Media.Player

A Software for Top-Down Spectral Deconvolution and Protein Identification

Technology Benefits

UC San Diego researchers compared MS-Align+ with various tools for top-down protein identification on two data sets from Saccharomyces cerevisiae (SC) and Salmonella typhimurium (ST). The results demonstrate that MS-Align+ significantly increase the number of identified spectra as compared to MASCOT and OMSSA on both data sets. While MS-Align+ and ProSightPC have similar performance on the ST data set, MS-Align+ outperforms ProSightPC on the more complex SC data set.

Detailed Technology Description

UC San Diego researchers have developed MS-Align+, a fast algorithm for top-down protein identification based on spectral alignment that enables searches for unexpected post-translational modifications (PTMs). The first step in top-down spectral interpretation is usually spectral deconvolution, which converts a complex top-down spectrum to a list of monoisotopic masses (a deconvoluted spectrum). Every protein (possibly with modifications) can be scored against a top-down deconvoluted spectrum, resulting in a protein-spectrum-match (PrSM). MS-Align+ shares the spectral alignment approach with MS-TopDown, but greatly improves on speed, statistical analysis (providing E-values of PrSMs), and the number of identified PrSMs (e.g., by finding spectral alignments between spectra and truncated proteins).

Others

Related Materials

Liu X, Sirotkin Y, Shen Y, Anderson G, Tsai YS, Ting YS, Goodlett DR, Smith RD, Bafna V, Pevzner PA. Protein Identification Using Top-Down Spectra. Mol Cell Proteomics. 2011 Oct 25. [Epub ahead of print.]

Tech ID/UC Case

22137/2012-815-0

Related Cases

2012-815-0

*Abstract

In the last two years, due to advances in protein separation and mass spectrometry, top-down mass spectrometry moved from analyzing single proteins to analyzing complex samples and identifying hundreds and even thousands of proteins. However, computational tools for database search of top-down spectra against protein databases are still in infancy.

*Principal Investigator

Name: Vineet Bafna

Department:

Name: Xiaowen Liu

Department:

Name: Pavel Pevzner

Department:

Country/Region

USA