AlphaFold 2 paper released: "Highly accurate protein structure prediction with AlphaFold", Jumper et al 2021

by gwern15th Jul 20216 comments

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Related development: https://www.nature.com/articles/d41586-021-01968-y

"Meanwhile, an academic team has developed its own protein-prediction tool inspired by AlphaFold 2, which is already gaining popularity with scientists. That system, called RoseTTaFold, performs nearly as well as AlphaFold 2, and is described in a paper in Science paper also published on 15 July[2] "

Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even where no similar structure is known.

Holy crap. I confess this one catches me by surprise; within my hopes, but beyond my expectations.

Pretty sure this is the same (impressive) news as from CASP14 ( https://www.blopig.com/blog/2020/12/casp14-what-google-deepminds-alphafold-2-really-achieved-and-what-it-means-for-protein-folding-biology-and-bioinformatics/ ). But with fancier figures (edit: and more technical details of how they made the predictions) :P

The previous AF2 discussions were largely a waste of space because the little abstract they had to provide for CASP14 provided hardly anything to go on. But now we have not just a full writeup but source code and models too! Now I consider it worth discussing.

As I recall the accuracy measurement was something of an average over the whole molecule deviation which could then allow small portions (local) of the predicted shape to differ from the true shape a good bit more.

First, is that a correct recollection? If so, does anyone know of any work on exploring the importance of local deviations from the global averaged type metrics? I would think that would be very important in this type of modeling.