More Protein Folding Progress – What’s It Mean?
I last wrote about Deepmind's efforts to predict protein folding and structure here, with their AlphaFold software. AlphaFold really performed very strongly in the 2020 protein folding challenge, and that got a lot of attention. If you have been working on computational protein folding yourself, odds are that you have had your doors blown off by these recent developments and will need to rethink. Protein X-ray structures depend on model-building as well; you try to see which structures best fit the experimental electron density data. In the case of protein folding and structure, it means that we now will spend more of our time on the harder stuff: protein complexes, the classification and function of protein surfaces in general, the effects of all the wide variety of post-translational modifications, the dynamics of protein conformation changes in real cell-biology time, the subtleties of how small-molecule ligands work their way in and out of binding sites, and the related question of how allosteric sites and cofactors modify these things from afar. Kinases have several regions that flop and scoot into different and easily accessible conformational states, and these structure prediction suites will not necessarily capture all of these, and they most certainly will not tell you which ones are associated with the active enzyme or would be more relevant to a protein's different functions in vivo. Many enzymes need cofactor molecules bound to them to do some of their work, and AlphaFold structures have no way to consider these - nor the presence of things like zinc or calcium ions that can also have a profound effect on protein structure and function.