[phenixbb] Detecting and dealing with Pseudotranslation and/or twinning
Thomas C. Terwilliger
terwilliger at lanl.gov
Thu May 12 14:31:51 PDT 2011
Hi Brent,
I am not sure of the solution, but I have a question that might possibly
give another clue.
You have 4 copies in the a.u., but your autosol solution does not show
ncs. Did this run go ahead and carry out phase_and_build..and was ncs
found there?
If not...can you run phenix.find_ncs resolve_30.mtz (or whatever the best
map is). Does this find ncs? If so...what are the operators? Do they have
a simple relationship to the cell?
All the best,
Tom
>> Ive been working on a troublesome protein structure. The native protein
>> forms crystals that diffract to 2.75A and belong to P212121 (55.179 64.316
>> 233.748 90.000 90.000 90.000) with 4 molecules in the ASU. I have 3
>> versions of the same protein where selenomethionine mutations are
>> incorporated at different positions. Interestingly, these mutations cause
>> the protein to form crystals belonging to C2221 (56.130 64.665 240.854
>> 90.000 90.000 90.000). Looking back at the native datasets, Xtraige
>> indicates the largest Patterson peak is (0.5, 0.486, 0), height is 11.8%
>> of the origin peak, and p_value(height) is 0.08549, which is just outside
>> of the threshold for being identified as containing pseudotranslation.
>> Datasets from a couple of the selenomet incorporated crystals yield
>> diffraction to ~3.5A and anomalous signal to ~6.7A. Some of the datasets
>> give a solution with reasonable maps, but the best maps are achieved from
>> combining MAD/SAD selenomet datasets and one from a mercury derivatized
>> crystal using the group command in Phenix.
>> ---------------------------------------------------------------------------
>> 5:
>> STEP: finished
>> Top solution: # 39 Dataset #0
>> BAYES-CC: 69.2 +/- 13.4 FOM: 0.6
>> Built: 219 Side-chains: 44 Chains: 9 CC: 0.74
>>
>>
>> Score type: SKEW CORR_RMS NCS_OVERLAP
>> Raw scores: 0.41 0.82 0.00
>> 100x EST OF CC: 69.17 43.34 31.25
>>
>> ---------------------------------------------------------------------------
>> Maps from this solution show connected electron density that looks like
>> helices, consistent with the predicted secondary structure. Strangely,
>> there is absolutely no side-chain density, only c-beta at most. I can
>> build a poly-ala model into the map and the distances between the heavy
>> atom sites appear correct based upon the known positions of the
>> selenomethionines and the single cysteine in the protein sequence.
>> However the model does not refine. R-free starts and remains near 0.45.
>> Ive tried indexing in lower symmetry space groups (P2, C2, P1) and
>> re-solving by molecular replacement, but the refinement still fails.
>>
>> Xtriage does not indicate twinning.
>>
>> Twinning and intensity statistics summary (acentric data):
>> Statistics independent of twin laws
>> <I^2>/<I>^2 : 2.305
>> <F>^2/<F^2> : 0.758
>> <|E^2-1|> : 0.788
>> <|L|>, <L^2>: 0.501, 0.333
>> Multivariate Z score L-test: 1.643
>>
>> The multivariate Z score is a quality measure of the given
>> spread in intensities. Good to reasonable data are expected
>> to have a Z score lower than 3.5.
>> Large values can indicate twinning, but small values do not
>> necessarily exclude it.
>>
>> One possible clue as to what is going on comes from analysis of SOLVE
>> results. I was analyzing whether SOLVE/PHENIX solutions were related with
>> one another by various origin shifts and came across one particular SOLVE
>> run from a SeMet SAD dataset in C2221 that gave good statistics for a
>> solution FOM=0.57, Z-score=20.26, peak height between 7.1 and 10.8 for 4
>> SeMet sites to 3.8A). The maps, however, looked poor. What was
>> interesting, though, was that 2 of the Se sites matched well with where I
>> was expecting the Se sites in one molecule in the asymmetric unit. The
>> other two matched with where I would expect the sites in the other
>> molecule when the model is shifted by one half the unit cell distance
>> along the a or b axis.
>>
>> Id appreciate any advice as to what might be happening, and how might I
>> go about detecting the problem, and how to dealing with the data?
>>
>> Brent Hamaoka
>> UC San Diego
>> 9500 Gilman Drive 0375
>> La Jolla, CA 92093
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>>
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