AMBER Archive (2008)
Subject: AMBER: silhouette width/coefficient after clustering by PTRAJ9.9
From: Dong Xu (d1xu_at_ucsd.edu)
Date: Thu May 29 2008 - 00:56:48 CDT
On Wed, May 28, 2008 at 9:07 PM, Jianyin Shao <jyshao2004_at_gmail.com> wrote:
> On 5/28/08, Dong Xu <dxu_at_mccammon.ucsd.edu> wrote:
>> Hi Jianyin,
>> Does the cluster command already include rms fit? I tried on a
>> trajectory without doing rms fit at first and the output
>> representative structure look they are rms aligned on the atom select
>> mask used in the cluster command.
> The clustering does not do an explicit rms fit. However, it makes sense to
> do rms fit before calculating the centroid (average structure). So all
> structures will align to the representative structure of the most populous
> cluster before outputing. For those agglomerative algorithms and k-mean
> algorithm, rms fit is not required. Other algorithms will need an explicit
> rms fit command before clustering.
>> If this is the case, how to disable rms fit in cluster command? Do we
>> have something similar to "nofit" in rms command?
> As I just said, rms fitting is required for calculating meaningful
> centroids. Is there a specific reason you don't want rms fitting?
Yes, I have an oligosacchride molecule which has 5 sugar units. I
first aligned all frames on the 1st sugar unit and then wanted to
performed clustering on the rmsd matrix of the remaining 4 sugar units
which are not aligned. So I did averagelinkage on the mask selection
of the remaining 4 sugar units. When I look at the output
representative structures, they are no long aligned on the 1st sugar
units, instead they seemed aligned on the rest of 4 sugar units
probably due to the mask selection. That's why I suspect the "cluster"
command actually does rms fit during the clustering, does it not?
The distances reported in the output *.txt file are much lower than
the rmsd I got from "rms" command with "nofit" on mask selection of
the remaining 4 sugar units, which means the frames were probably
aligned during the clustering.
I hope my questions make sense to you and your help is much appreciated!
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