Abstract title: Using hidden Markov models to recognize protein folds
Abtract author: Kevin Karplus
Presenting author: Kevin Karplus
Institution: University of California, Santa Cruz
e-mail: karplus@soe.ucsc.edu
Invited? Yes (by Andrew Torda)
Symposium title: Protein Structure Prediction
Student poster? No

I don't have access at the moment to RTF or Word, so here is plain text:

	Using hidden Markov models to recognize protein folds
			    Kevin Karplus


The protein-folding problem, in its purest form, is too difficult for
us to solve in the next several years, but we need structure
predictions now.  One solution is to try to recognize the similarity
between a target protein and one of the thousands of proteins 
whose structure has been determined experimentally.  For very similar
proteins, the relationships are easy to find and good models can be
built by copying the backbone (and even some sidechains) for the
homologous protein of known structure.  For less similar proteins (in
the ``twilight zone''), the fold-recognition problem is more
challenging, but it is often possible to find useful similarities.

Using evolutionary information helps enormously in recognizing remote
relationships, and one convenient way to summarize a family of
homologs is with a hidden Markov model (HMM).  Homologs can be found
and an HMM built by an iterated search, starting from a single target
sequence.  The resulting target HMM can be used to score the sequences
of all proteins of known structure.

Similarly, homologs can be found and HMMs built for template proteins
of known structure and used to score the target sequence.  Combining
both target-model and template-library results reduces the false
positive rate.

Some further improvements can be made by predicting local structural
properties of the target sequence (such as secondary structure or
solvent accessibility) and adding these predictions to the HMM used to
score the template sequences.

Fold-recognition techniques based on these HMMs have performed quite
well in blind prediction experiments (CASP2, CASP3, and CASP4) and are
doing better than threading techniques based on pairwise potentials.

New techniques for predicting new folds---especially David Baker's
program Rosetta---are beginning to mature and may
replace (or be merged with) fold-recognition methods in the next few
years.