PhosSA: Fast and accurate phosphorylation site assignment algorithm for mass spectrometry data

Proteome Science

Research

Fahad Saeed¹  Jason D Hoffert¹  Mark A Knepper¹  Sara Rashidian²  Trairak Pisitkun³  Guanghui Wang⁴  Marjan Gucek⁴ 

[1] Epithelial Systems Biology Laboratory, National Heart Lung and Blood Institute (NHLBI), National Institutes of Health (NIH), Bethesda, Maryland, USA;Epithelial Systems Biology Laboratory, National Heart Lung and Blood Institute (NHLBI), National Institutes of Health (NIH), Bethesda, Maryland, USA;Department of Electrical Engineering and Computer Science, Catholic University of American, Washington, D.C, USA;Epithelial Systems Biology Laboratory, National Heart Lung and Blood Institute (NHLBI), National Institutes of Health (NIH), Bethesda, Maryland, USA;Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand;Proteomics Core Facility, National Heart Lung and Blood Institute (NHLBI), National Institutes of Health (NIH), Bethesda, Maryland, USA;

关键词: Dynamic Programming;    Phosphorylation Site;    Mass Spectrometry Data;    Proteome Discoverer;    Site Assignment;   

DOI  :  10.1186/1477-5956-11-S1-S14

来源: Springer

【 摘 要 】

Phosphorylation site assignment of high throughput tandem mass spectrometry (LC-MS/MS) data is one of the most common and critical aspects of phosphoproteomics. Correctly assigning phosphorylated residues helps us understand their biological significance. The design of common search algorithms (such as Sequest, Mascot etc.) do not incorporate site assignment; therefore additional algorithms are essential to assign phosphorylation sites for mass spectrometry data. The main contribution of this study is the design and implementation of a linear time and space dynamic programming strategy for phosphorylation site assignment referred to as PhosSA. The proposed algorithm uses summation of peak intensities associated with theoretical spectra as an objective function. Quality control of the assigned sites is achieved using a post-processing redundancy criteria that indicates the signal-to-noise ratio properties of the fragmented spectra. The quality assessment of the algorithm was determined using experimentally generated data sets using synthetic peptides for which phosphorylation sites were known. We report that PhosSA was able to achieve a high degree of accuracy and sensitivity with all the experimentally generated mass spectrometry data sets. The implemented algorithm is shown to be extremely fast and scalable with increasing number of spectra (we report up to 0.5 million spectra/hour on a moderate workstation). The algorithm is designed to accept results from both Sequest and Mascot search engines. An executable is freely available at http://helixweb.nih.gov/ESBL/PhosSA/ for academic research purposes.

【 授权许可】

CC BY   
© Saeed et al; licensee BioMed Central Ltd. 2013