Stochastic optimization methods for protein folding

  • Authors:

  • Alexander Schug;Abhinav Verma;Kyu Hwan Lee;Wolfgang Wenzel

  • Affiliations:

  • Forschungszentrum Karlsruhe, Institut für Nanotechnologie, Karlsruhe, Germany and Supercomputing Materials Laboratory, Korean Institute for Science and Technology, Seoul, Korea;Forschungszentrum Karlsruhe, Institut für Nanotechnologie, Karlsruhe, Germany and Supercomputing Materials Laboratory, Korean Institute for Science and Technology, Seoul, Korea;Forschungszentrum Karlsruhe, Institut für Nanotechnologie, Karlsruhe, Germany and Supercomputing Materials Laboratory, Korean Institute for Science and Technology, Seoul, Korea;Forschungszentrum Karlsruhe, Institut für Nanotechnologie, Karlsruhe, Germany and Supercomputing Materials Laboratory, Korean Institute for Science and Technology, Seoul, Korea

  • Venue:
  • SMO'05 Proceedings of the 5th WSEAS international conference on Simulation, modelling and optimization
  • Year:
  • 2005

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Abstract

We recently developed an all-atom free energy forcefield (PFF01) for protein structure prediction with stochastic optimization methods. We demonstrated that PFF01 correctly predicts the native conformation of several proteins as the global optimum of the free energy surface. Here we review recent folding studies, which permitted the reproducible all-atom folding of the 20 amino-acid trp-cage protein, the 40-amino acid three-helix HIV accessory protein and the sixty amino acid bacterial ribosomal protein L20 with a variety of stochastic optimization methods. These results demonstrate that all-atom protein folding can be achieved with present day computational resources for proteins of moderate size.