title: 3D Computational Modeling of Proteins Using Sparse Paramagnetic NMR Data.
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.
authors.affiliation: Research School of Chemistry, Australian National University, Canberra, Australia.
authors.forename: Kala Bharath
authors.initials: KB
authors.lastname: Pilla
authors.affiliation: Research School of Chemistry, Australian National University, Canberra, Australia.
authors.forename: Gottfried
authors.initials: G
authors.lastname: Otting
authors.affiliation: Research School of Chemistry, Australian National University, Canberra, Australia. t.huber@anu.edu.au.
authors.forename: Thomas
authors.initials: T
authors.lastname: Huber
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D019906:Nuclear Magnetic Resonance, Biomolecular / Q000379:methods*; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins
keywords: 3D structure determination; GPS-Rosetta; PCS; Paramagnetic NMR; Pseudocontact shifts; Rosetta; Sparse restraints