Protein Identification using Top-Down

I admit it, I first downloaded this article in June's MCP because I thought that it was going to be a nice review of recent advances in top down proteomics.  For anyone who hasn't done this kind of work, this is where you do not digest your proteins before performing LC-MS/MS analysis.  When I was doing top down work years ago, I liked to start with 1 single protein and post-deconvolution, I could deliver results + or - 10 daltons.
Recent advances in LC separation of intact proteins as well as high resolution mass spectrometry and new fragmentation methods have allowed several recent studies to report the identification of hundreds of proteins in a single LC-MS/MS run.  It isn't uncommon for labs these days to report deconvoluted mass accuracy in the parts-per-million (PPM) range.

The paper we are discussing here is not, however, a review of top-down proteomics.  This paper is a description of a new piece of software for performing top-down analysis, called MS-Align+.

In order to evaluate the effectiveness of MS-Align+, the authors separately harvest proteins from yeast and a species of salmonella.  The proteins are separated on long LC runs (600 minutes) on a system coupled to an LTQ Orbitrap.  The MS1 spectra were obtained at 60,000 or 30,000 resolution, depending on the experiment.  The intact proteins were fragmented using the HCD cell and the MS/MS spectra were also obtained in the FT cell at a resolution of 30,000.

The obtained spectra were then evaluated with MS-Align+, Mascot, OMSSA.  Even though one of the primary goals of the project was to optimize MS-Align+ for simplicity and speed, the authors report that MS-Align+ compared favorably against every other algorithm they used.

Summary:  Despite what the title suggests, this is not a review paper on top-down proteomics.  It is a nice paper describing a new algorithm for top-down analysis.  The software uses some clever mathematics to run quickly, even on outdated desktop computers.  Unfortunately, due to the incredible similarity of the article's title to that of recent reviews on this subject, I fear that information on this algorithm will not disseminate as quickly as it deserves.