D
Daniel A. Keedy
Researcher at University of California, San Francisco
Publications - 43
Citations - 17412
Daniel A. Keedy is an academic researcher from University of California, San Francisco. The author has contributed to research in topics: Conformational ensembles & Cypa. The author has an hindex of 19, co-authored 36 publications receiving 12992 citations. Previous affiliations of Daniel A. Keedy include Duke University & University of California, Berkeley.
Papers
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Journal ArticleDOI
MolProbity: all-atom structure validation for macromolecular crystallography
Vincent B. Chen,W. Bryan Arendall,Jeffrey J. Headd,Daniel A. Keedy,R.M. Immormino,Gary J. Kapral,Laura Weston Murray,Jane S. Richardson,David S. Richardson +8 more
TL;DR: MolProbity structure validation will diagnose most local errors in macromolecular crystal structures and help to guide their correction.
Journal ArticleDOI
MolProbity: More and better reference data for improved all-atom structure validation.
Christopher J. Williams,Jeffrey J. Headd,Nigel W. Moriarty,Michael G. Prisant,Lizbeth L. Videau,Lindsay N. Deis,Vishal Verma,Daniel A. Keedy,Bradley J. Hintze,Vincent B. Chen,Swati Jain,Steven M. Lewis,W. Bryan Arendall,Jack Snoeyink,Paul D. Adams,Simon C. Lovell,Jane S. Richardson,David S. Richardson +17 more
TL;DR: Due to wide application of MolProbity validation and corrections by the research community, in Phenix, and at the worldwide Protein Data Bank, newly deposited structures have continued to improve greatly as measured by Mol probity's unique all‐atom clashscore.
Book ChapterDOI
Chapter 21.6 MolProbity: all-atom structure validation for macromolecular crystallography
Vincent B. Chen,W.B. Arendall,Jeffrey J. Headd,Daniel A. Keedy,R.M. Immormino,Gary J. Kapral,Laura Weston Murray,Jane S. Richardson,David S. Richardson +8 more
TL;DR: MolProbity is the authors’ contribution to helping solve the problem of local errors in X-ray crystallography and this chapter reviews its general capabilities, reports on recent enhancements and usage, and presents evidence that the resulting improvements are now beneficially affecting the global database.
Journal ArticleDOI
Alternate States of Proteins Revealed by Detailed Energy Landscape Mapping
Michael D. Tyka,Daniel A. Keedy,Ingemar André,Frank DiMaio,Yifan Song,David S. Richardson,Jane S. Richardson,David Baker +7 more
TL;DR: In the absence of tightly associating binding partners or ligands, the lowest-energy Rosetta models were nearly all <2.5 Å C(α)RMSD from the experimental structure; this result demonstrates that structure prediction accuracy for globular proteins is limited mainly by the ability to sample close to the native structure.
Journal ArticleDOI
CryptoSite: Expanding the Druggable Proteome by Characterization and Prediction of Cryptic Binding Sites.
Peter Cimermancic,Patrick Weinkam,T. Justin Rettenmaier,Leon Bichmann,Daniel A. Keedy,Rahel A. Woldeyes,Dina Schneidman-Duhovny,Omar N. A. Demerdash,Julie C. Mitchell,James A. Wells,James S. Fraser,Andrej Sali +11 more
TL;DR: The CryptoSite approach comprehensively characterize the cryptic sites in terms of their sequence, structure, and dynamics attributes, and finds that cryptic sites tend to be as conserved in evolution as traditional binding pockets but are less hydrophobic and more flexible.