SHREC 2020: Multi-domain protein shape retrieval challenge. (October 2020)

• Record Type: Journal Article
• Authors: Langenfeld, Florent; Peng, Yuxu; Lai, Yu-Kun; Rosin, Paul L.; Aderinwale, Tunde; Terashi, Genki; Christoffer, Charles; Kihara, Daisuke; Benhabiles, Halim; Hammoudi, Karim; Cabani, Adnane; Windal, Feryal; Melkemi, Mahmoud; Giachetti, Andrea; Mylonas, Stelios; Axenopoulos, Apostolos; Daras, Petros; Otu, Ekpo; Zwiggelaar, Reyer; Hunter, David; Liu, Yonghuai; Montès, Matthieu
• Is Part Of: Computers & graphics. Volume 91(2020)
• Page Start: 189
• Page End: 198
• Publication Date: 2020-10
• Subjects: 3D shape analysis -- 3D shape descriptor -- 3D shape retrieval -- 3D shape matching -- Protein shape -- SHREC; Computer graphics -- Periodicals; 006.6
• DOI: 10.1016/j.cag.2020.07.013
• Languages: English
• ISSNs: 0097-8493

Abstract

Highlights: Proteins are 3D molecular shapes of outmost importance in vivo. Methods to compare protein shape need to be evaluated on a benchmark dataset. This Shape Retrieval Contest aims to assess performances of shape comparison methods. The trade-off between performance and computational cost is evaluated. Graphical abstract: Abstract: 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.