What is the role of contingency/probability in protein folding ? If one
chain has many possible foldings, each affected with a given probability
amongst the population of molecules (like in evolution/population genetics),
is it possible to calculate all the variations ?
Tracy P. Hamilton <chem013 at uabdpo.dpo.uab.edu> a écrit dans le message :
816k1b$fc5$1 at SonOfMaze.dpo.uab.edu...
> I am just wandering in for the day, so don't expect any further response.
>> Pierre wrote in message <814hkq$965$1 at news2.isdnet.net>...
> >I agree with Tyson. Computer are more and more helpful. but we are still
> not
> >able to calculate reliably the 3D structure of a single protein. Thus,
> >saying that one day we will be able to calculate a vaccine with a
> >supercomputer is science fiction. May be we will. May be not. Notice also
> >that it is a reductionist/determinist dream that might be totally
> irrelevant
> >to biology. For example, will the same supercomputer be able to calculate
> >the future evolution of man ?
>> Progress is being made in predicting the 3-D structure of proteins. By
the
> end
> of next century that will not be a problem. However, there are more
> problems than
> that in fighting disease.
>> The least of which is finding out which protein is being affected by the
> disease.
>> Computers are used heavily now in what is called rational drug design:
>> Approach 1 - active site not known
>> 1) Take a known drug that works.
> 2) Build a model of the active site where the molecule
> determines its shape and charge distribution of your model site.
> For example, if the drug has a hydrophobic chain, there probably is
> a hydrophobic pocket at a corresponding position in the active site.
> 3) Make changes to the drug. From the effects, one can refine the model
> shape and charge distribution of the active site.
>> Approach 2 - active site structure known
>> 1) Make a theoretical combinatorial library of molecules that bind to
> the site.
> 2) Compute the binding of each.
> 3) Examine the shapes and charge distributions of the best, try different
> types of drugs that will give similar ones.
>> What can be done quite nicely (although there is room for better
> quantitative accuracy that will also come) is computing of binding
> to an active site.
>> However, just because a molecule has the best binding ability does not
> mean it is the best drug. It also has to *get to* the protein (or DNA, or
> RNA),
> not be metabolized, the treatment has to depend on strength of
> binding to the active site (best for competitive inhibition), and it
should
> not
> bind to other active sites as well, which can cause side effects.
>> It is a long, tedious process.
>> One way to try to reduce the side effects is to introduce rigidity into
the
> molecule. This would work best where a flexible molecule takes one
> shape in binding to the active site desired, and another in binding to the
> site which causes side effects. If you can make a drug with the rigid
> conformation close to the first, it will not bind as well to the second.
> Rigidity can be introduced primarily by using rings instead of chains.
>> Combinatorial chemistry relies more on automation rather than
> *calculations*,
> although computer data analysis and process control is required.
>> I have heard this approach called irrational drug design. There is no
clear
> advantage of irrational to rational. The major drug companies use both.
>> Experiments will always have to be done in the foreseeable future,
> although theory will help in both the screening of compounds AND
> in the study of the basic processes. Theory is also a major component
> of the latter.
>> Tracy P. Hamilton
>>>>
