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  • Pairing a high-resolution statistical potential with a nucleobase-centric sampling algorithm for improving RNA model refinement

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    Xiong492222-Published.pdf (3.704Mb)
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    Version of Record (VoR)
    Author(s)
    Xiong, P
    Wu, R
    Zhan, J
    Zhou, Y
    Griffith University Author(s)
    Zhan, Jian
    Xiong, Peng
    Zhou, Yaoqi
    Year published
    2021
    Metadata
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    Abstract
    Refining modelled structures to approach experimental accuracy is one of the most challenging problems in molecular biology. Despite many years’ efforts, the progress in protein or RNA structure refinement has been slow because the global minimum given by the energy scores is not at the experimentally determined “native” structure. Here, we propose a fully knowledge-based energy function that captures the full orientation dependence of base–base, base–oxygen and oxygen–oxygen interactions with the RNA backbone modelled by rotameric states and internal energies. A total of 4000 quantum-mechanical calculations were performed ...
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    Refining modelled structures to approach experimental accuracy is one of the most challenging problems in molecular biology. Despite many years’ efforts, the progress in protein or RNA structure refinement has been slow because the global minimum given by the energy scores is not at the experimentally determined “native” structure. Here, we propose a fully knowledge-based energy function that captures the full orientation dependence of base–base, base–oxygen and oxygen–oxygen interactions with the RNA backbone modelled by rotameric states and internal energies. A total of 4000 quantum-mechanical calculations were performed to reweight base–base statistical potentials for minimizing possible effects of indirect interactions. The resulting BRiQ knowledge-based potential, equipped with a nucleobase-centric sampling algorithm, provides a robust improvement in refining near-native RNA models generated by a wide variety of modelling techniques.
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    Journal Title
    Nature Communications
    Volume
    12
    Issue
    1
    DOI
    https://doi.org/10.1038/s41467-021-23100-4
    Copyright Statement
    © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
    Subject
    Biostatistics
    Clinical sciences
    Medical microbiology
    Publication URI
    http://hdl.handle.net/10072/404719
    Collection
    • Journal articles

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