I actually decided to start a new thread based on this reply. I'm considering the best algorithm to prepare libraries for virtual screening using Amber. There is a number of software tools like Corina, Concord, Omega2 etc to prepare datasets of comformations for ligands for viruatl screening.
But in this case the influence of solvent is not evaluated. Generally maybe it's possible to use short 20-30 ps runs in implicit solvent or with explicit solvent to prepare the most probable conformations for such a small molecules followed by clustering and selection of the most probable conformations datasets. Regardless the computational costs how reasonable is preparation of such small molecule databases? (diversity sets for example).
Best regards,
Andrew
08.12.09, 09:59, "Carlos Simmerling" <carlos.simmerling.gmail.com>:
> ptraj can be used for more than just folding trajectories. it should work
> fine for clustering your small molecules.
>
> On Tue, Dec 8, 2009 at 9:48 AM, Andrew Voronkov wrote:
>
> > Btw, ptraj can be used only for analysis of protein trajectories?
> >
> > One of the problems in computer assisted drug esign is proper conformations
> > prediction for the small molecule compounds.
> > I wonder if it s possible to cluster small molecule conformations resulted
> > from let s say 10-50 ps trajectory and us it for database preparation.
> > For example 100 000 DB preparation then should take only 1-5 microseconds
> > and for each molecule will request much lower times than for the protein.
> > But in this case the water influence on small molecule conformation will be
> > included which may be futher used for virtual screening purposes for example
> >
> > Best regards,
> > Andrew
> >
> > 06.12.09, 10:23, "Thomas Cheatham" :
> >
> > >
> > > > Yes, larger motions have some influence as far as the binding site is
> > in
> > > > the interface of homodimer and there are some little oscillations in
> > > > monomers distance. But I actually also want to optimize somehow the
> > > > number of clusters.
> > >
> > > You can read our paper on clustering MD trajectories in JCTC (Shao et
> > al.)
> > > and from this you will see that the metrics are not always so clear. In
> > > practice, my general path is to look at a 2D RMSd plot and count the
> > > number of big squares on the diagonal. Alternatively, I look at the
> > > cluster counts (i.e. how many frames in each cluster) which is reported
> > at
> > > the bottom on the *.txt file... i.e. below it looks like there are 4
> > > major clusters (1, 2, 5, 6) based on the count; the graphical time
> > course
> > > suggests that the clusters are not revisited much...
> > >
> > > #Clustering: divide 258425 points into 7 clusters
> > > #Cluster 0: has 6647 points, occurence 0.026
> > > #Cluster 1: has 133732 points, occurence 0.517
> > > #Cluster 2: has 58864 points, occurence 0.228
> > > #Cluster 3: has 243 points, occurence 0.001
> > > #Cluster 4: has 1110 points, occurence 0.004
> > > #Cluster 5: has 32013 points, occurence 0.124
> > > #Cluster 6: has 25816 points, occurence 0.100
> > > #Cluster 0 . ... .. . . . 54.1 . ....
> > > #Cluster 1 9XX999X99XXX999999X9X9X98. . .....54.. .. ..
> > > #Cluster 2 . ...... . . .1999999799994...
> > > #Cluster 3 . ....... .
> > > #Cluster 4 .1.
> > > #Cluster 5 . .. .5781...29997
> > > #Cluster 6 ..89997 ..2
> > >
> > > -- tec3
> > >
> > >
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Received on Sat Dec 26 2009 - 04:00:03 PST
