From: Hurt, Darrell (NIH/NIAID) [E] (
Date: Fri Sep 16 2011 - 10:17:54 CDT

Hi Santhosh,

This may break protocol, but I find for simple clustering, it might be easier to use the MD Movie tool in UCSF Chimera. They have an easy-to-use clustering algorithm based on that "just works".

Also, 16,000 frames for an 8 ns simulation seems like a little bit of overkill, unless you know that you need that kind of temporal resolution to build representative structures. Perhaps you could make things a little easier by pruning down the frames from every 500 fs to perhaps every 10 ps or even 100 ps to get a more manageable number of frames (800 or even 80 frames).


On Sep 16, 2011, at 9:13 AM, Vijay Vammi wrote:


I have a 8ns simulation with about 16000 frames. I want to cluster these frames to get representative structures.

I want to use the QR factorization method as described in "Evolutionary Profiles Derived from the QR Factorization of Multiple Structural Alignments Gives an Economy of Information" by Patrick O’Donoghue and Zaida Luthey-Schulten. I see that this has been part of multiseq plugin in VMD. I have couple of questions regarding its use :

1). Since we are dealing with rather large number of PDB's, is it advisable that I run this via command line instead of GUI. I tried using the GUI but I see that multiseq did not load all the frames but only 2000 of them.

2). In the actual implementation the problem becomes multi-dimmensional because of gaps, since I dont have gaps in the structure alignment the problem should be reducible to traditional QR factorization. (noofCAAtoms*3 X numframes would be the size of matrix I am trying to decompose.). Would be it be much faster and better if I use numpy or Matlab instead of VMD to get this done? Please correct me if I am wrong here.

Any help or advice on this is appreciated.


Darrell Hurt, Ph.D.
Section Head, Computational Biology
Bioinformatics and Computational Biosciences Branch (BCBB)

31 Center Drive, Room 3B62G, MSC 2135
Bethesda, MD 20892-2135
Office 301-402-0095
Mobile 301-758-3559 (Within NIH) (Public)

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