Re: [ccp4bb] mosaicity and SAD
Dear Supratim, In my experience, as long as you do not run into an overlap problem, a large mosaicity is not a problem. If the statistics look good, you can safely use the data. However, if the completeness of the data is much lower than what was predicted, many spots may have been thrown out because of overlaps and you may want to correct this. There has been a recent thread about this in the CCP4BB. Also, for large mosacities, you may want to consider a processing program with 3D profile fitting like XDS. Best, Herman From: CCP4 bulletin board [mailto:CCP4BB@JISCMAIL.AC.UK] On Behalf Of supratim dey Sent: Friday, August 31, 2012 4:56 AM To: CCP4BB@JISCMAIL.AC.UK Subject: [ccp4bb] mosaicity and SAD Hi friends what is the maximum mosaicity value to work with. When i am doing Heavy metal replacement my mosaicity value is around 1 - 1.2 after processing through HKL. I don't know is it acceptable data. I have used mercuric chloride for my protein and it has shown good results .However in case of potassium hexachloroplatinate it is not absorbing at all .Can you please suggest what other heavy metals can i use.
Re: [ccp4bb] Jelly body refinement?
Hi Gunnar,
I generally agree with your comments. However, I'd like to clarify a couple of
points:
For gamma=1 the DEN potential can follow anywhere, the entire conformational
space is accessible and dij(t+1) depends only on Dij(t) and dij(t).
...
But, again, the starting (or reference)
model is completely forgotten and never used after the first iteration.
Certainly, the entire conformational space is accessible. However, I'm not so
sure about the starting model being completely forgotten and never used after
the first iteration. Here are my thoughts: since the DEN update formula is
recursive, the equilibrium distance can also be written in terms of the Dij
alone (still assuming gamma=1):
dij(t+1) = Dij(0)*(1-kappa)^(t+1) + kappa*sum_n=0^t{Dij(t+1-n)*(1-kappa)^n}
This means that the equilibrium distance is indeed dependent on the initial
distance Dij(0) for all times t. For values of kappa in (0,1), this dependency
will diminish with time t, but will always exist. In fact, the equilibrium
distance dij(t) is dependent on the whole history of the distance throughout
the procedure, i.e. Dij(n) for n=0…t. Of course, the degree of influence of the
historical information is controlled by kappa. Values of kappa~=0 would mean
that the initial distance has very high weight (equilibrium distance dij(t) =
Dij(0) in the limit kappa=0), and kappa~=1 would mean that the most recent
distances have very high weight (equilibrium distance dij(t) = Dij(t) in the
limit kappa=1, as you have already stated). Intermediate values of kappa will
give various non-zero weights to the historical values of Dij.
This also means that the position of the minima of the target function
are not changed by the DEN (gamma=1) restraints.
I would have thought that changing the value and gradient of the target
function had the potential to alter the minima?
It is therefore usually useful to run a final minimization without
restraints to test whether the refinement reached a stable minimum of the
target function.
I agree. In the context of REFMAC5, my current favourite strategy at low
resolution is to first use external restraints in order to aid the structure to
adopt a more sensible conformation, but then subsequently release the external
restraints and replace them with jelly-body restraints towards the final
refinement stages.
From the user perspective, I think the main difference is that DEN is
designed
to be used in simulated annealing MD refinement, whereas jelly-body is
designed
to be used in minimization (and cannot be used for MD refinement as there are
no second derivatives).
I agree. Since the second derivative is utilised in ML refinement, it is
possible to design a regulariser that has the desirable properties X=0 and X'=0
(e.g. jelly-body refinement) in the absence of any externally-derived prior
information. Since this is not possible in simulated annealing MD refinement,
the analogous solution will undoubtedly have to alter X and/or X'. Either way,
all of these 'tricks' are just designed to aid robustness and combat
overfitting! Certainly, both approaches can give positive results when refining
at low resolution.
Cheers
Rob
On 30 Aug 2012, at 19:43, Gunnar Schroeder wrote:
Hi Rob,
thank you, your comments helped a lot.
From the Refmac5 paper I did not get the fact that d is set to d_current
after each step. In that case you are right, jelly-body corresponds rather to
DEN with gamma=1 than to gamma=0.
And of course, a very important difference is, as you said, the fact that
jelly-body is applied only to the second derivative.
However, I would like to clarify this one point you made:
For gamma=1 the DEN potential can follow anywhere, the entire conformational
space is accessible and dij(t+1) depends only on Dij(t) and dij(t).
The update formula is (again, for gamma=1):
dij(t+1) = (1-kappa)*dij(t) + kappa * Dij(t+1)
Dij(t) : distance between atom i and j and time t.
dij_ref : distance between atom i and j in the reference structure.
dij(t) : equilibrium distance of restraint between atom i and j at time t.
The parameter kappa just defines how quickly dij(t) changes,
i.e. kappa=1 sets dij(t+1)= Dij(t+1) at each time step.
The parameter kappa is usually set to 0.1, which means the restraints
slowly follow the atomic coordinates. But, again, the starting (or
reference)
model is completely forgotten and never used after the first iteration.
This also means that the position of the minima of the target function
are not changed by the DEN (gamma=1) restraints. It could just take longer
to get there as the restraints need to be dragged along.
For gamma1, the situation is different, there are additional forces toward
the reference (could be the starting) model, in which case dij(t+1)
additionally
depends on dij_ref. This also changes the position of the minima of the
target
function. It is
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Posted on behalf of Christophe Verlinde. Please reply to him at the e-mail address below. POST-DOC STRUCTURE-BASED DRUG DESIGN IN SEATTLE Join a multi-disciplinary team (protein crystallography, molecular modeling, synthetic chemistry, assay specialists, parasitologists) at the University of Washington in Seattle. We believe that computational methods in the hands of a scientist who is a creative thinker and an energetic collaborator can impact all aspects of drug discovery. Project goal: development of pre-clinical drug candidates to fight neglected parasitic diseases by exploiting in-house crystal structures of tRNA-synthetases. Your responsibilities will encompass large scale molecular docking, ligand optimization by design, chemo-informatics and occasionally mass-spectrometric follow-up of metabolic studies. Qualifications: - Ph.D. degree in computational chemistry, organic chemistry or a related field. - Experience in molecular docking, chemical library design, pharmacophore techniques. - Experience with linux-based computing and proficiency in at least one programming language. - Excellent oral and written communication skills in English. - Organizational skills. Interested individuals should send an e-mail to Christophe Verlinde (verli...@u.washington.edu) containing their CV, brief summary of previous research, and contact information for three references. For more info about the lab: http://www.bmsc.washington.edu/people/verlinde/research.html Initial appointment will be for 1 year an can be extended by another year upon satisfactory performance. Start date: asap. The University of Washington is an affirmative action, equal opportunity employer.
