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A surprising simplicity to protein folding

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  • David Baker

    (University of Washington)

Abstract

The polypeptide chains that make up proteins have thousands of atoms and hence millions of possible inter-atomic interactions. It might be supposed that the resulting complexity would make prediction of protein structure and protein-folding mechanisms nearly impossible. But the fundamental physics underlying folding may be much simpler than this complexity would lead us to expect: folding rates and mechanisms appear to be largely determined by the topology of the native (folded) state, and new methods have shown great promise in predicting protein-folding mechanisms and the three-dimensional structures of proteins.

Suggested Citation

  • David Baker, 2000. "A surprising simplicity to protein folding," Nature, Nature, vol. 405(6782), pages 39-42, May.
    • Handle: RePEc:nat:nature:v:405:y:2000:i:6782:d:10.1038_35011000
    DOI: 10.1038/35011000
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    Cited by:

    1. Omar Haq & Michael Andrec & Alexandre V Morozov & Ronald M Levy, 2012. "Correlated Electrostatic Mutations Provide a Reservoir of Stability in HIV Protease," PLOS Computational Biology, Public Library of Science, vol. 8(9), pages 1-10, September.
    2. Yaman Arkun & Burak Erman, 2010. "Prediction of Optimal Folding Routes of Proteins That Satisfy the Principle of Lowest Entropy Loss: Dynamic Contact Maps and Optimal Control," PLOS ONE, Public Library of Science, vol. 5(10), pages 1-11, October.
    3. Yaman Arkun & Mert Gur, 2012. "Combining Optimal Control Theory and Molecular Dynamics for Protein Folding," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-8, January.
    4. Marwa Mohammed M. Ghareeb & Ahmed Sharaf Eldin & Taysir Hassan A. Soliman & Mohammed Ebrahim Marie, 2013. "A Deeply Glimpse into Protein Fold Recognition," International Journal of Sciences, Office ijSciences, vol. 2(06), pages 24-33, June.
    5. Stefano Zamuner & Flavio Seno & Antonio Trovato, 2022. "Statistical potentials from the Gaussian scaling behaviour of chain fragments buried within protein globules," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-20, January.
    6. Seth Lichter & Benjamin Rafferty & Zachary Flohr & Ashlie Martini, 2012. "Protein High-Force Pulling Simulations Yield Low-Force Results," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-10, April.
    7. Christopher A Brown & Kevin S Brown, 2010. "Validation of Coevolving Residue Algorithms via Pipeline Sensitivity Analysis: ELSC and OMES and ZNMI, Oh My!," PLOS ONE, Public Library of Science, vol. 5(6), pages 1-14, June.
    8. Amit K Chattopadhyay & Biswajit Debnath & Rihab El-Hassani & Sadhan Kumar Ghosh & Rahul Baidya, 2020. "Cleaner Production in Optimized Multivariate Networks: Operations Management through a Roll of Dice," Papers 2003.00884, arXiv.org.

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