Hi Matt, Im wondering about occupancy refinements - both what's going on under the > hood and what are best practices. >
since you are quoting Phenix I suggest this bit of reading that is somewhat relevant to your questions: https://phenix-online.org/phenixwebsite_static/mainsite/files/newsletter/CCN_2015_07.pdf#page=12 This documents 13 typical occupancy refinement scenarios and how they can be handled in Phenix. > In the example I have, there is a ligand found in two distinct, partially > overlapping sites that can be modeled is some confidence, but likely there > are very low occupancy additional poses that blurs the electron density. > The modeled poses are known from prior work, so even though there is > smearing we know the ligand is in the modeled conformation. After > perturbing the crystal these I am trying to decide what the best approach > is to get some sort of numerical occupancy value to describe the > distribution. > I apologize in advance for this trivial statement, but refinement + validation are the tools to answer your question. If you can model these poses with an atomic model and prove they match the experimental diffraction data ("The modeled poses are known from prior work" doesn't count in this regard), then you are all good! Depending on the size ration ligand:whole structure, R factors may or may not be useful quantifiers of the modeling choices. So your best bets may be local quantities, such as refined ligand group occupancy, flatness of Fo-Fc map (assessed as map values at atom positions), local map-model CC, etc. Try to challenge your modeling decisions by placing similar to expected answers but knowingly wrong models and see how that changes the fit/quality criteria -- that may give you a way to assess uncertainty. 1. In Phenix, how is the occupancy number determined? Please refer to the paper I mentioned above, and let me know if you have additional questions. > Is there a real-space correlation between the experimental density and the > model(weighted to occ) that is optimized? Yes, but very indirectly through optimization of overall standard ML target function. > How can this go wrong? It can go wrong in as many ways as the refinement target function has local minima, that is in many thousands ways! > I fear that the smearing and heterogenous nature will through the > refinement off (over or underfitting to periphery density rather than > hyper-localized position of the model) > That is a valid concern that is good to keep in mind but that is not a show-stopper! > 2. Are there errors associated with the occupancy numbers? > Yes, like with any model refinable parameter. For some discussion please see: https://www.nature.com/articles/s41467-018-06957-w > > 3. For my own testing, I did 5% increments and manually observed Fo-Fc > and 2Fo-Fc maps and selected a value that resulted in the lowest amount of > both positive and negative Fo-Fc peaks. This is how we submitted the work > to journal but reviewer wants it to be automatically calculated. Above mentioned paper is now more relevant in light of this question! Yes, you can do manual sampling to get starting values (because the closer they are to the true values, the better chances refinement is successful), then do some refinement starting with these values. Good luck! Pavel ######################################################################## To unsubscribe from the CCP4BB list, click the following link: https://www.jiscmail.ac.uk/cgi-bin/WA-JISC.exe?SUBED1=CCP4BB&A=1 This message was issued to members of www.jiscmail.ac.uk/CCP4BB, a mailing list hosted by www.jiscmail.ac.uk, terms & conditions are available at https://www.jiscmail.ac.uk/policyandsecurity/
