Please use this identifier to cite or link to this item: https://doi.org/10.1155/2017/4818604
Title: Molecular Skin Surface-Based Transformation Visualization between Biological Macromolecules
Authors: Yan, K
Wang, B
Cheng, H 
Ji, Z
Huang, J
Gao, Z
Keywords: Binding sites
Computational efficiency
Macromolecules
Proteins
Biological macromolecule
Computational costs
Computational framework
Macromolecular dynamics
Molecular skin surface
Smooth surface modeling
Traditional approaches
Transformation process
Satellites
Article
bioinformatics
dynamics
macromolecule
molecular skin surface
skin surface
binding site
chemistry
computer simulation
functions of the skin and its appendages
human
macromolecule
molecular model
protein database
surface property
Binding Sites
Computer Simulation
Databases, Protein
Humans
Macromolecular Substances
Models, Molecular
Skin Physiological Phenomena
Surface Properties
Issue Date: 2017
Publisher: Hindawi Limited
Citation: Yan, K, Wang, B, Cheng, H, Ji, Z, Huang, J, Gao, Z (2017). Molecular Skin Surface-Based Transformation Visualization between Biological Macromolecules. Journal of Healthcare Engineering 2017 : 4818604. ScholarBank@NUS Repository. https://doi.org/10.1155/2017/4818604
Rights: Attribution 4.0 International
Abstract: Molecular skin surface (MSS), proposed by Edelsbrunner, is a C2 continuous smooth surface modeling approach of biological macromolecules. Compared to the traditional methods of molecular surface representations (e.g., the solvent exclusive surface), MSS has distinctive advantages including having no self-intersection and being decomposable and transformable. For further promoting MSS to the field of bioinformatics, transformation between different MSS representations mimicking the macromolecular dynamics is demanded. The transformation process helps biologists understand the macromolecular dynamics processes visually in the atomic level, which is important in studying the protein structures and binding sites for optimizing drug design. However, modeling the transformation between different MSSs suffers from high computational cost while the traditional approaches reconstruct every intermediate MSS from respective intermediate union of balls. In this study, we propose a novel computational framework named general MSS transformation framework (GMSSTF) between two MSSs without the assistance of union of balls. To evaluate the effectiveness of GMSSTF, we applied it on a popular public database PDB (Protein Data Bank) and compared the existing MSS algorithms with and without GMSSTF. The simulation results show that the proposed GMSSTF effectively improves the computational efficiency and is potentially useful for macromolecular dynamic simulations. © 2017 Ke Yan et al.
Source Title: Journal of Healthcare Engineering
URI: https://scholarbank.nus.edu.sg/handle/10635/179762
ISSN: 2040-2295
DOI: 10.1155/2017/4818604
Rights: Attribution 4.0 International
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