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search. These generally involve a move set, an acceptance criterion, and an energy
function.[2] The energy function can be a force field as above, and a wide array of move
sets can be used, which are often intentionally biased in some way. The acceptance
criterion is probabilistic; an energetically favorable move will always be accepted, but an
unfavorable one will be accepted with probability = exp(( E E ) / k T ) .
P old ! new B
Spectral decomposition, or Normal Mode analysis, is another popular method for
predicting flexibility and conformational change. Interatomic interactions being
nonlinear as mentioned, the eigenvectors can be obtained only for a linearized version of
the potential. Various authors have shown that large scale conformational change can be
mostly represented using several low order eigenvectors or normal modes. The popular
Gaussian Network Model postulates that the low order modes of proteins can be obtained
from a simplified model of the protein consisting of point masses at the α-carbon
positions and identical linear springs connecting all point masses within a 7Å radius of
each other. Despite the far-reaching simplifications this method has great utility as we
will show.
We decided that to compute the dynamics of proteins we would need to incorporate
knowledge about protein motions gained through observation in a database. In prior
work in the Gerstein group, protein motions were classified based on size into fragment
and domain. They were further classified based on the interactions between moving
regions as hinge, shear, and other. Hinge bending motions are those in which regions of
the protein exhibit approximately rigid body rotations about a hinge point. Despite the
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