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Proceedings Paper

GPU-accelerated visualization of protein dynamics in ribbon mode
Author(s): Manuel Wahle; Stefan Birmanns
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Paper Abstract

Proteins are biomolecules present in living organisms and essential for carrying out vital functions. Inherent to their functioning is folding into different spatial conformations, and to understand these processes, it is crucial to visually explore the structural changes. In recent years, significant advancements in experimental techniques and novel algorithms for post-processing of protein data have routinely revealed static and dynamic structures of increasing sizes. In turn, interactive visualization of the systems and their transitions became more challenging. Therefore, much research for the efficient display of protein dynamics has been done, with the focus being space filling models, but for the important class of abstract ribbon or cartoon representations, there exist only few methods for an efficient rendering. Yet, these models are of high interest to scientists, as they provide a compact and concise description of the structure elements along the protein main chain. In this work, a method was developed to speed up ribbon and cartoon visualizations. Separating two phases in the calculation of geometry allows to offload computational work from the CPU to the GPU. The first phase consists of computing a smooth curve along the protein's main chain on the CPU. In the second phase, conducted independently by the GPU, vertices along that curve are moved to set up the final geometrical representation of the molecule.

Paper Details

Date Published: 24 January 2011
PDF: 12 pages
Proc. SPIE 7868, Visualization and Data Analysis 2011, 786805 (24 January 2011); doi: 10.1117/12.872458
Show Author Affiliations
Manuel Wahle, The Univ. of Texas Health Science Ctr. at Houston (United States)
Stefan Birmanns, The Univ. of Texas Health Science Ctr. at Houston (United States)

Published in SPIE Proceedings Vol. 7868:
Visualization and Data Analysis 2011
Pak Chung Wong; Jinah Park; Ming C. Hao; Chaomei Chen; Katy Börner; David L. Kao; Jonathan C. Roberts, Editor(s)

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