SHREC 2020: multi-domain protein shape ...
Document type :
Article dans une revue scientifique
Title :
SHREC 2020: multi-domain protein shape retrieval challenge
Author(s) :
Langenfeld, Florent []
Laboratoire Génomique, bioinformatique et chimie moléculaire [GBCM]
Peng, Yuxu [Auteur]
School of Information Science and Engineering [Changsha]
Lai, Yu-Kun [Auteur]
School of Computer Sciences & Informatics [Cardiff]
Rosin, Paul L. [Auteur]
School of Computer Sciences & Informatics [Cardiff]
Aderinwale, Tunde [Auteur]
Purdue University [West Lafayette]
Terashi, Genki [Auteur]
Purdue University [West Lafayette]
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]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
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]
Centre for Research and Technology Hellas [CERTH]
Axenopoulos, Apostolos [Auteur]
Centre for Research and Technology Hellas [CERTH]
Daras, Petros [Auteur]
Centre for Research and Technology Hellas [CERTH]
Otu, Ekpo [Auteur]
Aberystwyth University
Zwiggelaar, Reyer [Auteur]
Aberystwyth University
Hunter, David [Auteur]
Aberystwyth University
Liu, Yonghuai [Auteur]
Edge Hill University
Montes, Matthieu [Auteur]
Laboratoire Génomique, bioinformatique et chimie moléculaire [GBCM]
Laboratoire Génomique, bioinformatique et chimie moléculaire [GBCM]
Peng, Yuxu [Auteur]
School of Information Science and Engineering [Changsha]
Lai, Yu-Kun [Auteur]
School of Computer Sciences & Informatics [Cardiff]
Rosin, Paul L. [Auteur]
School of Computer Sciences & Informatics [Cardiff]
Aderinwale, Tunde [Auteur]
Purdue University [West Lafayette]
Terashi, Genki [Auteur]
Purdue University [West Lafayette]
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]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
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]
Centre for Research and Technology Hellas [CERTH]
Axenopoulos, Apostolos [Auteur]
Centre for Research and Technology Hellas [CERTH]
Daras, Petros [Auteur]
Centre for Research and Technology Hellas [CERTH]
Otu, Ekpo [Auteur]
Aberystwyth University
Zwiggelaar, Reyer [Auteur]
Aberystwyth University
Hunter, David [Auteur]
Aberystwyth University
Liu, Yonghuai [Auteur]
Edge Hill University
Montes, Matthieu [Auteur]
Laboratoire Génomique, bioinformatique et chimie moléculaire [GBCM]
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
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 >
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
Source :
Files
- https://hal.archives-ouvertes.fr/hal-03321566/document
- Open access
- Access the document
- https://hal.archives-ouvertes.fr/hal-03321566/document
- Open access
- Access the document
- document
- Open access
- Access the document
- S0097849320301151.pdf
- Open access
- Access the document