A novel approach for protein identification from complex cell proteome using modified peptide mass fingerprinting algorithm.

Abstract

A unique peptide based search algorithm for identification of protein mixture using PMF is proposed. The proposed search algorithm utilizes binary search and heapsort programs to generate frequency chart depicting the unique peptides corresponding to all proteins in a proteome. The use of binary search program significantly reduces the time for frequency chart preparation to ∼2 s for a proteome comprising ∼23000 proteins. The algorithm was applied to a three-protein mixture identification, host cell protein (HCP) analysis, and a simulation-generated data set. It was found that the algorithm could identify at least one unique peptide of a protein even in the presence of fourfold higher concentration of another protein. In addition, two HCPs that are known to be difficult to remove were missed by MS/MS approach and were exclusively identified using the presented algorithm. Thus, the proposed algorithm when used along with standard proteomic approaches present avenues for enhanced protein identification efficiency, particularly for applications such as HCP analysis in biopharmaceutical research, where identification of low-abundance proteins are generally not achieved due to dynamic range limitations between the target product and HCPs.