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Coarse-Grained Models of Proteins: Theory and Applications

  • Cezary Czaplewski
  • Adam Liwo
  • Mariusz Makowski
  • Stanisław Ołdziej
  • Harold A. Scheraga
Chapter

Abstract

In this chapter, reduced (coarse-grained) protein models are discussed. Emphasis is given to those models which can be used in simulating the structure, thermodynamics, and dynamics of real proteins and are, at the same time, transferable. The coarse-grained force fields are introduced in a physics-based way as potentials of mean force of polypeptide chains in reduced representations, in which the secondary degrees of freedom have been averaged out. Based on this general formula, three categories of coarse-grained potentials are introduced: (i) statistical potentials derived from structural databases, (ii) potentials obtained by factorization of the parent potential of mean force, which enables us to split the system into smaller subsystems and derive each effective energy contribution independently, and (iii) potentials obtained by the force-matching method. Optimization of the potential function to achieve foldability is discussed. Applications of coarse-grained potentials to predict protein structures and simulate long-time protein dynamics are presented. We conclude that while, with the aid of massively parallel computers, coarse graining enables us to reach millisecond simulation timescales of real-size proteins, and case studies indicate that the results of these simulations are realistic, much work remains to be done to improve the force fields.

Keywords

Monte Carlo Protein Structure Prediction Bovine Pancreatic Trypsin Inhibitor Study Protein Dynamic Effective Energy Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We thank Ana Rojas for assistance in preparing Fig. 3.8. This work is supported by grants from the National Institutes of Health (GM-14312), the National Science Foundation (MCB00-03722), and grants N N204 049035 (grant contract number 0490/H03/2008/35) and N N204 152836 from the Polish Ministry of Science and Higher Education. Mariusz Makowski was also supported by a grant from the “Homing” program of the Foundation for Polish Science (FNP) and MF EOG resources . This research is conducted by using the resources of (a) our 800-processor Beowulf cluster at the Baker Laboratory of Chemistry and Chemical Biology, Cornell University, (b) the National Science Foundation Terascale Computing System at the Pittsburgh Supercomputer Center, (c) the John von Neumann Institute for Computing at the Central Institute for Applied Mathematics, Forschungszentrum Juelich, Germany, (d) the Beowulf cluster at the Department of Computer Science, Cornell University, (e) the resources of the Center for Computation and Technology at Louisiana State University, which is supported by funding from the Louisiana legislature, (f) our 45-processor Beowulf cluster at the Faculty of Chemistry, University of Gdańsk, (g) the Informatics Center of the Metropolitan Academic Network (IC MAN) in Gdańsk, and (h) the Interdisciplinary Center of Mathematical and Computer Modeling (ICM) at the University of Warsaw.

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Cezary Czaplewski
    • 1
    • 2
  • Adam Liwo
    • 1
    • 2
  • Mariusz Makowski
    • 1
    • 2
  • Stanisław Ołdziej
    • 2
    • 3
  • Harold A. Scheraga
    • 2
  1. 1.Faculty of ChemistryUniversity of GdańskGdańskPoland
  2. 2.Baker Laboratory of Chemistry and Chemical BiologyCornell UniversityIthacaUSA
  3. 3.Laboratory of Biopolymer Structure, Intercollegiate Faculty of BiotechnologyUniversity of Gdańsk and Medical University of GdańskGdańskPoland

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