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3D Computational Modeling of Proteins Using Sparse Paramagnetic NMR Data

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Bioinformatics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1526))

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.

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Authors and Affiliations

  1. Research School of Chemistry, Australian National University, Canberra, Australia

    Kala Bharath Pilla, Gottfried Otting & Thomas Huber

Authors
  1. Kala Bharath Pilla
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  2. Gottfried Otting
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  3. Thomas Huber
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Corresponding author

Correspondence to Thomas Huber .

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Editors and Affiliations

  1. Monash University, Melbourne, Victoria, Australia

    Jonathan M. Keith

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Pilla, K.B., Otting, G., Huber, T. (2017). 3D Computational Modeling of Proteins Using Sparse Paramagnetic NMR Data. In: Keith, J. (eds) Bioinformatics. Methods in Molecular Biology, vol 1526. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6613-4_1

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  • DOI: https://doi.org/10.1007/978-1-4939-6613-4_1

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-6611-0

  • Online ISBN: 978-1-4939-6613-4

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