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Teaching computers to fold proteins

Ole Winther·Anders Krogh
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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 3Å to their native fold after optimizing the potential functions.

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Ole Winther, Anders Krogh. 2003-09-22. Teaching computers to fold proteins. https://doi.org/10.1103/physreve.70.030903

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