Accurate protein structure modeling using sparse NMR data and homologous structure information

James M. Thompson, Nikolaos G. Sgourakis, Gaohua Liu, Paolo Rossi, Yuefeng Tang, Jeffrey L. Mills, Thomas Szyperski, Gaetano T. Montelione, David Baker

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

While information from homologous structures plays a central role in X-ray structure determination by molecular replacement, such information is rarely used in NMR structure determination because it can be incorrect, both locally and globally, when evolutionary relationships are inferred incorrectly or there has been considerable evolutionary structural divergence. Here we describe a method that allows robust modeling of protein structures of up to 225 residues by combining 1HN, 13C, and 15N backbone and 13Cβ chemical shift data, distance restraints derived from homologous structures, and a physically realistic all-atom energy function. Accurate models are distinguished from inaccurate models generated using incorrect sequence alignments by requiring that (i) the all-atom energies of models generated using the restraints are lower than models generated in unrestrained calculations and (ii) the low-energy structures converge to within 2.0 Å backbone rmsd over 75% of the protein. Benchmark calculations on known structures and blind targets show that the method can accurately model protein structures, even with very remote homology information, to a backbone rmsd of 1.2-1.9 Å relative to the conventional determined NMR ensembles and of 0.9-1.6 Å relative to X-ray structures for well-defined regions of the protein structures. This approach facilitates the accurate modeling of protein structures using backbone chemical shift data without need for side-chain resonance assignments and extensive analysis of NOESY cross-peak assignments.

Original languageEnglish (US)
Pages (from-to)9875-9880
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume109
Issue number25
DOIs
StatePublished - Jun 19 2012

Keywords

  • Biochemistry
  • Biophysics
  • Computational biology
  • Nuclear magnetic resonance
  • Structural genomics

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