Refinement using a homolog as a reference
Hi, I am refining ligand-protein complexes at 3.2Å dataset with a human protein. My Rfree is ~0.34 but my ramachandran stats are concerning me: 85% favoured, 4.5% disallowed. The structure of a yeast homolog has been deposited at 2.4Å resolution. The yeast structure contains additional loops but is ~55% identical to the human protein. The overall conformation between the homologs is very similar. Can I use this as a reference model? Should I prune the reference model? Will different numbering schemes affect the refinement? Many thanks, Amar
Hi Amar,
Yes, can use the homolog as a reference model. The method will do a
secondary structure-based alignment, so the sequence differences shouldn't
matter. You don't need to prune the model, as this will be handled
automatically. The differing numbering schemes should be handled
automatically, and you can check the log file to see which residues are
matched to assure that you are seeing the behavior you expect.
If you find that it is not matching the residues correctly, let me know and
I would be happy to help you offline.
Jeff
On Thu, Jul 25, 2013 at 4:44 AM, Joshi, Amar (Dr.)
Hi,
I am refining ligand-protein complexes at 3.2Å dataset with a human protein. My Rfree is ~0.34 but my ramachandran stats are concerning me: 85% favoured, 4.5% disallowed.
The structure of a yeast homolog has been deposited at 2.4Å resolution. The yeast structure contains additional loops but is ~55% identical to the human protein. The overall conformation between the homologs is very similar.
Can I use this as a reference model? Should I prune the reference model? Will different numbering schemes affect the refinement?
Many thanks, Amar _______________________________________________ phenixbb mailing list [email protected] http://phenix-online.org/mailman/listinfo/phenixbb
Hello, All We are wondering if the GPU computing will help significantly or not on phenix autosol, model building and refinement. Has anyone used such machines and how is the experience? Would you like to share with us? Thank you. Rongjin Guan
On Thu, Jul 25, 2013 at 8:05 AM, rjguan
We are wondering if the GPU computing will help significantly or not on phenix autosol, model building and refinement.
Not at all, especially since we don't have any code for this in place. GPUs really aren't very useful for macromolecular crystallography, unfortunately.
Has anyone used such machines and how is the experience? Would you like to share with us?
Several of us at LBL (Nick Sauter, Peter Zwart, Billy Poon, and myself) have done some experiments with the high-end NVidia chips. Basically they work very well for certain specialized tasks, which don't really apply to MX. I tried the simplest route, which was to call the CuFFT library, and it's about 20x faster than a single Intel CPU core, but once you add in the memory transfer it's only about 4x faster, and that's not really a fair comparison since the system had 1 GPU and 8 CPU cores. To really take advantage of the speedup we'd need to port other calculations to the GPU as well. I'm pretty certain we could make classical density modification (i.e. charge flipping) and maximum entropy calculations at least an order of magnitude faster on a GPU, but these aren't really rate-limiting steps in the overall process. Speeding up phenix.refine ultimately requires algorithmic improvements, not expensive proprietary hardware. -Nat
participants (4)
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Jeff Headd -
Joshi, Amar (Dr.) -
Nathaniel Echols -
rjguan