Scientific benchmarks for guiding macromolecular energy function improvement

Andrew Leaver-Fay, Matthew J. O'Meara, Mike Tyka, Ron Jacak, Yifan Song, Elizabeth H. Kellogg, James Thompson, Ian W. Davis, Roland A. Pache, Sergey Lyskov, Jeffrey J. Gray, Tanja Kortemme, Jane S. Richardson, James J. Havranek, Jack Snoeyink, David Baker, Brian Kuhlman

Research output: Chapter in Book/Report/Conference proceedingChapter

134 Scopus citations


Accurate energy functions are critical to macromolecular modeling and design. We describe new tools for identifying inaccuracies in energy functions and guiding their improvement, and illustrate the application of these tools to the improvement of the Rosetta energy function. The feature analysis tool identifies discrepancies between structures deposited in the PDB and low-energy structures generated by Rosetta; these likely arise from inaccuracies in the energy function. The optE tool optimizes the weights on the different components of the energy function by maximizing the recapitulation of a wide range of experimental observations. We use the tools to examine three proposed modifications to the Rosetta energy function: improving the unfolded state energy model (reference energies), using bicubic spline interpolation to generate knowledge-based torisonal potentials, and incorporating the recently developed Dunbrack 2010 rotamer library (Shapovalov & Dunbrack, 2011).

Original languageEnglish (US)
Title of host publicationMethods in Protein Design
PublisherAcademic Press Inc.
Number of pages35
ISBN (Print)9780123942920
StatePublished - 2013

Publication series

NameMethods in Enzymology
ISSN (Print)0076-6879
ISSN (Electronic)1557-7988


  • Decoy discrimination
  • Energy function
  • Parameter estimation
  • Rosetta
  • Scientific benchmarking

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology


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