Re: [ccp4bb] to model or not to model, that's not the question

[Miguel Ortiz Lombardia]

Le 17 nov. 09 à 12:40, Morten Kjeldgaard a écrit :

Tim Gruene wrote:

Yes, but models that can be validated against experimental data. The

defining characteristics of computational models is that they (A)


Cheers,
Morten

are 100% dependent on the algortihm, (B) can't be validated at all.

Sorry, they can be validated to some extend using biochemical data!

You are joking, right?

I would say that any prediction that can be derived from a model and

became too contradictorily for this model. Until then it was a good
model - better than no model at all, be it wrong or not.

confirmed is a validation of the model and the model remains valid until replaced by a better one. The sun was orbiting the earth until evidence

competitive technique to xray-crystallography.

Whoa there. Let's move back a few steps. This discussion started because someone said that there are "rumblings" that modelbuilding would soon be a

That was perhaps a joke. In any case, it can't be seriously considered. I think the potentially useful discussion is about what information can we gain from each other.

I objected with the fact that computational models cannot be validated, a claim which was countered with "they can be validated using biochemical data".

I think that is really funny. So, you want to compute a model of a

co-factors and your metal-binding sites are correct.

macromolecule from first principles, and then spend the next 10 years in the biochemistry lab validating it? Because that is what it will take until you can convince anyone that the positions of your loops, your rotamers, your

You don't need 10 years to test a clear prediction. From whatever kind of model. If you need 10 years the prediction is probably useless in its present form. I presume that is precisely your point. My point is that such models may produce clear, testable predictions. From the way we are discussing it would seem that this is a matter of opinion. The fact is that some models are validated, even structurally, see: Qian, B., Raman, S., Das, R., Bradley, P., McCoy, A. J., Read, R. J., and Baker, D. (2007) High-resolution structure prediction and the crystallographic phase problem. Nature, 450: 259–264.

I thought this list was for crystallographers, but apparently people no

longer understand what "validation" means in structural science.

I agree with this, but not in the way you think. Crystallographers may be/need to be inerested by other fields related to structural biology. Even if they don't agree with the way other fields research is carried out.

 (...) OTOH, a poor molecular model may cause
unlimited waste of time and money by other scientists.

Best,


-- Miguel

Architecture et Fonction des Macromolécules Biologiques (UMR6098)
CNRS, Universités d'Aix-Marseille I & II
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Tel: +33(0) 491 82 55 93
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That's what happened for example with the crystal structures of the Emr multidrug transporters. Biochemists found a hard time to get funding for research that was in contradiction with those, later retracted crystallographic models. I hope that they don't conclude from that episode that trusting crystallographic models is useless or even dangerous to them.

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