3D computational modeling of proteins using sparse paramagnetic NMR data

Kala Bharath Pilla, Gottfried Otting, Thomas Huber*

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

    8 Citations (Scopus)

    Abstract

    Computational modeling of proteins using evolutionary or de novo approaches offers rapid structural characterization, but often suffers from low success rates in generating high quality models comparable to the accuracy of structures observed in X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy. A computational/experimental hybrid approach incorporating sparse experimental restraints in computational modeling algorithms drastically improves reliability and accuracy of 3D models. This chapter discusses the use of structural information obtained from various paramagnetic NMR measurements and demonstrates computational algorithms implementing pseudocontact shifts as restraints to determine the structure of proteins at atomic resolution.

    Original languageEnglish
    Title of host publicationMethods in Molecular Biology
    PublisherHumana Press Inc.
    Pages3-21
    Number of pages19
    DOIs
    Publication statusPublished - 2017

    Publication series

    NameMethods in Molecular Biology
    Volume1526
    ISSN (Print)1064-3745

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