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Journal of Computational Physics 151, 402421 (1999) Article ID jcph.1999.6233, available online at http://www.idealibrary.com on
 

Summary: Journal of Computational Physics 151, 402421 (1999)
Article ID jcph.1999.6233, available online at http://www.idealibrary.com on
Ab Initio Folding of Peptides by the Optimal-Bias
Monte Carlo Minimization Procedure
Ruben A. Abagyan and Maxim Totrov
Biochemistry Department, Skirball Institute of Biomolecular Medicine, and Courant Institute
of Mathematics, New York University, 540 First Avenue, New York, New York 10016
Received August 20, 1998; revised February 15, 1999
Prediction of three-dimensional structures of proteins and peptides by global op-
timization of the free energy estimate has been attempted without much success for
over thirty years. The key problems were the insufficient accuracy of the free energy
estimate and the giant size of the conformational space. Global optimization of the
free energy function of a peptide in internal coordinate space is a powerful method
of structure prediction that outperforms both Molecular Dynamics, bound by the
continuity requirement, and Monte Carlo, bound by the Boltzmann ensemble gener-
ation requirement. We demonstrate that stochastic global optimization algorithms of
the first order, i.e., with local minimization after each iteration (e.g., Monte Carlo-
Minimization), have a greater chance of finding the global minimum after a fixed
number of function evaluations. Recently, the principle of optimal bias was mathe-
matically justified and the Optimal-Bias Monte Carlo-Minimization algorithm (a.k.a.

  

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

 

Collections: Biology and Medicine