Hi everyone, I have a series of datasets at 253K (~2.0A), 273K (2.0A), 293K (2.0A), 313K (2.2A) and I am curious as to the details in determining B-factors.
I have treated these datasets more-or-less identically for comparison's sake. I used DIALS to index, integrate, and scale the data. I scaled the data to a ~0.6 CC1/2 cutoff. After fully refining the datasets, there is an odd trend with respect to temperature (from what has been previously published) and I assume that this is because of "behind-the-scenes" computation rather than a biophysical observation. The B-factors slightly decrease from 252-293K, and then significantly drop at 313K. The maps look pretty well identical across the datasets. 253K - 53.8 A^2 273K - 48.4 A^2 293K - 45.5 A^2 313K - 18.6 A^2 I compared the wilson intensity plots from DIALS scaling for 273K and 313K and they are very comparable. I am looking for suggestions as to where to look at how these b-factors are selected or how to validate that these B-factor are or are not accurate. Also, any relevant literature would be welcomed. From what I have read, there is a general trend that as T increase, the atoms have more thermal energy which raises the b-factors and this trend is universal when comparing datasets from different temperatures. Thank you and happy to supply more information if that is helpful, Matt ######################################################################## 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/
