3D Computational Modeling of Proteins Using Sparse Paramagnetic NMR Data

Kala Bharath Pilla; Gottfried Otting; Thomas Huber

doi:10.1007/978-1-4939-6613-4_1

3D Computational Modeling of Proteins Using Sparse Paramagnetic NMR Data

Methods Mol Biol. 2017:1526:3-21. doi: 10.1007/978-1-4939-6613-4_1.

Authors

Kala Bharath Pilla¹, Gottfried Otting¹, Thomas Huber²

Affiliations

¹ Research School of Chemistry, Australian National University, Canberra, Australia.
² Research School of Chemistry, Australian National University, Canberra, Australia. t.huber@anu.edu.au.

PMID: 27896733
DOI: 10.1007/978-1-4939-6613-4_1

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.

Keywords: 3D structure determination; GPS-Rosetta; PCS; Paramagnetic NMR; Pseudocontact shifts; Rosetta; Sparse restraints.

MeSH terms

Algorithms
Computational Biology / methods*
Computer Simulation
Nuclear Magnetic Resonance, Biomolecular / methods*
Proteins / chemistry*

Substances

Proteins