Teaching computers to fold proteins

Ole Winther, Anders Stærmose Krogh

    Research output: Contribution to journalJournal articleResearchpeer-review

    267 Downloads (Pure)

    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 Angstrom to their native fold after optimizing the potential functions.
    Original languageEnglish
    JournalPhysical Review E
    Volume70
    Issue number3
    Pages (from-to)030903
    ISSN2470-0045
    DOIs
    Publication statusPublished - 2004

    Bibliographical note

    Copyright (2004) American Physical Society.

    Keywords

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

    Fingerprint Dive into the research topics of 'Teaching computers to fold proteins'. Together they form a unique fingerprint.

    Cite this