The problem (partially):
Typically, proteins have only one correct configuration. Trying to virtually simulate all of them to find the right one would require enormous computational resources and time.
On top of that there are factors concerning translational-regulation. As the protein chain is produced in a step-wise fashion on the ribosome, one end of a protein might start folding quicker and dictate how the opposite end should fold. Other factors to consider are chaperones (proteins which guide its misfolded partner into the right shape) and post-translation modifications (bits and pieces removed and/or added to the amino acids), which all make protein prediction even harder. That is why homology modelling or “machine learning” techniques tend to be more accurate. However, they all require similar proteins to be already analysed and cracked in the first place.
The solution:
Rather than locking another group of structural shamans in a basement to perform their biophysical black magic, the “Fold It” team created a game. It uses human brainpower, which is fuelled by high-octane logic and catalysed by giving it a competitive edge. Players challenge their three-dimensional problem-solving skills by trying to: 1) pack the protein 2) hide the hydrophobics and 3) clear the clashes.
Read the post or jump to the Foldit site.
Seems to me there are a lot of subject identity and relationship (association) issues that are a lot less complex that protein folding. Not that topic mappers should shy away from protein folding but we should be more imaginative about our authoring interfaces. Yes?