Journal article · Preprint article
Teaching computers to fold proteins
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.
Language: | English |
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Year: | 2004 |
Pages: | 030903 |
ISSN: | 15502376 , 15393755 , 24700053 and 24700045 |
Types: | Journal article and Preprint article |
DOI: | 10.1103/PhysRevE.70.030903 |
ORCIDs: | Winther, Ole and 0000-0002-5147-6282 |