Title :

SHREC 2020: multi-domain protein shape retrieval challenge

Author(s) :

Langenfeld, Florent []
Peng, Yuxu [Auteur]
Lai, Yu-Kun [Auteur]
Rosin, Paul L. [Auteur]
Aderinwale, Tunde [Auteur]
Purdue University [West Lafayette]
Terashi, Genki [Auteur]
Christoffer, Charles [Auteur]
Purdue University [West Lafayette]
Kihara, Daisuke [Auteur]
Purdue University [West Lafayette]
Benhabiles, Halim [Auteur]
Bio-Micro-Electro-Mechanical Systems - IEMN [BIOMEMS - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Hammoudi, Karim [Auteur]
Université de Strasbourg [UNISTRA]
Institut de Recherche en Informatique Mathématiques Automatique Signal [IRIMAS]
Cabani, Adnane [Auteur]
Institut de Recherche en Systèmes Electroniques Embarqués [IRSEEM]
Windal, Feryal [Auteur]
Melkemi, Mahmoud [Auteur]
Université de Strasbourg [UNISTRA]
Institut de Recherche en Informatique Mathématiques Automatique Signal [IRIMAS]
Giachetti, Andrea [Auteur]
Università degli studi di Verona = University of Verona [UNIVR]
Mylonas, Stelios [Auteur]
Axenopoulos, Apostolos [Auteur]
Daras, Petros [Auteur]
Otu, Ekpo [Auteur]
Zwiggelaar, Reyer [Auteur]
Hunter, David [Auteur]
Liu, Yonghuai [Auteur]
Montes, Matthieu [Auteur]

Journal title :

Computers and Graphics

Pages :

189-198

Publisher :

Elsevier

Publication date :

2020-10

ISSN :

0097-8493

English keyword(s) :

3D shape analysis
3D shape descriptor
3D shape retrieval
3D shape matching
Protein shape
SHREC

HAL domain(s) :

Sciences de l'ingénieur [physics]

English abstract : [en]

Proteins are natural modular objects usually composed of several domains, each domain bearing a specific function that is mediated through its surface, which is accessible to vicinal molecules. This draws attention to an ...
Show more >Proteins are natural modular objects usually composed of several domains, each domain bearing a specific function that is mediated through its surface, which is accessible to vicinal molecules. This draws attention to an understudied characteristic of protein structures: surface, that is mostly unexploited by protein structure comparison methods. In the present work, we evaluated the performance of six shape comparison methods, among which three are based on machine learning, to distinguish between 588 multi-domain proteins and to recreate the evolutionary relationships at the protein and species levels of the SCOPe database. The six groups that participated in the challenge submitted a total of 15 sets of results. We observed that the performance of all the methods significantly decreases at the species level, suggesting that shape-only protein comparison is challenging for closely related proteins. Even if the dataset is limited in size (only 588 proteins are considered whereas more than 160,000 protein structures are experimentally solved), we think that this work provides useful insights into the current shape comparison methods performance, and highlights possible limitations to large-scale applications due to the computational cost. (C) 2020 The Author(s). Published by Elsevier Ltd.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Comment :

[#17491] article suite à une conférence orale: 13th EG Euroworkshop on 3D object retrieval, 3DOR 2020, Graz, Austria, september 4-5, 2020

Collections :

• Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL