protein ID

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Prof. Alexey Nesvizhskii (left) of University of Michigan receives a thank-you gift from David Chiang after his talk.

If you really want to understand how peptide and protein identification is done, this video talk is a must-see!

Professor Alexey Nesvizhskii of the University of Michigan is one of the co-inventors (with Dr. Andy Keller) of the popular PeptideProphet/ProteinProphet algorithm for turning search engine results into statistically consistent peptide and protein identifications. (This algorithm is also the basis for the popular Scaffold software.)

At the “Translational Proteomics 2.0″ meeting, we were privileged to have Alexey give his insightful talk that reviews the various steps involved in inferring peptide and protein identifications from large spectra datasets.

In this talk, you will learn why False Discovery Rates are preferred over P-values, why you probably should not run more than 4 replicates of a MudPIT experiment, how FDR estimations from decoy differ from Peptide/ProteinProphet, how “The Two Prophets” compute probabilities by curve-fitting the score distributions, how sensitivity and FDR are computed, and the what and why of some advanced TPP options.

The talk is available at: http://www.scivee.tv/node/12671 (45 minutes).

I recommend using the “full screen” mode so you can view the slides, which are also available as a download from the site. (Please be aware that the slideset order is different from that in the presentation.)

(Note: Both Trans-Proteomic Pipeline and Scaffold Batch software are integrated into the SORCERER platforms.)

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by David.Chiang@SageNResearch.com

Proteomics mass spectrometry is finally sensitive and specific enough for robust translational medicine (at least in capable hands), and holds tremendous promise to revolutionize biology and medicine. For some, it holds the key to incredible research power for decades to come.

However, there is a chasm that continues to grow between the productive and unproductive labs, because too many proteomics practitioners focus too early on low-level issues (i.e. cost, automation, ease-of-use) without first resolving high-level ones (i.e. sensitivity in presence of noise, quality of results, algorithmic suitability).

For many researchers experimenting with a new high-resolution instrument, the most common scenario is to select a workflow based on running a simple protein solution, usually a purified BSA solution or a commercial protein mixture.

Since different workflows will give basically identical protein IDs results for these simple test cases, they may conclude that all search engines are equivalent. While true when there is almost no signal noise, it is largely irrelevant in translational research. In fact, the exact same test will likely show that low-resolution and high-resolution mass specs are equivalent, the lowest quality reagents will suffice, or maybe you don’t have to clean your glassware as often. These are also true when there is little or no signal noise, but again, that is irrelevant for real-world research.

Seeing that there is little difference in protein IDs, some focus on using protein coverage as the sole metric for evaluating search engines. However, this is actually the opposite of what is needed for sensitive discovery proteomics. For example, if you are hunting for new protein biomarkers (especially a “one-hit wonder”), you do not want the protein inference engine tuned to assigning any ambiguous peptides to already found proteins, thereby hiding them from further study.

Not surprisingly, a workflow selected based on low-noise experiments and focused on protein coverage will excel for simple mixtures, but is not sensitive enough to analyze complex mixtures with wide dynamic range, such as in translational research. Scientists will be able to see the abundant peptides and proteins, but probably little else. That is roughly what most proteomics researchers find today, nothing meaningful, but enough of the obvious to not change their methodologies.

The result is that most labs are not getting the value commensurate with their investments in proteomics mass spectrometry. Under the current economic environment, this is both wasteful and dangerous.

Within the academic world, while many proteomics researchers have trouble getting any interest, a select few are swamped and have to turn away collaborators. Within drug discovery firms, while many are staring at their mostly idle mass spectrometers, a select few are running multiple mass spectrometers 24/7 sieving productively through millions of peptides.

So why are the majority of the proteomics research not producing high-value results?

With our access into the world’s top academic and drug discovery proteomics labs, we have a unique bird’s eye view into the answer. (However, like attorneys, we never give out client-specific information.)

Please allow me to share some secrets to your future success.

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