Journal Article

DTA: dihedral transition analysis for characterization of the effects of large main-chain dihedral changes in proteins

Wataru Nishima, Guoying Qi, Steven Hayward and Akio Kitao

in Bioinformatics

Volume 25, issue 5, pages 628-635
Published in print March 2009 | ISSN: 1367-4803
Published online January 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp032
DTA: dihedral transition analysis for characterization of the effects of large main-chain dihedral changes in proteins

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Motivation: The biological function of proteins is associated with a variety of motions, ranging from global domain motion to local motion of side chain. We propose a method, dihedral transition analysis (DTA), to identify significant dihedral angle changes between two distinct protein conformations and for characterization of the effect of these transitions on both local and global conformation.

Results: Applying DTA to a comprehensive and non-redundant dataset of 459 high-resolution pairs of protein structures, we found that a dihedral transition occurs in 82% of proteins. Multiple dihedral transitions are shown to occur cooperatively along the sequence, which allows us to separate a polypeptide chain into fragments with and without transitions, namely transition fragments (TFs) and stable fragments (SFs), respectively. By characterizing the magnitude of TF conformational change and the effect of the transition on the neighboring fragments, flap and hinge motions are identified as typical motions. DTA is also useful to detect protein motions, subtle in RMSD but significant in terms of dihedral angle changes, such as the peptide-plane flip, the side-chain flip and path-preserving motions. We conclude that DTA is a useful tool to extract potential functional motions, some of which might have been missed using conventional methods for protein motion analysis.

Availability: http://dynamics.iam.u-tokyo.ac.jp/DTA/

Contact: kitao@iam.u-tokyo.ac.jp

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  5410 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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