from healthy subjects and subjects with hepatocellular carcinoma. Over 100 CF glycoproteins and CF sites were identified, and over 10,000 mass spectra of CF glycopeptide were found. The scale of identification results indicates great progress for finding biomarkers with a particular and attractive prospect, and the candidate spectra will be a useful resource for the improvement of database searching methods for glycopeptides. Molecular & Cellular Proteomics 8:913-923, 2009.