Langenfeld, FlorentAxenopoulos, ApostolosChatzitofis, AnargyrosCraciun, DanielaDaras, PetrosDu, BowenGiachetti, AndreaLai, Yu-kunLi, HaishengLi, YingbinMasoumi, MajidPeng, YuxuRosin, Paul L.Sirugue, JeremySun, LiThermos, SpyridonToews, MatthewWei, YangWu, YujuanZhai, YujiaZhao, TianyuZheng, YanpingMontes, MatthieuTelea, Alex and Theoharis, Theoharis and Veltkamp, Remco2018-04-142018-04-142018978-3-03868-053-61997-0471http://dx.doi.org/10.2312/3dor.20181053https://diglib.eg.org:443/handle/10.2312/3dor20181053Proteins 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'18.10.2312/3dor.2018105353-61