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An assignment of intrinsically disordered regions of proteins based on NMR structures
Authors:Motonori Ota  Ryotaro Koike  Takayuki Amemiya  Takeshi Tenno  Pedro R. Romero  Hidekazu Hiroaki  A. Keith Dunker  Satoshi Fukuchi
Affiliation:1. Graduate School of Information Sciences, Nagoya University, Nagoya 464-8601, Japan;2. Graduate School of Pharmaceutical Science, Nagoya University, Nagoya 464-8601, Japan;3. Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA;4. Faculty of Engineering, Maebashi Institute of Technology, Maebashi 371-0816, Japan
Abstract:Intrinsically disordered proteins (IDPs) do not adopt stable three-dimensional structures in physiological conditions, yet these proteins play crucial roles in biological phenomena. In most cases, intrinsic disorder manifests itself in segments or domains of an IDP, called intrinsically disordered regions (IDRs), but fully disordered IDPs also exist. Although IDRs can be detected as missing residues in protein structures determined by X-ray crystallography, no protocol has been developed to identify IDRs from structures obtained by Nuclear Magnetic Resonance (NMR). Here, we propose a computational method to assign IDRs based on NMR structures. We compared missing residues of X-ray structures with residue-wise deviations of NMR structures for identical proteins, and derived a threshold deviation that gives the best correlation of ordered and disordered regions of both structures. The obtained threshold of 3.2 Å was applied to proteins whose structures were only determined by NMR, and the resulting IDRs were analyzed and compared to those of X-ray structures with no NMR counterpart in terms of sequence length, IDR fraction, protein function, cellular location, and amino acid composition, all of which suggest distinct characteristics. The structural knowledge of IDPs is still inadequate compared with that of structured proteins. Our method can collect and utilize IDRs from structures determined by NMR, potentially enhancing the understanding of IDPs.
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