Topology representing neural networks reconcile biomolecular shape, structure, and dynamics
Identifieur interne : 001117 ( PascalFrancis/Corpus ); précédent : 001116; suivant : 001118Topology representing neural networks reconcile biomolecular shape, structure, and dynamics
Auteurs : W. Wriggers ; P. Chacon ; J. A. Kovacs ; F. Tama ; S. BirmannsSource :
- Neurocomputing [ 0925-2312 ] ; 2004.
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- Pascal (Inist)
English descriptors
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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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| NO : | PASCAL 04-0084062 EI |
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| ET : | Topology representing neural networks reconcile biomolecular shape, structure, and dynamics |
| AU : | WRIGGERS (W.); CHACON (P.); KOVACS (J. A.); TAMA (F.); BIRMANNS (S.) |
| AF : | School of Health Information Sci. University of Texas - Houston/Houston, TX 77030/Etats-Unis (1 aut.) |
| DT : | Publication en série; Niveau analytique |
| SO : | Neurocomputing; ISSN 0925-2312; Coden NRCGEO; Pays-Bas; Da. 2004; Vol. 56; No. 1-4; Pp. 365-379; Bibl. 47 Refs. |
| LA : | Anglais |
| EA : | 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. |
| CC : | 002B01; 001A02B; 001B00C40; 001C01 |
| FD : | Théorie; Topologie; Degré liberté; Dynamique moléculaire; Stéréochimie; Modèle mathématique; Réseau neuronal |
| ED : | Topology-representing networks (TRN); Theory; Topology; Degrees of freedom (mechanics); Molecular dynamics; Stereochemistry; Mathematical models; Neural networks |
| LO : | INIST-XXXX |
| ID : | 04-0084062 |
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Pascal:04-0084062Le document en format XML
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