Regarding Dayhoff...this model is assuming that substitutions
occur independently across sites according to a *reversible* Markov
chain. (By reversible, I mean that the probability that an i changes
to a j in time t is the same as the probability that a j changing to
an i in time t. This reversibility is very much suspect. (For
instance, it is hard to imagine what the reversibility assumption
means physically.)
It is my opinion, though I have only studied this problem
briefly, that there is more careful work being done on this problem
with DNA sequences (see the work by Gojobori, et al.), a key reason
being that with DNA we are estimating only a 4x4 transition matrix,
rather than a 20x20 one. And yet the estimators given by Gojobori's
method, I believe, are *inconsistent*, in that even with a infinitely
large amount of data, the estimates can be off appreciably.
karl
--------------------------------------------------------------------
Karl W. Broman University of California
Department of Statistics
(510) 658-2544 367 Evans Hall # 3860
(510) 642-7892 FAX Berkeley, CA 94720-3860
kbroman at stat.berkeley.edu USA
--------------------------------------------------------------------
In article <1995May26.165546.38556 at ac.dal.ca> you write:
>I'm interested in improving empirically-based amino acid
>substitution model for use in phylogenetic analysis.
>>One method which may improve upon existing models (like
>the Dayhoff models which programs like PROTDIST and
>PROTML use) is to determine whether the propensity
>of an amino acid (say X) to change to another amino acid
>(Y) depends upon the structural context in which it is
>found.
[lots of stuff deleted]
...