I can build from the impossible.mtz data in the following two steps:
1. getting the SE substructure from anomalous difference map
constructed from impossible.mtz
2. running "combined" model building using the substructure
from step 1 and starting from the impossible.mtz map
Only impossible.mtz and the sequence (which is probably not
really necessary) is used in this solution.
It is not a fully automatic solution - step 2 (model building
combined with density modif. and phasing via a recently
developed multivariate SAD function) was performed
automatically using CRANK (which calls Buccaneer, REFMAC
and Parrot), step 1 "manually" - using CCP4 tools (cfft and
peakmax).
Comparing to the deposited model, 96% of the mainchain is
(correctly) built and 92% is (correctly) docked and R factor
is 21% - clearly, the (relatively) weak anomalous signal is the
only limitation in this case. However, the model building
procedure did not struggle too much - I expect it would still
work if the Se incorporation is decreased somewhat further
(as long as the substructure can be obtained in some way).
Of course, this is not a "pure" solution in the sense that
I started from impossible.mtz rather than from scratch, ie
from the data only. Obtaining the substructure from scratch
might be more difficult.
Pavol
On Sat, Jan 12, 2013 at 10:50 PM, James Holton
<[log in to unmask]> wrote:
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
web:
http://bsc.lic.leidenuniv.nl/people/skubak-0
