Subject: Leapfrog Summary

From: Richard M Day <[email-masked]>
Date: Fri, 22 Jan 1999 07:45:27 +0000

Quite a while ago I sent the following message regarding the package
LEAPFROG de novo design

here is are some of the the replies:

ORIGINAL EMAIL !

Dear All,

I am trying to find people with experience in using LEAPFROG to suggest
lead affinity peptides.  

The big question - does this work at all ?? 

What is is the best way of sifting out the definate non-running peptides
before synthesis ? 

Is it worth seeing if one of the low energy conformations of the peptide is
close to the one required to provide interaction with the binding site -
i.e the one suggested by the lead finding program.

Your Comments would be greatfuly appreciated,

Thanks

Richard D.

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Hello Richard

I've been looking at Leapfrog's peptide method recently as a reasonable way
of coming up with readily synthesizable ligands for a crystal structure of
an unligated receptor.

>Dear All,
>
>I am trying to find people with experience in using LEAPFROG to suggest
>lead affinity peptides.
>
>The big question - does this work at all ??

(A) Does it work in producing reasonable peptides which seem to fit well in
to the site of interest?

Yes and no.  It has great potential, but in our hands we find the
ionization states of Asp, Arg etc unacceptable and further, it does not cap
the termini appropriately.  Coincidentally, Steve Bowless from Tripos was
out here yesterday and I talked to him about this and it seems he wants
this fixed, as it should be easy to do and will greatly improve results.

(B)Does the set of compounds it comes up with represent an improved set
over random peptides? (i.e. do more of the compounds that leapfrog thinks
will bind better actually bind better?)

Because of the capping problem, the interaction energy estimate doesn't
mean much.  Even when this is fixed, it has to be realized that the binding
estimate is extremely crude, so one would think about testing the top 100
peptides perhaps, rather than just the top 10.  Nevertheless, by virtue of
the fact basic steric and crude electrostatic matches are passed in this
set, I would think it would be a better quality set than a random set.  The
problem is here,  how many false negatives are we missing because crude
treatment of receptor flexibility: no protein x-ray structure for which
there is high res data is exactly the same at that with its complex
(Murcko, Rev. Comp. Chem.)?

One thing I want to look at is passing the Leapfrog set through a secondary
scoring function, involving HINT (G.Kellogg), or VALIDATE (Marshall), as
these really might be more meaningful.

One problem I have about the Tripos fitting functions is that relative to
Kuntz's DOCK, electrostatic interaction seem to be over-emphasized relative
to a good geometric fit.  I think of the fit as the primary thing, and
electrostatics second, but if you have Arg and Asp, this interaction
dominates in Leapfrog.  I spoke to Steve Bowless about this and he's going
to look into it.  If it works, the best workaround will be to scale down
the contribution of electrostatics by setting the dielectric to a high
distance value, but we don't know yet if Leapfrog looks at what one has set
for this.

>What is is the best way of sifting out the definate non-running peptides
>before synthesis ?
>
>Is it worth seeing if one of the low energy conformations of the peptide is
>close to the one required to provide interaction with the binding site -
>i.e the one suggested by the lead finding program.
>
>
A worthwhile thing to do here would be to write an SPL macro to prioritize
the hitset according to the number of "allowed" versus "disallowed" phi and
psi angles etc.

It is suprising, but as far as I know, there has been no study to look at
the success of this sort of approach.  It would be very interesting to
screen a few thousand tripeptides and screen a 100 Leapfrog tripeptides and
compare and publish the results.  Or, take a known peptide ligand (e.g.
RGD) and see if Leapfrog comes up with it if one has a reasonable starting
site geometry.

Jonathan Baell

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Dear Richard, 

My major is strcuture-based drug design and I know something about 
LEAPFROG. 

Your question is actually the most vital in the area of structure-based
drug design: can we predict the activity before we do the experiment? 
Unfortunately the answer at present is "we cannot always do that". 

LEAPFROG is often criticized for its naive scoring method. My suggestion
is: do not trust its ranking of the candidate molecules. But LEAPFROG is a
good tool to build molecules within the binding pocket of the target.  At
least the molecules generated have relatively good shapes complementary to
the binding pocket. You can choose yourself the molecules which seem to be
plausible and make necessary modifications on the structure to enhance the
interaction between the receptor and the ligand. Of course, it will 
depend on your own expertise. 

So you see why we always talk about "computer-aided drug design" rather 
than "computer-done drug design".

Hope these help.

Good luck.

Arthur

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