Subject: AUTODOCK 3.0

From: Simon Cross <[email-masked]>
Date: Fri, 2 Jun 2000 14:49:21 +0100 (BST)

Has anyone used soft potentials with Autodock 3.0, or used other methods
to overcome problems with 'gating' in a fixed receptor? I am specifically
working on antibodies and find that Autodock can only reproduce the ligand
location in the crystal structure if it is initiated within the binding
pocket - if the ligand is placed outside the binding pocket it docks in
the correct location but only 'half in' the pocket, presumably due to the
lack of flexibility of the antibody in this system. I am trying to get
around this by using soft potentials but am not entirely sure how to do
this. Any comments much appreciated.   

-----------------------------------------

Simon Cross
School of Chemistry
University of Nottingham
tel. 0115 9514193
Email: [email-masked]

---

From: "Garrett M. Morris" <[email-masked]>
Date: Fri, 2 Jun 2000 19:02:00 -0700

Dear Simon,

On Fri, 2 Jun 2000, Simon Cross wrote:

> Has anyone used soft potentials with Autodock 3.0, or used other

AutoGrid 3.1 has not yet been released, but it has a new form of
pairwise potential smoothing, which ramps the potential to a finite
value at separation zero.  This finite value can be varied by the
user, and so could help dramatically in the early stages of the
docking.

> methods to overcome problems with 'gating' in a fixed receptor? I
> am specifically working on antibodies and find that Autodock can
> only reproduce the ligand location in the crystal structure if it
> is initiated within the binding pocket - if the ligand is placed

Are you using SA (simulated annealing) to do this?  You can only start
the ligand at a pre-defined position using SA and LS (Solis and Wets).
The GA and LGA start from a population of random initial genomes.

> outside the binding pocket it docks in the correct location but
> only 'half in' the pocket, presumably due to the lack of
> flexibility of the antibody in this system.

The LGA is the most efficient search method: SA seems to be the least
efficient.  You should get better results with the LGA than with the
SA, for the same number of energy evaluations. We found this even
though both were started from random initial states or populations of
states.

If you are using LGA, with a population size of 50, an elitism number
of 1, and a crossover rate of 0.80, you might want to try increasing
elitism to 5.  Then there is a 20% chance that one of the top five do
not experience crossover, and thus will survive into the next
generation.

Another pointer: increase the maxium number of evaluations, by a
factor of 10.  This might help to improve the search results.

> I am trying to get around this by using soft potentials but am not
> entirely sure how to do this. Any comments much appreciated.
> 
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I hope this helps,

Garrett

___
Dr Garrett M. Morris, MA, DPhil
The Scripps Research Institute,       tel: (858) 784-2292
Dept. Molecular Biology,  MB-5,       fax: (858) 784-2860
10550  North Torrey Pines Road,       email: [email-masked]
La Jolla,  CA 92037-1000,  USA.       www.scripps.edu/pub/olson-web/gmm