Dear colleagues, For teaching purposes, I am looking for a small number (< 5) of macromolecular diffraction datasets (raw images) that might be considered 'difficult' for a beginning crystallography student to process. By 'difficult' I generally mean not able to be processed automatically by a common processing package (XDS, Mosflm, DIALS, etc) using default settings, i.e., no black box "click and done" processing. The datasets I am looking for would have some stumbling block such as incorrect experimental parameters recorded in the image headers, multiple lattices that cause indexing to fail, datasets for which determining the correct space group is tricky, datasets for experiments in which the crystal slipped or moved in the beam, or anything else you can think of. The idea is for these beginning students to examine several datasets that highlight various phenomena that can lead one astray during processing.
A good candidate dataset would also ideally comprise a modest number of images so as to keep integration time to a minimum. Factors that are mostly irrelevant for my purpose: resolution (as long as better than ~3.5 Å), source (home vs synchrotron), presence/absence of anomalous scattering, presence/absence of ligands, monomeric vs oligomeric structures, etc. Also, to be clear, I am not looking for datasets that have so many pathologies that they would require many long hours of work for an expert to process correctly. I have checked public repositories such as proteindiffraction.org and SBGrid databank, but all of the datasets I acquired from these sources process satisfactorily with little effort, and in any event I know of no way to search for 'challenging' datasets. (I also wonder whether anybody is in the habit of depositing, shall we say, less-than-pristine images to public repositories?) If you know of such a dataset that is already publicly available, or if you have such a dataset that you are willing to share for solely educational purposes, I would appreciate hearing from you, either on- or off-list. Thank you in advance for your suggestions. Matthew -- Matthew J. Whitley, Ph.D. Department of Pharmacology & Chemical Biology University of Pittsburgh School of Medicine ######################################################################## To unsubscribe from the CCP4BB list, click the following link: https://www.jiscmail.ac.uk/cgi-bin/webadmin?SUBED1=CCP4BB&A=1
