[EMAIL PROTECTED] wrote:
I don't have a problem, per se, but would like to discuss the problems
that may, or may not, arise when mixing force fields.
It is clear to me why one would not want to calculate the free energy of
binding for two proteins, one using the amber ff and the other using the
opls ff; also it is clear that there would be problems simulating a box
of water half of which is tip3p and half of which is spc. The common
thing to these examples is that such simulations would apply dissimilar
parameter sets for similar functional groups and therefore any results
could be subject to significant biases, the source of which will not be
obvious to the user.
However, If one was simulating the binding of a protein to DNA, or a
protein embedded in a lipid bilayer, the functional groups are no longer
shared by different types of macromolecules. Since I work on membrane
proteins, let me take the case of an oplsaa protein in a Berger lipid
bilayer. Not only are these ff's differently generated, but one is
all-atom and one is united-atom. The important difference in this case
is that there are few functional groups of the lipids that resemble
those of the protein e.g. the NH3 of a lipid head-group choline and a
lysine of the protein. Generally though, the functional groups are
entirely different between these macromolecules. I believe that this is
also the case for protein-DNA simulations. Therefore, what biases can
possibly occur by the combination of different ff's in this case that
could not also occur by combinations that exclusively use a single ff?
Force field parameters are the result of some kind of global
optimization procedure. As such it is well-known that you should not
expect a strong correlation between a bond stretching parameter and any
real measure of bond strength. This is because that real interaction is
being modeled a) approximately, and b) through model interactions not
necessarily localised to the two bonded atoms.
One would not expect to reach the same near-global minimum after
optimizing over protein parameters for two given sets of water
parameters. Trivially, the water-protein Coulomb interactions will have
to be different. Thus, the intra-protein Coulomb interactions will have
to be different. This may directly affect some bonded interactions,
depending on your exclusion treatment. Finally, then can be all manner
of indirect effects that might depend on which local minimum your
optimization ended up on. The same goes for any other sets of
constrained and free variables you might use in a parameterization
process, and IMO makes for a clear presumption of numerical suicide from
mixing force fields, possibly except in some fortuitous and well-tested
cases. Hopefully this oplsaa-Berger mix is such a case, but I don't know
anything about it.
I take the extreme example and ask: what special relevance do the opls
ion parameters have to the opls protein parameters? It seems to me that,
although they "derive them in a manner consistent with how the rest of
the force field was originally derived"
(http://wiki.gromacs.org/index.php/Parameterization), in this extreme
case I believe that this is an entirely abstract concept of no
particular value. In other words, how can Na+ possibly be generated
consistently/inconsistently with an amino acid that contains no Na?
In part, the general advice you cite is sound for cases where one is not
going to do a fully rigorous test of the performance of the parameters -
e.g. the antechamber or PRODRG approach. Using a similar methodology
gives one some basis for optimism. Using a different one *and not
testing* is random and asks for trouble. Using a different one *and
testing* for performance on observables relevant to the study you wish
to perform using those parameters seems quite reasonable to me. The only
value in an extended MM force field is its ability to model a physical
system featuring the elements of that extension. If you can demonstrate
it does that well enough, then the method by which you extended it seems
irrelevant.
Also, it could be true that achieving success in such a test has been
experienced to be difficult unless one has followed a similar methodology.
To clearly state my current point of view in the absence of a shred of
data, I suggest the following: "One should not combine parameters that
are derived inconsistently of one another except in cases where such
combination can be made without introducing multiple parametric
definitions of a given functional group."
I would disagree strongly for the above kinds of reasons.
If you believe that, it would
therefore be acceptable to combine the following in any way: i) protein,
ii) water, iii) ion, iv) DNA, v) lipid, vi) carbohydrate. The seventh
group: small molecules, is difficult to classify since one must take
into consideration the specific functional groups. For example, I would
suggest that ATP and a protein should be fine if different ff's are
used, but that ATP and DNA should use a consistent ff when simulated in
conjunction.
As we ramp up our simulations for ever-increasing cpu power and for
gromacs 4, these questions are well beyond pedantic. It is one thing to
develop parameters for a small molecule consistently with the the
methodology used for the protein/DNA ff. However, simulations of more
than one different type of macromolecule (e.g. protein-DNA simulations)
would greatly benefit, it seems, from the ability to use the DNA
parameters that lead to the most accurate sampling of DNA phase space
and the protein parameters that lead to the most accurate sampling of
protein phase space. It is my conjecture that such combinations would
not only be appropriate, but that they would be optimal.
These phase spaces are not independent. A solute phase space is sampled
differently in different solvent models. There is no reason to suppose
that the combinations you suggest would even be close to effective,
never mind optimal.
Disclaimer: If you are considering combining differently-generated
force-fields, please do not take this post as encouragement. The
standard logic never to combine force-fields is still recommended. I
only wanted to have some discussion on this topic.
Thanks for all comments, especially those that are in disagreement with
my proposition.
You're welcome :-)
Mark
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