Two years ago, AlphaFold dominated the free modeling category at CASP13. This year's CASP14 results are out, and AlphaFold has been acknowledged for solving the 50-year-old grand challege of protein structure prediction.
https://deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology
Will the Alphafold ability of predicting folding change what we are doing in fold.it? Shall we stop folding proteins?
Good question, maithra.
This is already being discussed at the CASP meeting that started today:
The question is what Deep Learning can do for protein design!
David Baker is presenting a "Protein design and covid" talk at this Friday's Covid CASP session.
Nature opinion piece on the importance of this result:
https://www.nature.com/articles/d41586-020-03348-4
After a multi-year sabbatical, I didn't come back to FoldIt until April of this year. Did we participate in CASP14? If not, why not? I don't remember reading anything about it here until this post.
The recent CASP targets have tended to be on the large side, and you have to work on each target to compete. Plus those robots are getting better and better.
Here's what beta_helix had to say about it in 2018, after CASP 13: https://fold.it/portal/node/2006272#comment-37895
The way CASP has worked since CASP 11 is that groups must submit models for all targets in a particular category in order to be evaluated.
Since Foldit cannot handle the puzzle load, not to mention the many targets that are over 300 residues, we have focused on protein design and ED… where we did use CASP 13 targets: https://doi.org/10.1371/journal.pbio.3000472.s028
More commentary here:
https://blogs.sciencemag.org/pipeline/archives/2020/11/30/protein-folding-2020
After the CASP14 conference is over, I would like to see an article from the management re: "What is the future of FoldIt?"