The ability of proteins to fold into their native state is essential for cell function; misfolded proteins not only lose their function, but can also cause neurodegenerative diseases, including Alzheimer and Huntington. Study of protein folding can aid in preventing protein misfolding diseases and in designing proteins with novel functions. Although most cellular proteins fold on timescales of milliseconds to seconds, several small proteins have been designed and characterized experimentally to fold on a timescale of less than 20 µs. The project will employ Center-developed long-time MD simulation with NAMD to investigate: (1) the folding pathway of λ-repressor and (2) the effect of mutations on folding of the λ-repressor.
Spotlight: Everybody can Fold Proteins (Jan 2013)
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Every living cell relies on proteins to carry out its functional tasks;
every protein needs to assume a proper shape in order to be operational for these tasks. How a protein,
composed of a particular sequence of amino acids,
could find its way to a proper shape is a fundamental, yet mysterious biological process.
Researchers have sought to unravel atomistic details of protein folding processes through computer simulations,
but modeling such processes is computationally demanding.
It was only recently that some researchers have been able to observe in some case
how proteins fold, but needed for the purpose the fastest computers available today.
One of these computers is Anton,
the expensive special-purpose supercomputer available essentially only to a single research group.
Is there an affordable way to simulate protein folding?
One solution could be coarse-grained methods.
These methods save tremendous computational effort by replacing
computational models that include all atomistic detail.
However,
the simplified models need to include a sufficiently accurate description of proteins
for modeling folding processes. As reported recently,
researchers have overcome the challenge by combining atomistic and coarse-grained descriptions.
The new method is fast enough to follow movements of proteins long enough to see them fold,
while requiring only readily available computer powers. The new method allowed
researchers to analyze complete folding events for seven proteins,
including a protein, called α3D (see movie, 11.5 M),
that is one of the largest proteins ever folded computationally.
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