Topology representing neural networks reconcile biomolecular shape, structure, and dynamics
Identifieur interne : 000B91 ( PascalFrancis/Checkpoint ); précédent : 000B90; suivant : 000B92Topology representing neural networks reconcile biomolecular shape, structure, and dynamics
Auteurs : W. Wriggers [États-Unis] ; P. Chacon ; J. A. Kovacs ; F. Tama ; S. BirmannsSource :
- Neurocomputing [ 0925-2312 ] ; 2004.
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Abstract
Topology-representing networks (TRNs) generate reduced models of biomolecules and thereby facilitate the fitting of molecular fragments into large macromolecular complexes. The components of such complexes undergo a wide range of motions, and shapes observed at low resolution often deviate from the known atomic structures. What is required for the modeling of such motions is a combination of global shape constraints based on the low-resolution data with a local modeling of atomic interactions. We present a novel Motion Capture Network that freezes inessential degrees of freedom to maintain the stereochemistry of an atomic model. TRN-based deformable models retain much of the mechanical properties of biological macromolecules. The elastic models yield a decomposition of the predicted motion into vibrational normal modes and are amenable to interactive manipulation with haptic rendering software. © 2003 Elsevier B.V. All rights reserved.
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