Protein folded states are kinetic 'hubs'.

[patfla]

Discovered the folding@home typepad blog a wk or 2 ago. This

http://folding.typepad.com/

led me here:

http://folding.stanford.edu/English/Papers#ntoc2

A kinetic 'hub'. That's an interesting idea but what does it mean?

Did the folding@home researchers use an existing term of art? Googled around and I don't think so. They may have coined the expression.

Together, these models show that protein dynamics are dominated by stochastic jumps between numerous metastable states and that proteins have heterogeneous unfolded states (many unfolded basins that interconvert more rapidly with the native state than with one another) yet often still appear two-state. Most importantly, we find that protein native states are hubs that can be reached quickly from any other state. However, metastability and a web of nonnative states slow the average folding rate.

The key word here (for 'hub' purposes) may be 'interconvert'.

So you have an n-dimensional space where every point is the entire protein in some degree of being folded or unfolded. I’d like to think that one of the n dimensions is potential energy so that, say, the native state is an energetic ‘well’ (low point). Not necessarily the ‘greatest (lowest) minimum but short of traveling to a distant part of the universe “good enough”. Don't confuse n-dimensional space with universe.

The protein for the most part (over time) occupies its native state – but, it bops around (jumps in and out) stochastically. ‘Stochastic’ brings to mind quantum mechanical effects which is not necessarily wrong since, at this level of molecular chemistry, QM can indeed have a measurable impact on chemistry.

So the hub part? The molecule bops around between states (somewhat) but it always transitions between states via the native state.

Now why would that be? I’d guess that quantum mechanics does play some role.

[bruce]

I have read several of the scientific papers on related topics but not one using the term "hubs" so you may get a more precise answer from the Pande Group. Nevertheless I just gave a talk on a related paper and so here's what I think they mean.

First, 'stochastic’ is an appropriate word. The concept of Brownian motion describes small particles being kicked around by the thermal processes in a fluid. If you have a PS3 or you watch the FAH videos on youtube, you'll notice that the same thing happens at an atomic level. A protein does not fold in a simple, straightforward progression from high energy to low energy any more than the stock market moves up/down in a uniform way in response to some important news announcement.

The mathematical word "state" describes what we commonly call a particular "shape" of a protein and I'm going to use the more precise word.

The energy landscape across all possible states (shapes) has various peaks and valleys. If a local minimum or basin is surrounded by relatively high energy peaks/ridges, it forms a metastable shape where the protein may spend a considerable amount of time before the random (thermal) motions allow the protein to move to another metastable shape at a nearby minimum.

With enough results from FAH, you can create a map of the local minima. (It's a very high dimensional "map" but that doesn't change the basic concept.) Then, with a sufficient number of individual FAH trajectories, you can calculate the probability that a protein will dwell in a particular metastable state for a certain time. You can also calculate the probability of transitions between that state and each of the nearby states.

If a particular state has reasonably high probability of transition to a number of adjacent states, I'd probably call it a "hub." If a particular state has extremely low priority of transition to all other states except maybe one or two, I would NOT call it a hub.

That's a layman's description of the mathematics behind the concepts and it's currently very much on the forefront of FAH's research results.

In a more visual way, see http://www.youtube.com/watch?v=gFcp2Xpd29I
The protein exhibits quite a bit of random motion, pauses at a couple of metastable states, gets trapped in one that has very low probabilities of a direct transition to the final state, and then has to "go the long way around" to get to the lower energy state it was seeking.