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RosettaHoles: Rapid assessment of protein core packing for structure prediction, refinement, design, and validation
Author(s) -
Sheffler Will,
Baker David
Publication year - 2009
Publication title -
protein science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.353
H-Index - 175
eISSN - 1469-896X
pISSN - 0961-8368
DOI - 10.1002/pro.8
Subject(s) - visualization , representation (politics) , computer science , core (optical fiber) , set (abstract data type) , protein structure prediction , ball (mathematics) , biological system , protein data bank , algorithm , protein structure , computational science , data mining , geometry , physics , mathematics , biology , telecommunications , nuclear magnetic resonance , politics , political science , law , programming language
We present a novel method called RosettaHoles for visual and quantitative assessment of underpacking in the protein core. RosettaHoles generates a set of spherical cavity balls that fill the empty volume between atoms in the protein interior. For visualization, the cavity balls are aggregated into contiguous overlapping clusters and small cavities are discarded, leaving an uncluttered representation of the unfilled regions of space in a structure. For quantitative analysis, the cavity ball data are used to estimate the probability of observing a given cavity in a high‐resolution crystal structure. RosettaHoles provides excellent discrimination between real and computationally generated structures, is predictive of incorrect regions in models, identifies problematic structures in the Protein Data Bank, and promises to be a useful validation tool for newly solved experimental structures.