[chimerax-users] unifold

[Tom Goddard]

Hi Roger,

  Unfortunately the Google Colab notebooks for AlphaFold and clones often have problems causing predictions to fail.  There are many reasons for the failure, but all of them come down to poor maintenance of these free prediction services.  Here are examples of why they have failed: Google Colab updates Python version (to 3.7 to 3.8 to 3.9) without advance warning, libraries being used update (most often jax gpu calculation) and break the code.  Often it takes a week for Google or other Colab notebooks to fix the problem.   I try to fix the breakages within a day or two for ChimeraX.  I think the ColabFold notebook (https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb) is one of the more reliable ones.

  It is not too surprising that these free protein structure prediction services fail frequently.  It will be interesting to see what happens when one day Google decides that free Colab is too expensive to maintain and pulls the plug on it.

  Another problem with all the AlphaFold-like notebooks is that Google Colab has old GPUs, typically 12 or 16 GB of memory, and this limits the size of the predicted structures.  Even high-end desktop graphics like an Nvidia RTX 3090 has 24 GB and handles larger structures, while graphics intended for machine learning often has 40 GB, 48 GB, or 80 GB.  Here at UCSF people who often use predictions run them on our UCSF machines.  Of course lots of work goes into maintain our software and hardware.

  Tom

> On Apr 12, 2023, at 3:25 PM, Roger Leng via ChimeraX-users <chimerax-users at cgl.ucsf.edu> wrote:
> 
> Many thanks, Tom.
> I watched your lots of "youtube". It is great to learn and use ChimeraX. I tried unifold.ipynb (online), but, un-luck, failed several times with paid program. Interestingly, I failed several times to use alphafold2.ipynb (online); but I used ChimeraX with Alphafold successfully for complex prediction. 
> In addition, I was told that RoseTTAFold (Linux) could use for complex prediction. I am installing RoseTTAFold in Linux. Again, I filed several times to use RoseTTAFold.ipynb (online). Actually, I do not understand why I always filed to use AlphaFold or RoseTTAFold online (.ipynb with the paid program, Pro+).
> Thank you again.
> Have a great day!
> Roger
> Roger Leng, 
> Faculty of Medicine,
> University of Alberta,
> Canada
> 
> On Wed, Apr 12, 2023 at 4:05 PM Tom Goddard <goddard at sonic.net <mailto:goddard at sonic.net>> wrote:
>> Hi Roger,
>> 
>>   Unifold looks interesting.
>> 
>> 	https://github.com/dptech-corp/Uni-Fold
>> 
>> It is an open source reimplementation of AlphaFold using the PyTorch machine learning framework done by a China-based company called DPTech (https://www.dptech.com/).  From reading the github page it sounds like one of its main advantages over AlphaFold is that the training code and protocol is all open source.  Code for the training of AlphaFold was never made available as far as I know.
>> 
>>   That is all nice, but what are the best reasons for our UCSF lab to try to offer this in ChimeraX?  The ChimeraX AlphaFold user interface took about 2 months of development with ongoing maintenance.  I'd estimate the cost so far at $30,000.  ChimeraX is funded by NIH grants.  Is it worth expending similar resources to implement and maintain an interface to UniFold?
>> 
>> 	Tom
>> 
>> 
>>> On Apr 12, 2023, at 2:24 PM, Roger Leng via ChimeraX-users <chimerax-users at cgl.ucsf.edu <mailto:chimerax-users at cgl.ucsf.edu>> wrote:
>>> 
>>> Dear Administrators,
>>> 
>>> Is it possible to add "unifold" in your prediction, just like Alphafold?
>>> 
>>> Thank you.
>>> 
>>> Sincerely,
>>> 
>>> Roger Leng
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