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Currently, there is a lot of research focused on solving the folding patterns of proteins using computers (Folding@Home, https://fold.it/portal/, etc.).

The question that I have is: How do you know when you get it right? Is there some way of verifying, in silico, that you have found a legitimate/correct structure for a protein?

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    $\begingroup$ Normally you have an output in silico you'll have to verify by NMR, xray crystallography, or experimentally in other words. If your protein is particularly disordered, however, you must compile many techniques into a conformational ensemble as outlined here. The computational techniques provide lots of data but there's no 100% without experimental verification. $\endgroup$ – CKM May 18 '15 at 22:27
  • $\begingroup$ biology.stackexchange.com/questions/30240/proteins-folding $\endgroup$ – canadianer May 19 '15 at 1:39
  • $\begingroup$ @Kendall Although experimental analysis "trumps" modelling, there are methods to analyse the viability of a model (see my answer for more depth). $\endgroup$ – James May 19 '15 at 2:40
  • $\begingroup$ the best you can do is, modelling using I-TASSER server or may also prefer Rosetta package, but the question you asked is still a "million dollar question"... $\endgroup$ – diffracteD May 20 '15 at 9:58
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Overview

Modelling has come on leaps and bounds over the last decade or so and in many cases has acted as a sometimes viable, and inexpensive substitute for experimental structures.

How do you know when you get it right?

Ultimately, one still needs experimental evidence to know when a model generated in silico is right. But there are ways of scoring a model for how likely it is to be right.

Is there some way of verifying, in silico, that you have found a legitimate/correct structure for a protein?

There are lots of ways to score and verify your models. Each method tells you something slightly different about the merits, or lack thereof, of your structural model. Some are designed to weed out the obviously awful models and some allow you to detect exactly where your model looks to be accurate or inaccurate.

MODELLER Homology modelling output verification on the fly.

I am most familiar with modeller for homology modelling. Other softwares are available and they are each evaluated by CASP every two years since 1994.

In homology modelling there are 3 common scoring systems that can be used to assess the biochemical viability of a model. This email covers when to use each one. My answer expands and explains a bit more.

molpdf is the Modeller objective function. GA341, discussed here is derived from Z-score (calculated with a statistical potential function), which is a target-template sequence identity, and a measure of structural compactness. DOPE is a more up to date method, first published in 2006, and is more true to "biological viability". From the publication:

DOPE is based on an improved reference state that corresponds to noninteracting atoms in a homogeneous sphere with the radius dependent on a sample native structure; it thus accounts for the finite and spherical shape of the native structures.

Which to use depends on what you want to do with the model, but of those three scores, DOPE is the most reliable at separating native-like models from "decoys". DOPE is usually the starting place for figuring out which models might be right and which models are just plain rubbish.

Note: If you use Rosetta then there will be equivalents to these, or you can run your generated models through these techniques. If you are using SWISS MODEL that comes with it's own somewhat black box verification techniques but you can still export the model for further verification.

General model check against experimental data.

A further validation of homology modelling methods or other structural models is ProSA. ProSA provides a great visual representation of where the z-score lies amongst actual crystal and NMR structures. There are probably others that do similar functions, but this is my personal go-to to get an idea of where my structure lies among experimentally gathered structures.

Sensitive residue by residue verification.

Although the aforementioned methods examine each residue, they usually output an overall score. Residue by residue scores are also available and require a lot of careful interpretation. For example, if you are analysing catalytic activity, a surface looping region that scores poorly might not be an issue, but a core catalytic residue that scores poorly renders the model useless. This means that just because your model has a good (lower) overall DOPE score than another model, doesn't mean it is necessarily a more accurate model for what you are interested in.

There are plenty of sensitive modelling scoring systems. Some of which are XdVal, MTZdump, the famous albeit old-school Ramachandran Plotting method, pdbU, pdbSNAFU, PROCHECK, Verify3D, and ERRAT to name a few. Each has a place when checking how correct your model is.

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    $\begingroup$ Thanks, this is very informative. I was always wondering if there was some kind of "magic formula" which told you that your folding was correct. I'll take a better look at specifics of those models, but this now leads me to wonder how Folding@Home and others manage to "solve" protein structures. $\endgroup$ – Stack Tracer May 22 '15 at 7:26
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At this point, it must be verified experimentally.

In this foldit research paper, they use software and user input to design essentially an enhanced version of a naturally occurring protein, but they then physically make their new protein and determine its structure experimentally, using x-ray crystallography. Overall, they use a lot of trial and error http://homes.cs.washington.edu/~zoran/foldit-nbt-2012.pdf

Projects like this are kind of geared towards towards the goal of being able to determine a protein's structure from its amino acid sequence in silico. Once we achieve this ability, it will be revolutionary. However it is very difficult, because making such predictions accurately would require the use of quantum mechanics in a way that is extremely difficult to computationally model. These projects use shortcuts to get around this problem, so their results aren't very accurate, but they can be accurate enough to be useful, as shown in that paper.

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    $\begingroup$ Here you are discussing ab initio modelling which is complex as you state. However, "in silico" modelling also includes homology structural modelling, something you miss out entirely from the answer. Homology modelling is a much more established method and has been shown to be very accurate in many circumstances. The key is that structural information from similar known structures can aid the prediction. ab initio is only used in some rare instances if homology modelling cannot be used and experimental analysis is not available. $\endgroup$ – James May 19 '15 at 2:39

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