Dear AMBER community,
I have a couple of questions.
I have gone through the Abseher and Nilges paper for the reduced
eigenvector formulation for distance-covariance matrices but I could not
exactly understand how linear combination of the members of the distance
space vector members can account for the linear coordination of the
coordinates in 3D. I would like to use distance-space calculations
because my different alignments to fixed atomic positions (Ca's with
lowest RMSD's over time - also on the Abseher Nilges paper) produce
different results.
Second, what is the advantage of reducing the covariance matrix from
coordinate space to dihedral space? I tried to find appropriate
references to research that did that but could not come with any.
Third, I have read some papers that argued strongly against the validity
of PCA analysis. The principal concern was that the sampling time
usually used (the most I have seen was 3ns) was not enough to achieve
convergence (a nice paper was from Rueda et al. - they used 100ns
simulations like 30 proteins and argued against the reproducibility of
MD trajectories from only a slice of the timeframe). If you had any
personal experience with PCA, what is your opinion about this?
I know that this is not really AMBER related, but since I don't know any
discussion forums that specifically discuss MD in biomolecular
simulations and the validity of the methodology and since so many
experts are gathered under this mail group, I would most probably
satisfy my curiosity.
Thank you
Cihan Aydin
UMass Medical School
Worcester, MA
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Received on Mon Jul 06 2009 - 09:44:18 PDT
