Hi Spyros,

> Is there any statistical
> methodology that would be appropriate and take this into account?

It's been a while since I've had to consider such distributions.  Many
folks like the following text for sequence-related problems:
  Biological sequence analysis: probabilistic models of proteins and
nucleic acids
  By Richard Durbin - Cambridge University Press (1998) - Paperback -
356 pages - ISBN 0521629713

Give it a shot & good luck!

Cheers,

-- Jason

On Tue, Jul 12, 2011 at 7:38 PM, Spyros Charonis <s.charo...@gmail.com> wrote:
> Dear PyMOLers,
> I should say that this isn't a PyMOL-specific question, for that I apologize
> but I imagine several users on this mailing list will have experience
> in sequence/structure analysis, that is the reason for posting it. I am
> using a database that stores motifs sequences describing several proteins.
> I use PyMOL to visualize how well these motifs coincide with functional
> residues (ligand-binding, protein coupling, oligomerization, etc)
> What types of statistical distributions would be appropriate for analyzing
> data from such investigations?
>  I can think of a simplistic case where the binomial distribution could be
> used to calculate the probability that a certain residue will fall within a
> motif by chance alone where
>  Prob = average motif length /average overall sequence length.
> This assumes however that position of ligand-binding residues are
> independent of one another, whereas in fact it is likely
> that functional residues form in clusters? Is there any statistical
> methodology that would be appropriate and take this into account?
> Again, sorry for the slightly off-topic question and many thanks.
> Spyros

-- 
Jason Vertrees, PhD
PyMOL Product Manager
Schrodinger, LLC

(e) jason.vertr...@schrodinger.com
(o) +1 (603) 374-7120

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