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Bioinformatics Advance Access published online on September 18, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl481
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received July 23, 2006
Revised September 8, 2006
Accepted September 11, 2006

Article

Hierarchical and multi-resolution representation of protein flexibility

Yong Zhao 1, Daniel Stoffler 2, and Michel Sanner 1 *

1 Department of Molecular Biology, TPC26, The Scripps Research Institute, La Jolla, CA, USA
2 F. Hoffmann-La Roche Ltd, Pharmaceuticals Division, CH-4070 Basel, Switzerland


  Abstract

Motivation: Conformational rearrangements during molecular interactions are observed in a wide range of biological systems. However, computational methods aiming at simulating and predicting molecular interactions are still largely ignoring the flexible nature of biological macromolecules as the number of degrees of freedom is computationally intractable when using brute force representations.

Results: In this paper we present a computational data structure called the Flexibility Tree (FT) that enables a multi-resolution and hierarchical encoding of molecular flexibility. This tree-like data structure allows the encoding of relatively small, yet complex subspaces of a protein's conformational space. These conformational sub-spaces are parameterized by a small number of variables and can be searched efficiently using standard global search techniques. The FT structure makes it straightforward to combine and nest a wide variety of motion types such as hinge, shear, twist, screw, rotameric side chains, normal modes, essential dynamics, etc. Moreover, the ability to assign shapes to the nodes in a FT allows the interactive manipulation of flexible protein shapes and the interactive visualization of the impact of conformational changes on the protein's overall shape. We describe the design of the flexibility tree and illustrate the construction of such trees to hierarchically combine motion information obtained from a variety of sources ranging from experiment to user intuition, and describing conformational changes at different biological scales. We show that the combination of various types of motion helps refine the encoded conformational subspaces to include experimentally determined structures, and we demonstrate searching these subspaces for specific conformations.

Associate Editor: Alex Bateman
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