Issue No.05 - Sept.-Oct. (2012 vol.32)
pp: 62-69
Sara Su , Google
Connor Gramazio , Brown University
Daniela Extrum-Fernandez , Royal Veterinary College
Caitlin Crumm , University of Texas Southwestern Medical School
Lenore J. Cowen , Tufts University
Matt Menke , Google Cambridge
Megan Strait , Tufts University
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.
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
Sara Su, Connor Gramazio, Daniela Extrum-Fernandez, Caitlin Crumm, Lenore J. Cowen, Matt Menke, Megan 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
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