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Evaluating the quality of NMR structures by local density of protons
Author(s) -
Ban YihEn Andrew,
Rudolph Johannes,
Zhou Pei,
Edelsbrunner Herbert
Publication year - 2005
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.20811
Subject(s) - crystal structure prediction , proton , crystal structure , quality (philosophy) , crystal (programming language) , proton nmr , resolution (logic) , crystallography , chemistry , materials science , nuclear magnetic resonance , physics , computer science , nuclear physics , artificial intelligence , quantum mechanics , programming language
Evaluating the quality of experimentally determined protein structural models is an essential step toward identifying potential errors and guiding further structural refinement. Herein, we report the use of proton local density as a sensitive measure to assess the quality of nuclear magnetic resonance (NMR) structures. Using 256 high‐resolution crystal structures with protons added and optimized, we show that the local density of different proton types display distinct distributions. These distributions can be characterized by statistical moments and are used to establish local density Z‐scores for evaluating both global and local packing for individual protons. Analysis of 546 crystal structures at various resolutions shows that the local density Z‐scores increase as the structural resolution decreases and correlate well with the ClashScore (Word et al. J Mol Biol 1999;285(4):1711–1733) generated by all atom contact analysis. Local density Z‐scores for NMR structures exhibit a significantly wider range of values than for X‐ray structures and demonstrate a combination of potentially problematic inflation and compression. Water‐refined NMR structures show improved packing quality. Our analysis of a high‐quality structural ensemble of ubiquitin refined against order parameters shows proton density distributions that correlate nearly perfectly with our standards derived from crystal structures, further validating our approach. We present an automated analysis and visualization tool for proton packing to evaluate the quality of NMR structures. Proteins 2006. © 2005 Wiley‐Liss, Inc.