AMBER Archive (2009)

Subject: Re: Re: [AMBER] ptraj usage for clustering of protein ensembles manual or tutorial

From: Carlos Simmerling (carlos.simmerling_at_gmail.com)
Date: Tue Dec 08 2009 - 08:59:48 CST

 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 <drugdesign_at_yandex.ru>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" <tec3_at_utah.edu>:
>
> >
> > > 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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>
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