Publication: Research - peer-review › Journal article – Annual report year: 2004
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.
|Journal||Physical Review E (Statistical, Nonlinear, and Soft Matter Physics)|
|State||Published - 2004|
Copyright (2004) American Physical Society.
|Citations||Web of Science® Times Cited: No match on DOI|
- OPTIMIZATION, GLOBULAR-PROTEINS, ENERGY, PREDICTION, POTENTIALS
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