How can computer predictions of protein folding be verified computationally?
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.