SOURCE: Hospital & Nursing Home Week via NewsRx.com & NewsRx.net
October 28, 2004
Pg. 90
HEADLINE: RICE UNIVERSITY;
Study probes basic science related to AD, other diseases
Patients suffering from diseases as varied as type 2 diabetes,
Alzheimer,
Parkinson and dozens of lesser known maladies have one thing in common:
They
suffer from a large buildup of amyloids, tissue that's created when
millions
upon millions of misfolded proteins stick together and form a mass that
the body
can't get rid of on its own.
Doctors don't yet understand whether amyloids cause disease or result
from
it, but the fact that they are present in very different diseases
affecting
millions of people points to the need for improved understanding of the
basic
processes of protein folding, one of the most complicated and least
understood
of all biological phenomena.
Research appearing in the October 8, 2004, issue of the Journal of
Molecular
Biology described a new technique that may help scientists predict which
proteins are prone to misfold and at what point the folding process is
likely to
break down. The research could support efforts to find the causes for
diseases
involving amyloids, and it could prove useful for researchers studying
proteins
involved in even more prevalent diseases like cancer and heart disease.
"We know now that most diseases involve proteins going wrong in one
of two
ways," said lead researcher Cecilia Clementi, assistant professor of
chemistry
at Rice University. "In the first, proteins don't function correctly
because
they fold into the wrong shape. This is something we see in sickle-cell
anemia,
for instance, because of genetic flaws that cause the amino acid sequence
to be
incorrectly synthesized.
"The second way proteins go wrong is by not folding at all, which is
what we
find in diseases involving amyloids. In these situations, the misfolded
proteins
assemble together into macroscopic aggregates."
All the basic functions of life are carried out by proteins, and the
DNA in
each of our cells contains the blueprints for all the proteins we need.
Every
protein has a characteristic shape, and it folds itself into that shape
very
soon - generally in less than a second - after it is made. To carry out
their
tasks, proteins interact with one another, bind with some molecules,
cleave
others into pieces and fuse other molecules together.
Since the function of a protein is often based upon specific chemical
interactions - enzymes, for instance, are proteins that make or disrupt
chemical
bonds in other molecules - individual atoms of a protein must be aligned
just so
if they are to function properly. Consequently, there is a direct
relationship
between a protein's shape and its function.
The study of amyloids is complicated by the fact that the shape of
very few
proteins is known, the mechanics of protein folding are a mystery, and
protein
folding is incredibly complex; even the fastest supercomputer in the
world would
take decades to simulate all of the chemical interactions that take place
when a
single protein folds itself into its characteristic shape.
Despite this mystery and complexity, Clementi and colleagues believe
they
are creating a statistical method that will help scientists predict which
proteins are prone to misfold.
"In designing a computer model for protein folding, you can't take
everything into account because there are too many variables," said Vijay
Pande,
assistant professor chemistry and of structural biology at Stanford
University
and founder of the Folding@Home distributed computing project. "By
designing
simplified models that retain the essential physical and chemical
features of
protein dynamics, Clementi's team is making excellent progress in
quantitative
prediction - work that's highly complimentary to the detailed simulations
we're
creating through [log in to unmask]"
Basic thermodynamics dictates that the entire process of protein
folding can
be seen as the systematic progression of the unfolded protein into its
lowest
possible free energy state. To identify whether a protein is a candidate
for
misfolding, Clementi has devised a dynamic interplay between theory and
experiment - with each informing the other as an experiment is carried
out - to
construct a profile of the energy states a protein progresses through
while it
folds.
When this energy profile is plotted on a multidimensional graph,
Clementi
and her colleagues study the image like a mountain range, looking for
low-lying
valleys - reduced energy states where the protein is most likely to
become
sidetracked before it can finish folding into its proper shape.
Ultimately, Clementi hopes the technique will be refined and used to
catalog
the folding energies of proteins that have already been implicated in
specific
diseases. She believes the profiles could offer new clues to doctors and
drug
companies about which proteins are good candidates for drug therapy.
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