• Welcome to Overclockers Forums! Join us to reply in threads, receive reduced ads, and to customize your site experience!

3D pictures to help folding prediction

Overclockers is supported by our readers. When you click a link to make a purchase, we may earn a commission. Learn More.


Super Kiwi
Aug 31, 2001
New Zealand
Folding at Home is about accurately predicting the shape of large protein molecules. Here's a report of another branch of applied physics seeking an alternative, or parallel, solution. 3D pictures!

The American Institute of Physics Bulletin of Physics News
Number 582 March 26, 2002 by Phillip F. Schewe, Ben Stein,
and James Riordon

STRUCTURE. Despite its name, atomic force microscopy (AFM)
does not produce atomic-resolution images of proteins or other
large molecules. When imaging macromolecules, a large region,
about 100 square nanometers, of the AFM tip makes contact with
the molecule. This region is comparable in size to the entire
molecule and makes the tip a blunt probe by atomic standards. To
extract more detailed information from AFM images of
macromolecules, one can directly subtract the effects of the tip but
the results are often inaccurate. At the March APS Meeting,
Steven Eppell and Brian Todd of Case Western Reserve University
(216-368-4067, [email protected]) presented a new technique for
obtaining submolecular information about proteins. Investigating
aggrecan, a cartilage protein important in osteoarthritis, the
researchers used a technique that combined AFM with genome
information and transmission electron microscopy data. All of the
data were integrated by using a sophisticated image processing
technique to provide a best guess at the 3D structure. The resulting
refined structure yielded new information on the molecule,
showing distinct locations of kinks as well as regions of
mechanical flexibility. The researchers hope to combine their
results with AFM-measured force fields around cartilage proteins
to link the biological and mechanical properties of cartilage with
its molecular structure. This approach has the potential to provide
information on molecular-scale mechanisms for arthritis and lead
to intelligent drug design and other interventions to prevent or
alleviate the disease.