Automated Iterative MS/MS Acquisition: A Tool for Improving Efficiency of Protein Identification Using a LC–MALDI MS Workflow

Abstract

We have developed an information-dependent, iterative MS/MS acquisition (IMMA) tool for improving MS/MS efficiency, increasing proteome coverage, and shortening analysis time for high-throughput proteomics applications based on the LC-MALDI MS/MS platform. The underlying principle of IMMA is to limit MS/MS analyses to a subset of molecular ions that are likely to identify a maximum number of proteins. IMMA reduces redundancy of MS/MS analyses by excluding from the precursor ion peak lists proteotypic peptides derived from the already identified proteins and uses a retention time prediction algorithm to limit the degree of false exclusions. It also increases the utilization rate of MS/MS spectra by removing "low value" unidentifiable targets like nonpeptides and peptides carrying large loads of modifications, which are flagged by their "nonpeptide" excess-to-nominal mass ratios. For some samples, IMMA increases the number of identified proteins by ∼20-40% when compared to the data dependent methods. IMMA terminates an MS/MS run at the operator-defined point when "costs" (e.g., time of analysis) start to overrun "benefits" (e.g., number of identified proteins), without prior knowledge of sample contents and complexity. To facilitate analysis of closely related samples, IMMA's inclusion list functionality is currently under development.