@inbook{49286055d0d246c2bdf60dbbdceaa7a3,
title = "Scientific benchmarks for guiding macromolecular energy function improvement",
abstract = "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).",
keywords = "Decoy discrimination, Energy function, Parameter estimation, Rosetta, Scientific benchmarking",
author = "Andrew Leaver-Fay and O'Meara, {Matthew J.} and Mike Tyka and Ron Jacak and Yifan Song and Kellogg, {Elizabeth H.} and James Thompson and Davis, {Ian W.} and Pache, {Roland A.} and Sergey Lyskov and Gray, {Jeffrey J.} and Tanja Kortemme and Richardson, {Jane S.} and Havranek, {James J.} and Jack Snoeyink and David Baker and Brian Kuhlman",
year = "2013",
doi = "10.1016/B978-0-12-394292-0.00006-0",
language = "English (US)",
isbn = "9780123942920",
series = "Methods in Enzymology",
publisher = "Academic Press Inc.",
pages = "109--143",
booktitle = "Methods in Protein Design",
}