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Efficient Stochastic Global Optimization for Protein Structure Prediction

  • Chapter
Rigidity Theory and Applications

Part of the book series: Fundamental Materials Research ((FMRE))

Summary

The native structure of a protein may be described with reasonable accuracy as the global minimum of the free energy (in the pseudo-potential energy form), only as a function of free torsion angles. Therefore, global optimization methods might be preferable over methods designed to create dynamic ensembles, such as MD or MC that are bound by the trajectory continuity requirement or the local balance requirement.

The Monte Carlo Minimization (MCM) method outperforms zero order MC-like stochastic global optimization protocols.

The Optimal-Bias-MCM method further improves the sampling efficiency by an order of magnitude by incorporating the optimal-bias into MC conformation generation. The square-root bias derived in this work and the linear bias13 are two possible strategies.

The OBMCM algorithm can predict a 23-residue ββα peptide56, with 70 essential torsion angles and 385 atoms, starting from completely random conformations. (Figure 3).

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Authors and Affiliations

  1. Skirball Institute of Bimolecular Medicine Biochemistry Department, New York University Medical Center, 540 1st Avenue, New York, NY, 10016

    Yingyao Zhou & Ruben Abagyan

Authors
  1. Yingyao Zhou
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  2. Ruben Abagyan
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Editors and Affiliations

  1. Michigan State University, East Lansing, Michigan

    M. F. Thorpe  & P. M. Duxbury  & 

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© 2002 Kluwer Academic Publishers

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Zhou, Y., Abagyan, R. (2002). Efficient Stochastic Global Optimization for Protein Structure Prediction. In: Thorpe, M.F., Duxbury, P.M. (eds) Rigidity Theory and Applications. Fundamental Materials Research. Springer, Boston, MA. https://doi.org/10.1007/0-306-47089-6_19

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  • DOI: https://doi.org/10.1007/0-306-47089-6_19

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-306-46115-6

  • Online ISBN: 978-0-306-47089-9

  • eBook Packages: Springer Book Archive

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