This sounds like a job for a DDM (difference-distance matrix). This method can detect very subtle conformational changes between a pair of protein structures without performing a structural alignment. Once the areas of change have been identified a traditional alignment can be performed using the conformationally inert regions as a target.
The program ESCET can do this and full details can be found here: http://schneider.group.ifom-ieo-campus.it/escet/index.html Alternatively DDMP could be used: http://www.csb.yale.edu/userguides/datamanip/ddmp/ddmp_descrip.html Hope that helps, Tom On Wed, Jan 7, 2009 at 2:18 PM, Nathaniel Echols <nathaniel.ech...@gmail.com > wrote: > On Wed, Jan 7, 2009 at 1:54 PM, Jacob Keller < > j-kell...@md.northwestern.edu> wrote: > > I am sure that most here have dealt with the issue, when making > superpositions of conformationally-different structures, > of which > regions to align as references and which to call "mobile." Conformational > changes can range from very local (e.g., > > unwinding of a helix) to very diffuse (e.g., subtle but significant rigid > body shifts between two domains.) In the first case, > > it would probably make sense to do a global least-squares fitting, but in > the latter, one would do better to fix one of the > > domains, and show the shift in the other domain. These cases, however, > presuppose that one knows which type of case > > one is dealing with. This could be done by guesswork and trial-and-error, > but does anybody know of an approach (e.g., a > > program) to define the most reasonable way to think about a given > conformational change? Variable-size sliding-window > > least-squares superpositions with comparisons of local versus global > rmsd's come to mind, but I do not know whether > > this has been implemented anywhere, and would not know readily how to set > the parameters thereof either. > > DynDom may do this, but I'm not familiar with the program. (It's in CCP4 > now, I think) > > If you're just trying to get a reasonable superposition and don't care very > much about the resulting statistics, you can usually use a much simpler > method called a "sieve-fit", described in these references: > > http://www.ncbi.nlm.nih.gov/pubmed/2067013 > http://www.ncbi.nlm.nih.gov/pubmed/10734184 > > In practice, the procedure described in the second paper generally worked > very well for the intended purpose of visualizing any arbitrary > conformational change in the PDB clearly. The code that actually performs > this isn't distributed as far as I know; however, it should be relatively > trivial to re-implement using CCTBX or something equivalent. > > PyMOL's "align" command also does some kind of iterative optimization by > throwing away outliers, but it's much less aggressive and appears to try for > the best global fit, excluding loops etc. > > -Nat >
