Hi Patrick Take a look at the Binet-Cauchy kernel, e.g. here: http://bioinformatics.oxfordjournals.org/content/30/6/784.long It is a shape-correlation metric that is rather insensitive to global shape changes, but sensitive to local ones. Maybe that's what you need. It's also much faster to compute than RMSD. I have attached a python code snippet that computes a BC distance matrix for a set of proteins defined by CA coordinates. Kind regards, Dmytro. On 02/07/14 17:15, Patrick. C wrote:
Hi Phenix users,
I am not a crystallographer but I though you guys might be a good place to ask this question.
I have 2 super secondary structures, A and B and they consist of Helix-turn-Strand
Due to the turn the two structures have a poor RMSD because the two flanking fragments of Helix and Strand are far from each other but when I superimpose the two fragments individually(helixA with helix B and standA with strandB in Pymol they align very well).
Now, is there a way to express this instead of using the RMSD? When the two structures align well the RMSD is very good but a slight movement and the RMSD is awful. But looking at the two structures I can see they follow the same path through space.
Thank you, Patrick
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