Published April 18, 2018
| Version v1
Journal article
Open
SHREC 2018 – Protein Shape Retrieval
Authors/Creators
- Langenfeld, Florent1
- Axenopoulos, Apostolos2
- Chatzitofis, Anargyros2
- Craciun, Daniela1
- Daras, Petros2
- Du, Bowen3
- Giachetti, Andrea4
- Lai, Yu-kun5
- Li, Haisheng3
- Li, Yingbin3
- Masoumi, Majid6
- Peng, Yuxu5
- Rosin, Paul5
- Sirugue, Jeremy1
- Sun, Li3
- Thermos, Spyridon2
- Toews, Matthew6
- Wei, Yang3
- Wu, Yujuan3
- Zhai, Yujia3
- Zhao, Tianyu3
- Zheng, Yanping3
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Montes, Matthieu1
- 1. Laboratoire GBA, EA4627, CNAM
- 2. Information Technologies Institute, Centre for Research and Technology
- 3. School of Computer Science and Information Engineering, Beijing Technology and Business University
- 4. Department of Computer Science, Università di Verona
- 5. School of Computer Science and Informatics, Cardiff University
- 6. École de Téchnologie Supérieure, University of Québec
Description
Proteins are macromolecules central to biological processes that display a dynamic and complex surface. They display mul- tiple conformations differing by local (residue side-chain) or global (loop or domain) structural changes which can impact drastically their global and local shape. Since the structure of proteins is linked to their function and the disruption of their interactions can lead to a disease state, it is of major importance to characterize their shape. In the present work, we report the performance in enrichment of six shape-retrieval methods (3D-FusionNet, GSGW, HAPT, DEM, SIWKS and WKS) on a 2 267 protein structures dataset generated for this protein shape retrieval track of SHREC’18.
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