Computational protein structure refinement: almost there, yet still so far to go

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    Abstract

    Protein structures are essential in modern biology yet experimental methods are far from being able to catch up with the rapid increase in available genomic data. Computational protein structure prediction methods aim to fill the gap while the role of protein structure refinement is to take approximate initial template-based models and bring them closer to the true native structure. Current methods for computational structure refinement rely on molecular dynamics simulations, related sampling methods, or iterative structure optimization protocols. The best methods are able to achieve moderate degrees of refinement but consistent refinement that can reach near-experimental accuracy remains elusive. Key issues revolve around the accuracy of the energy function, the inability to reliably rank multiple models, and the use of restraints that keep sampling close to the native state but also limit the degree of possible refinement. A different aspect is the question of what exactly the target of high-resolution refinement should be as experimental structures are affected by experimental conditions and different biological questions require varying levels of accuracy. While improvement of the global protein structure is a difficult problem, high-resolution refinement methods that improve local structural quality such as favorable stereochemistry and the avoidance of atomic clashes are much more successful. WIREs Comput Mol Sci 2017, 7:e1307. doi: 10.1002/wcms.1307. For further resources related to this article, please visit the WIREs website.

    Original languageEnglish (US)
    Article numbere1307
    JournalWiley Interdisciplinary Reviews: Computational Molecular Science
    Volume7
    Issue number3
    DOIs
    StatePublished - May 1 2017

    Profile

    proteins
    Refinement
    Proteins
    Protein structure
    sampling
    high resolution
    Sampling
    Beta-Globulins
    Anthralin
    High resolution
    websites
    avoidance
    stereochemistry
    biology
    resources
    templates
    molecular dynamics
    optimization
    predictions
    simulation

    ASJC Scopus subject areas

    • Biochemistry
    • Computer Science Applications
    • Physical and Theoretical Chemistry
    • Computational Mathematics
    • Materials Chemistry

    Cite this

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    title = "Computational protein structure refinement: almost there, yet still so far to go",
    abstract = "Protein structures are essential in modern biology yet experimental methods are far from being able to catch up with the rapid increase in available genomic data. Computational protein structure prediction methods aim to fill the gap while the role of protein structure refinement is to take approximate initial template-based models and bring them closer to the true native structure. Current methods for computational structure refinement rely on molecular dynamics simulations, related sampling methods, or iterative structure optimization protocols. The best methods are able to achieve moderate degrees of refinement but consistent refinement that can reach near-experimental accuracy remains elusive. Key issues revolve around the accuracy of the energy function, the inability to reliably rank multiple models, and the use of restraints that keep sampling close to the native state but also limit the degree of possible refinement. A different aspect is the question of what exactly the target of high-resolution refinement should be as experimental structures are affected by experimental conditions and different biological questions require varying levels of accuracy. While improvement of the global protein structure is a difficult problem, high-resolution refinement methods that improve local structural quality such as favorable stereochemistry and the avoidance of atomic clashes are much more successful. WIREs Comput Mol Sci 2017, 7:e1307. doi: 10.1002/wcms.1307. For further resources related to this article, please visit the WIREs website.",
    author = "Michael Feig",
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    AB - Protein structures are essential in modern biology yet experimental methods are far from being able to catch up with the rapid increase in available genomic data. Computational protein structure prediction methods aim to fill the gap while the role of protein structure refinement is to take approximate initial template-based models and bring them closer to the true native structure. Current methods for computational structure refinement rely on molecular dynamics simulations, related sampling methods, or iterative structure optimization protocols. The best methods are able to achieve moderate degrees of refinement but consistent refinement that can reach near-experimental accuracy remains elusive. Key issues revolve around the accuracy of the energy function, the inability to reliably rank multiple models, and the use of restraints that keep sampling close to the native state but also limit the degree of possible refinement. A different aspect is the question of what exactly the target of high-resolution refinement should be as experimental structures are affected by experimental conditions and different biological questions require varying levels of accuracy. While improvement of the global protein structure is a difficult problem, high-resolution refinement methods that improve local structural quality such as favorable stereochemistry and the avoidance of atomic clashes are much more successful. WIREs Comput Mol Sci 2017, 7:e1307. doi: 10.1002/wcms.1307. For further resources related to this article, please visit the WIREs website.

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