Edvin Fuglebakk1, Sandhya P Tiwari2, Nathalie Reuter3. 1. Department of Molecular Biology, University of Bergen, Pb. 7803, N-5020 Bergen, Norway; Computational Biology Unit, Department of Informatics, University of Bergen, Pb. 7803, N-5020 Bergen, Norway. Electronic address: Edvin.Fuglebakk@mbi.uib.no. 2. Department of Molecular Biology, University of Bergen, Pb. 7803, N-5020 Bergen, Norway; Computational Biology Unit, Department of Informatics, University of Bergen, Pb. 7803, N-5020 Bergen, Norway. Electronic address: Sandhya.Tiwari@mbi.uib.no. 3. Department of Molecular Biology, University of Bergen, Pb. 7803, N-5020 Bergen, Norway; Computational Biology Unit, Department of Informatics, University of Bergen, Pb. 7803, N-5020 Bergen, Norway. Electronic address: nathalie.reuter@mbi.uib.no.
Abstract
BACKGROUND: Elastic network models (ENMs) are based on the simple idea that a protein can be described as a set of particles connected by springs, which can then be used to describe its intrinsic flexibility using, for example, normal mode analysis. Since the introduction of the first ENM by Monique Tirion in 1996, several variants using coarser protein models have been proposed and their reliability for the description of protein intrinsic dynamics has been widely demonstrated. Lately an increasing number of studies have focused on the meaning of slow dynamics for protein function and its potential conservation through evolution. This leads naturally to comparisons of the intrinsic dynamics of multiple protein structures with varying levels of similarity. SCOPE OF REVIEW: We describe computational strategies for calculating and comparing intrinsic dynamics of multiple proteins using elastic network models, as well as a selection of examples from the recent literature. MAJOR CONCLUSIONS: The increasing interest for comparing dynamics across protein structures with various levels of similarity, has led to the establishment and validation of reliable computational strategies using ENMs. Comparing dynamics has been shown to be a viable way for gaining greater understanding for the mechanisms employed by proteins for their function. Choices of ENM parameters, structure alignment or similarity measures will likely influence the interpretation of the comparative analysis of protein motion. GENERAL SIGNIFICANCE: Understanding the relation between protein function and dynamics is relevant to the fundamental understanding of protein structure-dynamics-function relationship. This article is part of a Special Issue entitled Recent developments of molecular dynamics.
BACKGROUND: Elastic network models (ENMs) are based on the simple idea that a protein can be described as a set of particles connected by springs, which can then be used to describe its intrinsic flexibility using, for example, normal mode analysis. Since the introduction of the first ENM by Monique Tirion in 1996, several variants using coarser protein models have been proposed and their reliability for the description of protein intrinsic dynamics has been widely demonstrated. Lately an increasing number of studies have focused on the meaning of slow dynamics for protein function and its potential conservation through evolution. This leads naturally to comparisons of the intrinsic dynamics of multiple protein structures with varying levels of similarity. SCOPE OF REVIEW: We describe computational strategies for calculating and comparing intrinsic dynamics of multiple proteins using elastic network models, as well as a selection of examples from the recent literature. MAJOR CONCLUSIONS: The increasing interest for comparing dynamics across protein structures with various levels of similarity, has led to the establishment and validation of reliable computational strategies using ENMs. Comparing dynamics has been shown to be a viable way for gaining greater understanding for the mechanisms employed by proteins for their function. Choices of ENM parameters, structure alignment or similarity measures will likely influence the interpretation of the comparative analysis of protein motion. GENERAL SIGNIFICANCE: Understanding the relation between protein function and dynamics is relevant to the fundamental understanding of protein structure-dynamics-function relationship. This article is part of a Special Issue entitled Recent developments of molecular dynamics.
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