A bit late to this thread.

1.
Juergen:   Jim was not actually adopting CC*, he was asking how to make practical use of it when faced with actual datasets fading into noise.  If I understand correctly from later responses, paired refinement is what K&D suggest should be best practice?

2.
I'm struck by how small the improvements in R/Rfree are in Diederichs & Karplus (ActaD 2013, http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3689524/);  the authors don't discuss it, but what's current thinking on how to estimate the expected variation in R/Rfree - does the Tickle formalism (1998) still apply for ML with very weak data?

I'm puzzled by Table 4 (and discussion):  do I read correctly that discarding negative unique reflections led to higher CCwork/CCfree?  Wasn't the point of the paper that massaging data always shows up in worse refinement stats?  Is this a corner case, and how would one know?

Cheers
phx











On 28/08/2013 01:48, Bosch, Juergen wrote:
[log in to unmask]" type="cite">
Hi Jim,

all data is good data - the more data you have the better (that's what they say anyhow)

Not everybody is adopting to the Karplus Diederich paper as quickly as you do. And not to be confused with the Diederichs and Karplus paper :-)
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3689524/
http://www.ncbi.nlm.nih.gov/pubmed/22628654

My models get better by including the data I had been omitting before, that's all that counts for me.

Jürgen

P.S. reminds me somehow of those guys collecting more and more data - PRISM greetings

On Aug 27, 2013, at 8:29 PM, Jim Pflugrath wrote:

I have to ask flamingly: So what about CC1/2 and CC*?  

Did we not replace an arbitrary resolution cut-off based on a value of Rmerge with an arbitrary resolution cut-off based on a value of Rmeas already?  And now we are going to replace that with an arbitrary resolution cut-off based on a value of CC* or is it CC1/2?

I am asked often:  What value of CC1/2 should I cut my resolution at?  What should I tell my students?  I've got a course coming up and I am sure they will ask me again.

Jim

From: CCP4 bulletin board [[log in to unmask]] on behalf of Arka Chakraborty [[log in to unmask]]
Sent: Tuesday, August 27, 2013 7:45 AM
To: [log in to unmask]
Subject: Re: [ccp4bb] Resolution, R factors and data quality

Hi all,
does this not again bring up the still prevailing adherence to R factors and not  a shift to correlation coefficients ( CC1/2 and CC*) ? (as Dr. Phil Evans has indicated).?
The way we look at data quality ( by "we" I mean the end users ) needs to be altered, I guess.

best,

Arka Chakraborty

On Tue, Aug 27, 2013 at 9:50 AM, Phil Evans <[log in to unmask]> wrote:
The question you should ask yourself is "why would omitting data improve my model?"

Phil

......................
Jürgen Bosch
Johns Hopkins University
Bloomberg School of Public Health
Department of Biochemistry & Molecular Biology
Johns Hopkins Malaria Research Institute
615 North Wolfe Street, W8708
Baltimore, MD 21205
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