CASP13 will be held December 1-4, 2018, at the Iberostar Paraiso Maya resort (site of the CASP11 conference in 2014, destination airport is Cancun - CUN).

Abstracts of conference are public
And Rosetta@Home is citated (page 23)

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Group performance
Baker team has passed from 1st position in CASP12 to 22th position in CASP13
Guys, it's time to work hard on rosetta code!!

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There is also a "BAKER-ROSETTASERVER" (whatever that is) in 31st position.
I am sure they are still doing great work though, just in a different way.

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I am sure they are still doing great work though, just in a different way.

I'm agree with you.
A comment of David Baker about AlphaFold.
AI in the "folding field" will be a new era

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AI in the "folding field" will be a new era

Quite so. I am sure that everyone who can possibly use it will be rushing to figure out how.
I wonder if it can be used to control nuclear fusion reactions? That is the sort of semi-chaotic thing that it might be able to do.

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With the winner of the competition being a combo of neural network models which can generate protein structure predictions nearly instantaneously, I am wondering if there are other uses the Rosetta software has beyond protein prediction? If not, are there other benefits of the software, or are we going to start seeing structure predictions being made by the network?

Thanks.

Did some research into my question above and thought I would share. If anyone knows an area where my explanation is incorrect or incomplete, please respond as I am wholly ignorant in the field of biology and only know a very small bit about machine learning.

TL;DR there is plenty of room for Rosetta to continue generating large amounts of value as AlphaFold does not necessarily replace the software. In fact, when combined with AlphaFold, its utility may be multiplied.

So for those who are not familiar with the Rosetta software, its goal is to compute the 3D structure of a protein given the sequence of amino acids which composes it. At a high level, it does this by computing the thermodynamic energy associated with possible 3D states of the protein in search of the state with the lowest thermodynamic energy; this state is oftentimes the final 3D state of the protein. With that out of the way, here are some notes:

1. The innovations of AlphaFold focus on a different part of the protein structure prediction problem than Rosetta

Explained in the DeepMind team's blog post on AlphaFold [1], the team's innovations focused on solving two subproblems which they use to create the final protein structure. The first subproblem is predicting the angles between every consecutive pair of amino acids in the chain; the second subproblem is predicting the distances between every pair of amino acids in the chain. The advances made by the DeepMind team were in solving these two subproblems using neural networks, a type of machine learning model. Using these two sets of information, they can create a candidate 3D structure using another software - for example, Rosetta.

2. AlphaFold incorporates folding software in building their final structure prediction

According to Professor Jinbo Xu's (heading the RaptorX team for the CASP competition) Reddit comment on the /r/MachineLearning forum [2], AlphaFold "employed Prof. David Baker's Rosetta to build 3D models from the predicted distance". On the other hand, Professor alQuraishi, another participant in the CASP competition, claims in his blog post that AlphaFold used a simple gradient descent technique called L-BFGS, rather than Rosetta (which is also a gradient descent software, but with more bells and whistles), as the "folding engine".

In the case where the Rosetta software was used, I believe the idea here is that the AlphaFold neural networks provides the Rosetta software information to construct a template 3D model, and then incrementally improve on that 3D model using Rosetta software. In other words, it was the Rosetta software which outputted AlphaFold's final prediction! In fact, Professor Xu raises the possibility that AlphaFold's prediction technique, which followed a similar strategy to his own, outperformed his CASP entries because AlphaFold used a sophisticated structure modeling software (i.e. Rosetta) and his did not. (on the other hand, Professor alQuraishi says "perhaps there is further gain to be had by combining AlphaFold’s approach with something like I-Tasser or Rosetta, but AlphaFold’s preliminary results seem to suggest that they’ve already squeezed out what can be had from a better folding engine")

In my opinion, this is good news either way since the opportunity for Rosetta to improve predictions generated by these new techniques is clear.

3. AlphaFold and other 'co-evolution' methods may find difficulty in application to new protein sequences (in particular de novo designed proteins

Professor alQuraishi's blog post says AlphaFold's method of predicting distances between pairs of amino acids is a "co-evolution" based technique. If I am reading this correctly, this means that the distance-based predictions rely on looking at looking at which amino acids have historically been clustered together in proteins with similar amino acid sequences. This means that for wholly new sequences of proteins ("de novo" proteins), AlphaFold may not draw on such historic information and thus would not have good performance. Incidentally, a big area of Professor Baker's current research is "de novo" protein design using the Rosetta software; this shows an area of research where Rosetta continues to shine and (in my uninformed opinion) an area where machine learning is unlikely to encroach upon in the near future because of a lack of training data.

[1] AlphaFold blog post: https://deepmind.com/blog/alphafold/

[2] Jinbo Xu's explanation on Reddit: https://www.reddit.com/r/MachineLearning/comments/a2oaiy/r_alphafold_using_ai_for_scientific_discovery/ec342m6

[3] https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp13-what-just-happened/[/i][/url]

In my opinion, this is good news either way since the opportunity for Rosetta to improve predictions generated by these new techniques is clear.

Thank you for de-mystifying that subject matter, to the extent possible. It appears that Rosetta can be adapted for use by a variety of projects.

As I have posted elsewhere on this forum, the Baker lab is also collaborating with Folding@home. That raises the intriguing possibility of combining CPUs with GPUs for certain problems.
https://apps.foldingathome.org/project.py?p=14150
I hope we see more of it.

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Thank you for de-mystifying that subject matter, to the extent possible. It appears that Rosetta can be adapted for use by a variety of projects.

Remembert that this is the first public version of AlphaFold, and it's not bad for a newbie in CASP.
The "collaboration/coordination" between Rosetta, Foldit and (maybe) AlphaFold in the future will give, imho, a lot of new possibilities.

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