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Phys Rev E Stat Nonlin Soft Matter Phys. 2004 Sep;70(3 Pt 1):030903. Epub 2004 Sep 27.

Teaching computers to fold proteins.

Author information

  • 1Center for Biological Sequence Analysis, The Technical University of Denmark, Building 208, DK-2800 Lyngby, Denmark. owi@imm.dtu.dk

Abstract

A new general algorithm for optimization of potential functions for protein folding is introduced. It is based upon gradient optimization of the thermodynamic stability of native folds of a training set of proteins with known structure. The iterative update rule contains two thermodynamic averages which are estimated by (generalized ensemble) Monte Carlo. We test the learning algorithm on a Lennard-Jones (LJ) force field with a torsional angle degrees-of-freedom and a single-atom side-chain. In a test with 24 peptides of known structure, none folded correctly with the initial potential functions, but two-thirds came within 3 A to their native fold after optimizing the potential functions.

PMID:
15524499
[PubMed - indexed for MEDLINE]
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