From: samodena@csemail.cropsci.ncsu.edu (S. A. Modena@)
Newsgroups: bionet.info-theory
Subject: Weak signal detection in the N-cap of protein helices
Summary: Small dataset corrections on relative information content calc's
Keywords: N-cap proteins helices info theory statictics small data sets
Date: 9 Apr 92 13:31:26 GMT
Article-I.D.: ncsu.1992Apr9.133126.26623
Organization: Dept. of Crop Science, NCSU, Raleigh, NC.
Originator: samodena@csemail.cropsci.ncsu.edu

Attended a seminar yesterday:

	Alignment Methods for Characterizing Structural Sites
	in Biological Sequences

	MacDonald Morris (Post-doc with Joe Cohen )
	Dept. of Pharmaceutical Chemistry
	University of Calif. at San Francisco
	
His 	ABSTRACT
"The problem of extracting information from a large set of short
biological sequences with a common structural or functional role is
investigated.  Our approach involves two steps: The first is to align
a large group of short sequences which generally have only weak
sequence similarity.  The second is to use the information in this
alignment to identify similar sites in novel sequences.  Techniques
from information theory and small data-set statistics are applied
to this problem and then tested using computer-simulated data. The
methods developed are shown to significantly enhance predictive ability
as compared to currently available methods.  Tests with real data (the
ends of alpha helices in proteins) show similar improvements."

I remember: 
One slide cited Schneider et al. 1986...and Morris called the calculation
"relative information content."   After a long, elementary intro it seems
that that group is trying to find some underlying sequence similarities in
N-cap areas of helices (where the non-helix:helix tranition occurs) by
looking at short sequence examples drawn from a diverse set of proteins
for which x-ray crystallographic structural determination has been done.
In a run of perhaps 10-12 residues, the author has improved the information
content sumation calculation from 2 bits to 4 bits by a combination of
mathematical "small dataset" corrections and  iterative realignment of the
example set to maximize the "information content" result. All calculations
are done with the assumption of independence for each residue, no deletions 
or substitions allowed, expert guessing of how to align the group at the
start, and no consideration of whether the helix start is tucked against a
hydrophobic domain or is exposed to solvent.

I gather that a main approach of Cohen's lab is to go one step beyond the
traditional consensus sequence to one of semi-correlation of the type
[DVLP]...[AJD], when likely alternates for a position are in brackets and
intervening "not important" residues are each represented by a dot.  Morris
is doing a straight Schneider-style calculation from a probability matrix
with small dataset correction strategies being applied to adjusting the
values in the matrix....one purpose being to avoid any "zero values" by
substituting the general background frequency of a non-appearing amino acid
for it's zero value in the matrix.

Disclaimer: my misunderstanding of this work (as related above) is probably
due to my novice status and the poor quality of the seminar presentation.
I've noticed that mathematicians generally are excellent at poorly presenting
their concise concepts in precise terms.  Of course, some one ought to look
up the email address of the Cohen lab at UCSF and give them opportunity
to join us and give me my come-uppance for this posting.

Have a nice day, Folks! :^)
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