[chimerax-users] Could you please inform the best advice to solve a structure via colabfold when GPU is an issue? I tried some routes already.

[Josh Gutierrez]

Hi Elaine and Tom,

Thank you for all the helpful information and suggestions! I will try all
of the following and get back to you.

Many thanks,
Josh

On Fri, Apr 14, 2023, 4:03 PM Tom Goddard <goddard at sonic.net> wrote:

> Hi Josh,
>
>   Predicting 1300 residues is at the limit of what the ChimeraX AlphaFold
> tool can do because it uses Google Colab GPUs which have only 12 or 16 GB
> of memory.  With Colab Pro ($10/month) I never got a GPU with more than 16
> GB.  But maybe there is some way if you research how online.  A GPU with 24
> GB would easily handle 1300 amino acids.
>
>   You might try the ColabFold web page (not ChimeraX) since it is using
> AlphaFold 2.3 which is supposed to handle larger structures with the same
> memory.  ChimeraX uses an older ColabFold version using AlphaFold 2.2 or
> 2.1 but will be updated in coming months.
>
>   Setting up your own AlphaFold on a Linux machine with a GPU with more
> than 16 GB is some trouble because it uses a few TBytes of databases.
>
>   In the future I plan to allow ChimeraX to run AlphaFold jobs on your own
> paid cloud GPU account (e.g. AWS, Google Cloud, Lambda Cloud) and you could
> just pay to use a GPU with more memory.  But that is not likely to be
> working until then end of 2023.
>
>   If ChimeraX or ColabFold does not predict your 1300 residue structure
> with reasonable confidence in 2 pieces it is very unlikely to do better run
> as a single piece.
>
>         Tom
>
>
> > On Apr 14, 2023, at 3:17 PM, Elaine Meng via ChimeraX-users <
> chimerax-users at cgl.ucsf.edu> wrote:
> >
> > Hi Josh,
> > I'm no expert in this area but here are a few thoughts:
> >
> > (1) make sure that your protein is not already available in the
> AlphaFold Database, e.g. did you try the Fetch option on the tool?
> > <
> https://urldefense.proofpoint.com/v2/url?u=https-3A__rbvi.ucsf.edu_chimerax_docs_user_tools_alphafold.html-23match&d=DwIFAg&c=slrrB7dE8n7gBJbeO0g-IQ&r=FSRBqwcPG7hQubFJBi1gYQ&m=YcZWpQIIJ2_oO9iEVp2ZOzYT8K-X2sIevJ67rKb6blO_Y_R0OI4Zfa1a97PgIYT8&s=fR-otWz2Rg0Qgtr8mAolwsnayY5cgP2KgqUaiCYRuDc&e=
> >
> >
> > (2) for parameter settings, you might consider asking the ColabFold
> developers (see authors on the ColabFold publication in the ChimeraX
> AlphaFold documentation),
> > <
> https://urldefense.proofpoint.com/v2/url?u=https-3A__rbvi.ucsf.edu_chimerax_docs_user_tools_alphafold.html-23predict&d=DwIFAg&c=slrrB7dE8n7gBJbeO0g-IQ&r=FSRBqwcPG7hQubFJBi1gYQ&m=YcZWpQIIJ2_oO9iEVp2ZOzYT8K-X2sIevJ67rKb6blO_Y_R0OI4Zfa1a97PgIYT8&s=Tyw0CR2BZZvn6HzUYAmLzvAZJSxnspb0Pj93i9ObjPU&e=
> >
> >
> > ...but perhaps some of these parameters are not adjustable via the
> ChimeraX tool.  If so, you'd need to run ColabFold directly instead.  I
> don't know if the UCSF professor you're collaborating with is in one of the
> groups with access to the UCSF Wynton cluster, but there is information
> about setup and running on the Wynton cluster here:
> > <
> https://urldefense.proofpoint.com/v2/url?u=https-3A__www.rbvi.ucsf.edu_chimerax_data_singularity-2Dapr2022_afsingularity.html&d=DwIFAg&c=slrrB7dE8n7gBJbeO0g-IQ&r=FSRBqwcPG7hQubFJBi1gYQ&m=YcZWpQIIJ2_oO9iEVp2ZOzYT8K-X2sIevJ67rKb6blO_Y_R0OI4Zfa1a97PgIYT8&s=lJ6geGkbmg9Z6sVHshh4zJR2GriO55PvMSRlXvcp2Ms&e=
> >
> >
> > (3) as mentioned in the ChimeraX AlphaFold documentation (top link
> above), you can pay Google for Colab accounts that provide greater
> computing resources.  Sounds like you already tried that, but with high
> predicted error.  It is unclear, however, whether this structure would have
> high predicted error regardless of whether you solved it in multiple pieces
> or a single piece.  Some proteins may simply have lots of
> disordered/unstructured regions, and/or maybe there is not much sequence
> data available for homologs of this protein to help constrain the problem.
> >
> > (4) consider trying ESMFold, for which there is also a tool in ChimeraX
> analogous to the AlphaFold one.  There are database fetch and
> new-prediction options.  Still, I don't know if this approach would work in
> your specific case either.
> > <
> https://urldefense.proofpoint.com/v2/url?u=https-3A__rbvi.ucsf.edu_chimerax_docs_user_tools_esmfold.html&d=DwIFAg&c=slrrB7dE8n7gBJbeO0g-IQ&r=FSRBqwcPG7hQubFJBi1gYQ&m=YcZWpQIIJ2_oO9iEVp2ZOzYT8K-X2sIevJ67rKb6blO_Y_R0OI4Zfa1a97PgIYT8&s=AQ3K7Ki8G0FrLPVDSRJNUp6TPGsw_egRhVgYwbvg_aQ&e=
> >
> >
> > I hope this helps,
> > Elaine
> > -----
> > Elaine C. Meng, Ph.D.
> > UCSF Chimera(X) team
> > Department of Pharmaceutical Chemistry
> > University of California, San Francisco
> >
> >> On Apr 14, 2023, at 2:52 PM, Josh Gutierrez via ChimeraX-users <
> chimerax-users at cgl.ucsf.edu> wrote:
> >>
> >> Hello,
> >>
> >> I recently submitted a job through ChimeriaX and used the "prediction"
> task for it to be solved via Alphafold2-- the sequence is about 1300 amino
> acids in length. However, the protein could not be solved by alphafold2 and
> the colabfold reports the GPU consumption is too high for the structure to
> be solved. To resolve the problem, I tried breaking the sequence into two
> to solve this expected bimodal protein individually (into two separate
> structural parts) and even in combination purchased "compute units" for
> more memory and storage to do this, but the structure can't be solved
> without high predicted error.
> >>
> >> Please let me know what you think may  resolve this issue. Currently, I
> am working with an external professor who may be able to connect me with a
> collaborator on this issue at UCSF if there is a cluster or core we can use
> to expedite the process to solve this structure I am addressing. Please let
> me know your thoughts at your earliest convenience. Please also let me know
> if you need any more information!
> >>
> >> Many thanks,
> >> Josh Gutierrez
> >
> >
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> >
>
>
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