One of the grand challenges is the protein structure prediction problem.
The problem consists of determining tertiary structure of a protein given
its primary sequence of amino acids. Experimental approaches, like X-ray
crystallography and NMR spectroscopy, are very time consuming. Computer
simulations that predict the protein fold are a promising alternative.
Computational techniques that aim to solve tertiary structure conformation
will not only tackle the folding of existing proteins but also the folding
of "engineered" proteins, including those designed for drug purposes. Two
kinds of methods have been identified to tackle the protein structure
prediction problem: knowledge-based methods that rely on the presence of
homologous proteins (in sequence or structure) in the databases, and
physics-based methods that emphasize more the physical principles.
Although the physics-based methods are extremely important to find
genuine new folds, they are also extremely expensive. Thus, knowledge-based
methods are a valid alternative when there is some homology. However,
because in most cases only fragments of the proteins are similar, the
results achieved by these methods may be limited.
This image shows a protein molecule crafted and visualized with the
ProteinShop software, developed at LBNL.