Teaching computers to fold proteins

Publication: Research - peer-reviewJournal article – Annual report year: 2004

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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 Angstrom to their native fold after optimizing the potential functions.
Original languageEnglish
JournalPhysical Review E (Statistical, Nonlinear, and Soft Matter Physics)
Publication date2004
Volume70
Issue3
Pages030903
ISSN1539-3755
DOIs
StatePublished

Bibliographical note

Copyright (2004) American Physical Society.

CitationsWeb of Science® Times Cited: 8

Keywords

  • OPTIMIZATION, GLOBULAR-PROTEINS, ENERGY, PREDICTION, POTENTIALS
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