Heavy reading! Maybe you could summarize a little what that means, to matter ignorants like myself?

Regards,
Martin

by studying of some special stable amino-acid chains in proteins from one generation to another, they want to see what evolution pressures cause what changes in proteins. And with probabilistic methods they could find out 1) what evo pressures withstanded some protein with yet unknown function and 2) what functions can get protein after some kind of pressure (to me, the last thing looks quite useful for development of better genetically modified stuff)

sorry if this is completely wrong interpretation of topic article

Organisms from the various branches of the tree of life share a lot in common. Wildly different organisms share much of the same molecular machinery, and as you group them into smaller categories their proteomes begin to look very similar. Using protein sequence similarity we can identify proteins from multiple organisms that clearly shared a common ancestor, and biologists have developed algorithms for determining their evolutionary relationships. From these relationships we can infer how evolutionary pressures change proteins... encourage or discourage mutations at certain places in the protein. You can imagine that some portions of a protein might be very important for carrying its task and don't show many mutations, some portions just mutate randomly (neutral drift), and others seem to mutate wildly (perhaps as the protein develops a new function).

We've begun to perform this kind of analysis on some major plant protein families. It remains to be seen what kind of evolutionary trends we'll discover, but on a per protein basis this information can be very important to researchers. In my example I show how our analysis suggests that the DNA binding portions of a particular protein family are undergoing some sort of adaptive change.

Now we can perform this kind of analysis irrespective of structure predictions since it is based on protein sequence, but integrating it with WCG structure predictions is a primary goal of the project. The best part is that it doesn't require re-running any results from the WCG, and the trends we discover can be used to better select models from WCG runs and better identify functional properties.

Facebook suggested I look at the recent HPF2 Status Update. I read this thread the day it was started, but I checked just now to see if there was more. I hit the Research tab, went to the HPF2 page and clicked on the Status updates link. It took me to the page that links to all the HPF2 news updates.

Oops. The most recent update shown on the page is dated 2009.


Lawrence