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J Biomol Tech. 2007 February; 18(1): 4.
PMCID: PMC2292061

P10-S A Novel Protein Database Search Algorithm for 1D and 2D LC-MS Data

Abstract

A novel, “Ion Accounting” algorithm has been developed for protein identification using time-resolved, LC-MSE data from 1D and 2D LC-MS experiments. The data from a 1D LC-MS analysis generate a series of precursor-product tables that are initially queried against a protein database using the “Ion Accounting” algorithm. Hereby each precursor and product is associated with only single peptide identification. The database search is a hierarchal process containing three modules. With the first module, the data are matched to only correctly cleaved proteolytic peptides whose precursor and product ion mass tolerances are within 10 and 20 ppm, respectively. With the second module, precursor and product ions that have not yet been assigned are queried against a subset database of the identified proteins from the first module. The second module includes missed cleavages, in-source fragments, neutral losses, and variable modifications. With the last module, the remaining unidentified ions are considered against the complete database for additional protein identifications (including PMF) with improved selectivity and specificity from the elimination of those precursor and product ions from the first two modules.

The data from a 2D LC-MS separation of proteolytic peptides is conducted by fractionating the peptides in a first dimension and subsequent separation in a second dimension during the LC-MSE analysis. Each fraction produces a series of precursor-product tables. From these tables, the peptides (precursor-products) that were not distributed over multiple fractions are saved to a “Combined Precursor-Product Table” (CPPT). The peptides that are split among neighboring fractions are combined by precursor mass, precursor retention time, and product ion pattern, and are appended to the CCPT. The final CCPT is submitted to the “Ion Accounting” protein database search engine in a similar fashion to the 1D LC-MS data analysis.


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