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EFFICIENT STOCHASTIC GLOBAL OPTIMIZATION FOR PROTEIN STRUCTURE PREDICTION
 

Summary: EFFICIENT STOCHASTIC GLOBAL OPTIMIZATION FOR PROTEIN
STRUCTURE PREDICTION
Yingyao Zhou and Ruben Abagyan*
Skirball Institute of Bimolecular Medicine
Biochemistry Department
New York University Medical Center
540 Avenue, New York, NY 10016
INTRODUCTION: Why not MD or MC?
Biological macromolecules, large chain molecules with hundreds of torsion angles,
adopt compact, uniquely folded and rigid conformations that correspond to their global free
energy minimum. Predicting this unique conformation from a vast number of alternatives,
for the whole protein or its parts, is the biggest challenge of computational biology. One of
the difficulties is conceptual. To evaluate the free energy correctly we need to account for
the dynamic nature of the entire system, including mobile water molecules, flexible side-
chains and soft vibrational modes of a solute. Molecular Dynamics (MD, reviewed in Ref.
1-4) or Monte Carlo simulations (MC, reviewed in Ref. 4-8) in water can be applied to
sample the conformational space and evaluate the free energy. However, these methods are
still too slow to reach the biologically relevant folding times for proteins or even large
peptides2,9
.

  

Source: Abagyan, Ruben - School of Pharmacy and Pharmaceutical Sciences, University of California at San Diego

 

Collections: Biology and Medicine