Difference in water molecules count
Hi All, I have been refining the mutant structures and at the end while making the comparative study I found that there is a large difference (almost half) number of water molecules in the protein strucutre of same mutant from different crystal conditions. The resolution of the structure, space group and no.of molecules in a.s.u is same. Here is the data collection statistics and no.of water & protein atoms present. I could also not understand the difference of unique and measured reflections at same resolution. Could anyone please explain me? Resolution range (Å) 20-1.6 (1.64-1.60) 20-1.8 (1.85-1.80) 20-1.9 (1.99-1.94) 20-1.6 (1.63-1.59) 20-1.6 (1.64-1.60) Number of measured reflections 221864 (14118) 173933 (9556) 85976 (5129) 206723 (12667) 262291 (15803) Unique reflections 84896 (6139) 59955 (4010) 38628 (2777) 86161 (5992) 85305 (5803) Completeness (%) 98.7 (97.2) 95.9 (87.9) 77.6 (75.5) 93.9 (87.9) 97.8 (90.9) No of protein atoms 5101 5096 5076 5113 5120 No of waters 1103 528 453 585 1214 Thanx, World
On Tue, Feb 7, 2012 at 2:20 AM,
I have been refining the mutant structures and at the end while making the comparative study I found that there is a large difference (almost half) number of water molecules in the protein strucutre of same mutant from different crystal conditions. The resolution of the structure, space group and no.of molecules in a.s.u is same.
Here is the data collection statistics and no.of water & protein atoms present. I could also not understand the difference of unique and measured reflections at same resolution. Could anyone please explain me?
It depends on how many frames of data you collect and how well the integration worked. The fourth dataset in the list is both less redundant and less complete than the other two that go to 1.6A; the slightly lower data quality could have an impact on the map quality too. (Pavel and Sacha Urzhumtsev have found some interesting cases where a relatively small number of missing reflections really hurt the maps.) If you are using slightly different I/sigma cutoffs, that can also have a large effect, as can different levels of disorder in the crystal. What are the average protein and solvent B-factors for these structures? The other thing to check: how were these waters identified? If you're relying on completely automatic water-picking methods like the one in phenix.refine, it's possible that it missed many weaker (but still valid) map peaks in one dataset - you can still fill these manually and turn the solvent update off. -Nat
Hi Nat,
The average B-factors for the protein and solvents are:
Main chain
11.01
14.14
19.77
13.43
10.956
Side chain
13.07
17.22
21.44
16.76
12.648
Waters
30.22
29.39
28.49
30.23
26.08
Yes, the water molecules were identified automatically by phenix.refine
programme.
Regards,
World
Quoting Nathaniel Echols
I have been refining the mutant structures and at the end while making the comparative study I found that there is a large difference (almost half) number of water molecules in the protein strucutre of same mutant from different crystal conditions. The resolution of the structure, space group and no.of molecules in a.s.u is same.
Here is the data collection statistics and no.of water & protein atoms
On Tue, Feb 7, 2012 at 2:20 AM,
wrote: present. I could also not understand the difference of unique and measured reflections at same resolution. Could anyone please explain me?
It depends on how many frames of data you collect and how well the integration worked. The fourth dataset in the list is both less redundant and less complete than the other two that go to 1.6A; the slightly lower data quality could have an impact on the map quality too. (Pavel and Sacha Urzhumtsev have found some interesting cases where a relatively small number of missing reflections really hurt the maps.) If you are using slightly different I/sigma cutoffs, that can also have a large effect, as can different levels of disorder in the crystal. What are the average protein and solvent B-factors for these structures?
The other thing to check: how were these waters identified? If you're relying on completely automatic water-picking methods like the one in phenix.refine, it's possible that it missed many weaker (but still valid) map peaks in one dataset - you can still fill these manually and turn the solvent update off.
-Nat _______________________________________________ phenixbb mailing list [email protected] http://phenix-online.org/mailman/listinfo/phenixbb
On Tue, Feb 7, 2012 at 6:29 AM,
The average B-factors for the protein and solvents are: Main chain [snip] Yes, the water molecules were identified automatically by phenix.refine programme.
Okay, so the 1.6A structure with significantly fewer waters has slightly higher B-factors overall. Since many of the waters will be defined largely (entirely?) by high-resolution data, and the cutoffs used by phenix.refine are relatively conservative, it wouldn't surprise me if it's just missing a lot of weaker sites - and there probably really are fewer to find anyway. At the end of refinement I don't think you should rely on phenix.refine to find all of the waters anyway; try using the water-picking in Coot, filter out the obviously wrong ones, and refine a final time without solvent update. (This applies to the other datasets as well, at least the two at 1.6A - it wouldn't surprise me if there are more waters to be found.) For the obsessive-compulsive or just curious, there is now a standalone tool in the nightly builds for looking at difference maps: mmtbx.find_peaks_holes model.pdb data.mtz write_pdb=True This is basically recycled from phenix.refine but since the idea wasn't to add waters, but to make it possible to cycle through the map peaks in Coot, it's slightly more aggressive and thorough. (The novel features are incorporation of the anomalous difference map if available, and identification of "waters" with suspicious mFo-DFc peaks.) We'll probably merge this with phenix.refine at some point. -Nat
participants (2)
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bsubedi@btk.fi
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Nathaniel Echols