Protein Shape Retrieval

Florent Langenfeld, Apostolos Axenopoulos, Anargyros Chatzitofis, Daniela Craciun, Petros Daras, Bowen Du, Andrea Giachetti, Yu-Kun Lai, Haisheng Li, Yingbin Li, Majid Masoumi, Yuxu Peng, Paul L. Rosin, Jeremy Sirugue, Li Sun, Spyridon Thermos, Matthew Toews, Yang Wei, Yujuan Wu, Yujia Zhai et al. (3 additional authors not shown)

Abstract: Proteins are macromolecules central to biological processes that display a dynamic and complex surface. They display multiple 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.