2018 IEEE Scientific Visualization Conference (SciVis) / Institute of Electrical and Electronics Engineers (IEEE).

Format:

Book

Author/Creator:

Institute of Electrical and Electronics Engineers (IEEE), author, issuing body.

Language:

English

Subjects (All):

Computer graphics--Congresses.

Computer graphics.

Information visualization--Congresses.

Information visualization.

Physical Description:

1 online resource : illustrations

Other Title:

2018 IEEE Scientific Visualization Conference

Place of Publication:

Piscataway, New Jersey : Institute of Electrical and Electronics Engineers (IEEE), 2018.

Summary:

Molecular visualization of molecular dynamics data commonly uses representative surfaces of varying resolution to depict protein molecules while a variety of geometric shapes, from lines to spheres, are often used to represent atoms and inter-atomic bonds. In general, the aim of molecular visualization is focused on efficiently rendering atoms or molecules themselves, while the interaction space between them is less explored. Furthermore, with naive approaches rendering every molecule, the particles overlap so that significant interaction details are obscured due to clutter. Contrary to common approaches, our work focuses on the interaction between lipids and proteins and the area in which this occurs, the - lipid - membrane. To do so, we introduce a novel abstract interaction space for Protein-Lipid Interaction (PLI). The cylindrical abstraction simplifies perception and computation of PLI. It does so by using a local projection of PLI onto a cylindrical geometry. This also addresses the challenge of visualizing complex, time-dependent interactions between these molecules. We propose a fast GPU-based implementation that maps lipid-constituents involved in the interaction onto the abstract protein interaction space. The tool provides interactive rendering of PLI for 336,260 particles over almost 2,000 time-steps. The result is a great simplification of this complex Protein-Lipid Interaction leading to both better perception and new insight for computational biologists.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9781538668825

1538668823