Fair enough!
I have just now added DANO and I(+)/I(-) to the files. I'll be very
interested to see what you can come up with! For the record, the phases
therein came from running mlphare with default parameters but exactly
the correct heavy-atom constellation (all the sulfur atoms in 3dko), and
then running dm with default parameters.
Yes, there are other ways to run mlphare and dm that give better phases,
but I was only able to determine those parameters by "cheating"
(comparing the resulting map to the right answer), so I don't think it
is "fair" to use those maps.
I have had a few questions about what is "cheating" and what is not
cheating. I don't have a problem with the use of sequence information
because that actually is something that you realistically would know
about your protein when you sat down to collect data. The sequence of
this molecule is that of 3dko:
http://bl831.als.lbl.gov/~jamesh/challenge/seq.pir
I also don't have a problem with anyone actually using an automation
program to _help_ them solve the "impossible" dataset as long as they
can explain what they did. Simply putting the above sequence into
BALBES would, of course, be cheating! I suppose one could try
eliminating 3dko and its "homologs" from the BALBES search, but that, in
and of itself, is perhaps relevant to the challenge: "what is the most
distance homolog that still allows you to solve the structure?". That,
I think, is also a stringent test of model-building skill.
I have already tried ARP/wARP, phenix.autobuild and
buccaneer/refmac. With default parameters, all of these programs fail
on both the "possible" and "impossible" datasets. It was only with some
substantial tweaking that I found a way to get phenix.autobuild to crack
the "possible" dataset (using 20 models in parallel). I have not yet
found a way to get any automation program to build its way out of the
"impossible" dataset. Personally, I think that the breakthrough might be
something like what Tom Terwilliger mentioned. If you build a good
enough starting set of atoms, then I think an automation program should
be able to take you the rest of the way. If that is the case, then it
means people like Tom who develop such programs for us might be able to
use that insight to improve the software, and that is something that
will benefit all of us.
Or, it is entirely possible that I'm just not running the current
software properly! If so, I'd love it if someone who knows better (such
as their developers) could enlighten me.
-James Holton
MAD Scientist
On 1/12/2013 3:07 AM, Pavol Skubak wrote:
Dear James,
your challenge in its current form ignores an important source
of information for model building that is available for your
simulated data - namely, it does not allow to use anomalous
phase information in the model building. In difficult cases on
the edge of success such as this one, this typically makes
the difference between building and not building.
If you can make the F+/F- and Se substructure available, we
can test whether this is the case indeed. However, while I
expect this would push the challenge further significantly,
most likely you would be able to decrease the Se incorporation
of your simulated data further to such levels that the anomalous
signal is again no longer sufficient to build the structure. And
most likely, there would again exist an edge where a small
decrease in the Se incorporation would lead from a model built
to no model built.
Best regards,
--
Pavol Skubak
Biophysical Structural Chemistry
Gorleaus Laboratories
Einsteinweg 55
Leiden University
LEIDEN 2333CC
the Netherlands
tel: 0031715274414 <tel:0031715274414>
web: http://bsc.lic.leidenuniv.nl/people/skubak-0