Database searching and accounting of multiplexed precursor and product ion spectra from the data independent analysis of simple and complex peptide mixtures

Abstract

A novel database search algorithm is presented for the qualitative identification of proteins over a wide dynamic range, both in simple and complex biological samples. The algorithm has been designed for the analysis of data originating from data independent acquisitions, whereby multiple precursor ions are fragmented simultaneously. Measurements used by the algorithm include retention time, ion intensities, charge state, and accurate masses on both precursor and product ions from LC-MS data. The search algorithm uses an iterative process whereby each iteration incrementally increases the selectivity, specificity, and sensitivity of the overall strategy. Increased specificity is obtained by utilizing a subset database search approach, whereby for each subsequent stage of the search, only those peptides from securely identified proteins are queried. Tentative peptide and protein identifications are ranked and scored by their relative correlation to a number of models of known and empirically derived physicochemical attributes of proteins and peptides. In addition, the algorithm utilizes decoy database techniques for automatically determining the false positive identification rates. The search algorithm has been tested by comparing the search results from a four-protein mixture, the same four-protein mixture spiked into a complex biological background, and a variety of other "system" type protein digest mixtures. The method was validated independently by data dependent methods, while concurrently relying on replication and selectivity. Comparisons were also performed with other commercially and publicly available peptide fragmentation search algorithms. The presented results demonstrate the ability to correctly identify peptides and proteins from data independent acquisition strategies with high sensitivity and specificity. They also illustrate a more comprehensive analysis of the samples studied; providing approximately 20% more protein identifications, compared to a more conventional data directed approach using the same identification criteria, with a concurrent increase in both sequence coverage and the number of modified peptides.

Label-Free Protein Quantification for Plant Golgi Protein Localization and Abundance

“…The [Glu-1]-fibrinopeptide was specified as a lock mass compound with 785.8426 m/z for z = 2 and a 0.25-D tolerance window. A detailed description of the PLGS pipeline is in Li et al (2009).…”

Proteomic Profiling of a Layered Tissue Reveals Unique Glycolytic Specializations of Photoreceptor Cells

“…For DDA acquisition files, .mgf searchable files were produced in Rosetta Elucidator and searches were then submitted to and retrieved from the Mascot v2.2 (Matrix Sciences, Inc) search engine in an automated fashion. For MS E data, ProteinLynx Global Server 2.4 (Waters Corporation) was used to generate searchable files that were then submitted to the IdentityE search engine (Waters Corporation, Milford, MA) (38,39); results files were then imported back into Elucidator. To enable global spectra scoring across results from both search engines, all search results were concurrently validated using the PeptideProphet and ProteinProphet algorithms in Elucidator using independent reverse decoy database validation (40,41).…”

Protein repertoire impact of Ubiquitin–Proteasome System impairment: Insight into the protective role of beta-estradiol

“…In solution protein digestion was performed as already described [20,21]. This novel approach for qualitative and relative quantitative analysis is based on experiments carried out without the use of isotopic labels, applying an ion accounting algorithm useful to performed an "Expression Analysis" [22,23]. How the PLGS Expression module works, applied data clustering and normalization and statistic analysis, has been previously described in detail by Silva JC, Huges MA et al [24][25][26].…”