Protein folding pathways

This blog post addresses another question that was neglected in our last Science chat:

Is there any pathway for natural folding?Bruno Kestemont

This an excellent question, but unfortunately it does not have a simple answer. The folding pathway—sometimes discussed as "folding kinetics"—describes how an unfolded protein transitions to its native fold over the course of time. In general, folding pathways are poorly understood, but it is an area of active research (in fact, our very own David Baker started off studying the kinetics of protein folding in the '90s!).

Most of us working with Foldit or Rosetta do not think much about folding pathways (as one colleague put it, "Who cares?"). We lean heavily on the assumption that a chain of amino acids will naturally adopt its lowest-energy structure (see Anfinsen's dogma), and we don't worry too much about the path required to get there. In other words, we're more interested in how a protein system behaves at equilibrium; exactly how the system reaches equilibrium is another matter. Coincidentally, I am not an expert in folding kinetics, but I can touch on the main points.

Most people agree that strong, local interactions will form first (e.g. the short-range hydrogen bonds that stabilize α-helices and β-hairpins); and weak, nonlocal interactions will form more slowly (e.g. β-strand pairings between distant residues, interactions between pre-folded domains, etc.).

Many small proteins seem to fold via a concerted, two-state mechanism. You might imagine that such a protein is translated completely by the ribosome, and exists briefly as a random coil in solution before collapsing all-at-once into a stable fold. We observe such proteins in only two states: either completely unfolded or completely folded. This is the most likely scenario for the types of small proteins (<150 residues) that are encountered in Foldit puzzles.

Larger proteins seem to follow more complex, multi-state folding pathways. In some cases, we can actually observe multiple populations of a protein that exists in various, discrete stages of “foldedness.” Many of these proteins even fold co-translationally in the cell, so that the N-terminus of the protein might be completely folded before the ribosome finishes translating the C-terminus. In fact, there is evidence that certain genes have evolved “brake” regions in their mRNA, which actually slow down the ribosome at certain points during translation so that the N-terminus has a chance to fold before the C-terminus is translated.

More Info:
If you want to know more (and we hope you do), I strongly recommend this review article by Dill et al. It is a clearly-written overview intended for readers outside of the field. And, like any good review, it includes many pages of references for more curious readers.

( Posted by  bkoep 88 1379  |  Wed, 02/01/2017 - 20:05  |  4 comments )
Joined: 09/24/2012
Groups: Go Science
From the player's perspective

Very inspiring. I also recommend the review article by Dill et al.

Player's kinetics
Players also first start with helices and sheets (Ideal SS then trying to bond the sheets together, with a preference for following sheets). Then trying to compact and align these in order to put the orange inside and blue outside.

Then "fixing" with local interactions optimizations (idealizing shakes and local wiggle strategies) without touching the macro structure.

Then it will depend of the protein length. For small disordered proteins, we tent to use quasi random "disturbing" (GAB) recipes. That makes sense if, in nature, small proteins fold in microseconds ("jumping" above energetic barriers): we have to rely on the Roseta optimization but "breathing" to try different ones in order to artificially "jump" above local minima.

For big puzzles, we tend to continue with local strategies in order to preserve our first macro design (which protects our hydrogen bonds actually). One of them is the DRW familly of recipes (Deep Rebuild Worst) starting from small local (2-3 segments) to long (4-6) segments.

Our disadvantage to nature?
I wonder if one of our (including computer) disadvantage to nature would not be that we have to decide in a sequence of actions, while nature decides multi-paralelly in all dimensions together. This thinking is inspired by the following citation of the paper: "Not stable on their own, a few of those local structures are sufficiently metastable to survive to the next longer timescale, where they grow (or zip) into increasingly larger and more stable structures" (see figure 9). That would explain why Local Wiggle Strategies help us to "fix" a structure: our artifact is to freeze a lot of segments in order to allow the computer to serial optimize local parts, "stopping" the time for the other optimization processes.

"There are many parallel microscopic routes at the beginning, and fewer and more sequential routes at the end". We observe that at end game, the computer seems to be able to fine tune the fold. At start game, it's simply unable to follow "multiple parallel local routes" at the same time.

As a conclusion, from a player's perspective, "we care" on the fold kinetics, because this is how we see the problem. Beginner's always ask: "how shall I start"?

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Interesting perspective

Thanks for the perspective, Bruno! I think many of your ideas are useful for thinking about protein energetics; but I want to gently warn against the use of folding theory as inspiration for Foldit strategy.

Specifically, I don't want to misguide players by suggesting that Foldit gameplay has any physical semblance to folding kinetics. The Foldit score function includes terms that are not suitable for predicting a protein's motion over time. If you read the article above (see Fig. 5, in particular), I would encourage you to think about Foldit gameplay as "probing the energy landscape" (testing different structures in an attempt to find the lowest point in the landscape, i.e. to find the highest-scoring Foldit structure). It's less appropriate to imagine following a folding path through time, as an actual protein molecule would do. This may seem a subtle point for Foldit players, but it's an important scientific distinction on our end.

It may be easy to find parallels between Foldit gameplay strategies and folding kinetics theory. (And if you find that this thinking does help you on the Foldit scoreboards, then by all means go for it!) But there is little scientific basis for thinking that modeling a folding pathway in Foldit will lead to more accurate solutions.

Joined: 09/24/2012
Groups: Go Science
Thanks bkoep

Indeed Foldit players have an advantage on nature: when nature has to follow kinetics in varying environments, we (have to) concentrate on final stable fold (crystalized) in water at standard environment if I understood well. Our advantage is that we may cut and take full parts of a puzzle to other sides in order to find out better scores. I suppose that completely changing a shape would be difficult for nature (btw I wonder if natural mutant proteins can also end sticked to local minima, as I understood it might not be the case - nature seems to always ends at the bottom of the funnel, with best fit "score" or ?). We can pass through "unideal" and non realist intermediaries, like when using the rebuild tool or cut and uncut in impossible angles, change the clashing importance etc . in order to finally find the best result.

However, I retain that full creativity is not realist for gaining solutions. We cannot be too creative in making beautiful designs or complex nodes. For example, a succession of sheets will probably bond one the the following one preferably to complex shapes like for example sheet 1 bonding with 3, 2 with 4 and so on. That's what I tend to understand from your recent papers on "ideal" designs.

Joined: 09/24/2012
Groups: Go Science
Volcano shape rather than funnel

"we find that the free energy landscape has a volcano shape, rather than a simple funnel, that folding is two-state (single-exponential) when secondary structures are intrinsically unstable, and that each structure along the folding path is a transition state for the previous structure".

I like the picture here:

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