ADL: question about Vina flexible residues and pockets
dbromley at uw.edu
Sat Jan 25 17:08:55 PST 2014
This is a good discussion, everyone, thank you.
To follow up on this, what are the in silico techniques and tools aside from bulk high throughout screening that other people here have used to narrow down ligand leads for a novel pocket with no known binders?
> On Jan 23, 2014, at 1:39 PM, Oleg Trott <trott at scripps.edu> wrote:
> I think Vina-based VS is useful even without any information about
> binders. Here's why:
> The enrichment varies significantly depending on the target, but the
> average early enrichment for Vina on the DUD dataset is about 9-fold,
> based on the Watowich group's results, if I remember correctly. That's
> better than random guessing, on average, which would have been 1.
> Secondly, the DUD dataset is already enriched, with 1/40 actives, so
> enriching it further is probably harder than in a random dataset where
> you might have 1/1000 actives. Additionally the decoys in DUD are
> chosen to be particularly challenging (similar to actives,
> physically). So in a real-world application, the average enrichment
> might be higher. (On the other hand, the receptors in DUD are in an
> induced-fit conformation, and that probably helps)
> Thirdly, let's suppose that the average expected enrichment is 1
> (instead of 9, or whatever the real number is), so that there is no
> enrichment, averaged over all receptors. However, VS will still be
> useful, on average, if you employ a "staged" approach (patent
> pending): Test a small number of predicted actives to determine if VS
> works for you receptor. If it seems like it doesn't, cut your losses,
> if it does, then you can continue to use it to find more/better hits.
>> On Thu, Jan 23, 2014 at 11:54 AM, Dennis N Bromley <dbromley at uw.edu> wrote:
>> Julio, thank you, this is very helpful, particularly the ideas about
>> negative controls.
>> So, I have a more general question then: given a novel target pocket with
>> no known positive controls, are people saying that in silico high
>> throughput virtual screening is of no use in narrowing down a set of
>> ligands for eventual wetlab testing?
>> On Thu, Jan 23, 2014 at 10:57 AM, Julio Dominguez <acheron24 at hotmail.com>wrote:
>>> Just adding my two cents here. I agree completely with Oleg for the
>>> positive controls: you need a known ligand. Worst case scenario you need
>>> data for mutagenesis and binding assays. Now, for the negative control I
>>> have been using molecules that should bind proteins albeit in a non
>>> specific fashion: ammonia, phosphate, sulfate, bencene, phenol, aniline. I
>>> my experience using known lipid binding proteins, enzymes and even DNA this
>>> controls work rather well always being on the side of the least likely to
>>> I hope this helps.
>>> Best regards
>>>> Message: 2
>>>> Date: Wed, 22 Jan 2014 13:18:07 -0800
>>>> From: Oleg Trott <trott at scripps.edu>
>>>> Subject: Re: ADL: question about Vina flexible residues and pockets
>>>> To: autodock at scripps.edu
>>> CAHgi0UhVEOBQ1jYM+6FP80OH3JQZuii6UcmXzUkkXuhQHafs+Q at mail.gmail.com>
>>>> Content-Type: text/plain; charset=ISO-8859-1
>>>> If you have known binders for your pocket, as I thought you did, a
>>>> retrospective VS is what one would do to evaluate a VS protocol.
>>>> If you don't, perhaps you can extrapolate the results from homologous
>>>> proteins with known binders (but I guess it comes down to user's
>>>> intuition and experience, as I don't know of any quantitative models
>>>> for this)
>>>> If, in the worst case, you have no known binders for your pocket, and
>>>> no relevant information from homologous proteins, I don't think there
>>>> is much you can do to predict if a VS will turn out to be useful for
>>>> your system: perhaps it will work brilliantly, or perhaps it will have
>>>> < 1 enrichment (worse than random guessing).
>>>> The conventional wisdom is that the "baseline" for good binders would
>>>> vary depending on the pocket.
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> Oleg Trott, Ph.D. (Columbia University)
> Staff Scientist in the Olson Lab
> The Scripps Research Institute
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