Some of us are involved in the folding@home project, spending time, money, and resources.

I would like to know the answer to two main questions:

  1. How do we know we fold it right? I mean, these models used in folding calculations. Is it probable that in a decade we will find that all these results are nil and void?
  2. Do we have any example that these calculations were practically applied anywhere?

3 Answers 3


in silico modelling of anything in biology is an active field of research. It's very useful for making predictions and developing hypotheses, but any findings need to be confirmed experimentally.

From the Folding@Home website:

Folding@home has been a success. In 2000-2001, we folded several small and fast folding proteins with experimental validation of our method. We are now working to further develop our method, and to apply it to more complex and interesting proteins and protein folding and misfolding questions. Since then (2002-2006), Folding@home has studied more complex proteins, reporting on the folding of many proteins on the microsecond timescale, including BBA5, the villin headpiece, Trp Cage, among others. In 2007, we crossed the millisecond milestone by simulating a protein called NTL9, and the 10 millisecond barrier in 2010 with ACBP.

More recently (2006-present), we have been putting a great deal of effort into studying proteins relevant for diseases, such as Alzheimer’s and Hunntington’s Disease. You can learn more about our results and peer-reviewed scientific achievements on our Papers Page.

As they say, you should check out their research contributions for how it's being practically applied.

You may also be interest in this article for the science behind and prospects of protein folding prediction: Bowman GR, Voelz VA, Pande VS. 2011. Taming the complexity of protein folding. Curr Opin Struct Biol 21(1):4-11. At least try reading the conclusion for a less technical overview.

  • $\begingroup$ In the coming years, making quantitative comparisons between theory and experiment should further deepen our understanding of processes like folding and allow us to refine simulation methodologies and parameterizations. $\endgroup$ Mar 9, 2015 at 5:42
  • $\begingroup$ Looking to the future, it will perhaps be in the application of this knowledge to numerous related problems, such as protein misfolding (relevant for numerous diseases [2]) and protein dynamics associated with function (such as enzymatic activity [74]), where advances in protein folding will continue to yield insight and impact for many years to come. $\endgroup$ Mar 9, 2015 at 5:43
  • $\begingroup$ It is from the Taming the complexity of protein folding. first quote talks about refining MD simulation, does it mean the folding we do now will be rejected because of more precise methods? $\endgroup$ Mar 9, 2015 at 5:46
  • $\begingroup$ Second quote - that's what I asked in my original question, where the knowledge was applied? I would like to see a research of treatment for any disease which was based on folding data and then verified by other means $\endgroup$ Mar 9, 2015 at 5:52
  • $\begingroup$ Research contributions - I didn't read all 118 items but looks like it contributed mostly to modeling. something like "lets fold to make folding better", this is not the outcome I expect from project of this scale $\endgroup$ Mar 9, 2015 at 5:55

I will try answer your question directly.

  1. How do we know if we fold it right?

    A. If you're interested in only the end product pf folding -- the 3D structure, then this is the subset of the folding problem called the structure prediction (from sequence alone).

    a. We can verify the structure experimentally by determining the 3D structures by NMR or crystallography. In fact, the CASP competition withholds newly solved structures and see which algorithm comes out the closest to the experimental structure.

    b. Based on Anfinsen's hypothesis, we can calculate the free energy of folding, and the structure that has the lowest energy among the assembles is probably (but not necessarily) the folded state.

    B. If you're also interested in how exactly the protein folds, including its folding rate, plausible intermediates etc.:

    a. You can experimentally determine the folding rate although the computed folding rate is often off by several order of magnitude.

    b. Some interactions, esp non-native interactions (aka. interactions between amino acids that are only observed during folding but not in the final folded structure) can be verified by carefully designed NMR experiment.

1b. Yes it is possible. Adjusting the force constants and force field in molecular dynamics (MD) (the core engine of Folding@Home) so that the calculations converge to consistent and sensible results is not trivial.

  1. If you mean the method of calculation, then yes, MD has a wide range of application outside the field of protein folding. Besides the improvement in MD algorithm and necessary approximation, it also facilitates some advances on calculation on more specialized hardwares e.g. GPU and programmable array.

2b. If you mean the end of product of computation model, then probably no, but one example came close. A model determined purely computationally (aka. ab initio), although still not as useful as an experimental structure, was close enough to facilitate the experimental determination of that structure (by a method called molecular replacement in X-ray crystallography)


A certain fold of a peptide string can be validated or ruled out if other experimental data is available. Some other techniques to infer protein structure are X-ray crystallography (requires pure protein that will crystallize) and single particle analysis.

  • $\begingroup$ As I understand crystallography is quite complex and expensive how much of simulations were verified with crystallography to prove it right? $\endgroup$ Mar 9, 2015 at 5:47
  • $\begingroup$ It's not really my field, but there are databases of protein structures, with annotations for each structure how it was obtained. $\endgroup$
    – Sleepses
    Mar 9, 2015 at 8:41

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