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Issue No.05 - Sept.-Oct. (2012 vol.32)
pp: 62-69
ABSTRACT
Many programs have been designed to view the 3D structures of protein molecules in 2D. However, three types of linked information haven't been previously defined in a systematic way that highlights the interface design challenge. Specifically, a scientist must have sequence, structure, and homology information in working memory to manipulate and understand a protein structure or related protein structures. Categorizing information types enables the application of classical interaction principles to the design of an intuitive interface for both expert and novice users. In a comparative user evaluation, their Molli system enhances the exploratory process of manipulating proteins of varying complexity by preserving the underlying data's linkages and relations.
INDEX TERMS
Proteins, Three dimensional displays, Solid modeling, Rendering (computer graphics), Data visualization, Biological system modeling,multiple structure alignment, Proteins, Three dimensional displays, Solid modeling, Rendering (computer graphics), Data visualization, Biological system modeling, homology, protein structure, protein visualization, coordinated views
CITATION
Sara Su, C. Gramazio, D. Extrum-Fernandez, C. Crumm, L. J. Cowen, M. Menke, M. Strait, "Molli: Interactive Visualization for Exploratory Protein Analysis", IEEE Computer Graphics and Applications, vol.32, no. 5, pp. 62-69, Sept.-Oct. 2012, doi:10.1109/MCG.2012.66
REFERENCES
1. W.L. Delano, The PyMOL Molecular Graphics System, 2002; http://pymol.sourceforge.net/overviewindex.htm .
2. N. Guex and M. Peitsch, “Swiss-Model and the Swiss-PdbViewer: An Environment for Comparative Protein Modeling,” Electrophoresis, vol. 18, no. 15, 1997, pp. 2714–2723.
3. A. Buja et al., “Interactive Data Visualization Using Focusing and Linking,” Proc. 2nd Conf. Visualization (VIS 91), IEEE CS, 1991, pp. 156–163.
4. R. Jianu, C. Demiralp, and D.H. Laidlaw, “Exploring Brain Connectivity with Two-Dimensional Neural Maps,” IEEE Trans. Visualization and Computer Graphics, vol. 18, no. 6, 2012, pp. 978–987.
5. S. Brooks and J.L. Whalley, “Multilayer Hybrid Visualizations to Support 3D GIS,” Computers, Environment and Urban Systems, vol. 32, no. 4, 2008, pp. 278–292.
6. S. O'Donoghue et al., “The SRS 3D Module: Integrating Structures, Sequences and Features,” Bioinformatics, vol. 20, no. 15, 2004, pp. 2476–2478.
7. M. Menke, B. Berger, and L. Cowen, “MATT: Local Flexibility Aids Protein Multiple Structure Alignment,” Public Library of Science Computational Biology, vol. 4, no. 1, 2008; http://dx.plos.org10.1371%2Fjournal.pcbi.0040010 .
8. T. Sando, M. Tory, and P. Irani, “Effects of Animation, User-Controlled Interactions, and Multiple Static Views in Understanding 3D Structures,” Proc. Applied Perception in Graphics and Visualization, ACM, 2009, pp. 69–76.
9. H. Berman et al., “The Protein Data Bank,” Nucleic Acids Research, vol. 28, no. 1, 2000, pp. 235–242.
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