Browsing by Author "Hammoudi, Karim"

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    Protein Shape Retrieval Contest
    (The Eurographics Association, 2019) Langenfeld, Florent; Axenopoulos, Apostolos; Benhabiles, Halim; Daras, Petros; Giachetti, Andrea; Han, Xusi; Hammoudi, Karim; Kihara, Daisuke; Lai, Tuan M.; Liu, Haiguang; Melkemi, Mahmoud; Mylonas, Stelios K.; Terashi, Genki; Wang, Yufan; Windal, Feryal; Montes, Matthieu; Biasotti, Silvia and Lavoué, Guillaume and Veltkamp, Remco
    This track aimed at retrieving protein evolutionary classification based on their surfaces meshes only. Given that proteins are dynamic, non-rigid objects and that evolution tends to conserve patterns related to their activity and function, this track offers a challenging issue using biologically relevant molecules. We evaluated the performance of 5 different algorithms and analyzed their ability, over a dataset of 5,298 objects, to retrieve various conformations of identical proteins and various conformations of ortholog proteins (proteins from different organisms and showing the same activity). All methods were able to retrieve a member of the same class as the query in at least 94% of the cases when considering the first match, but show more divergent when more matches were considered. Last, similarity metrics trained on databases dedicated to proteins improved the results.
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    SHREC 2021: Surface-based Protein Domains Retrieval
    (The Eurographics Association, 2021) Langenfeld, Florent; Aderinwale, Tunde; Christoffer, Charles; Shin, Woong-Hee; Terashi, Genki; Wang, Xiao; Kihara, Daisuke; Benhabiles, Halim; Hammoudi, Karim; Cabani, Adnane; Windal, Feryal; Melkemi, Mahmoud; Otu, Ekpo; Zwiggelaar, Reyer; Hunter, David; Liu, Yonghuai; Sirugue, Léa; Nguyen, Huu-Nghia H.; Nguyen, Tuan-Duy H.; Nguyen–Truong, Vinh-Thuyen; Le, Danh; Nguyen, Hai-Dang; Tran, Minh-Triet; Montès, Matthieu; Biasotti, Silvia and Dyke, Roberto M. and Lai, Yukun and Rosin, Paul L. and Veltkamp, Remco C.
    Proteins are essential to nearly all cellular mechanism, and often interact through their surface with other cell molecules, such as proteins and ligands. The evolution generates plenty of different proteins, with unique abilities, but also proteins with related functions hence surface, which is therefore of primary importance for their activity. In the present work, we assess the ability of five methods to retrieve similar protein surfaces, using either their shape only (3D meshes), or their shape and the electrostatic potential at their surface, an important surface property. Five different groups participated in this challenge using the shape only, and one group extended its pre-existing algorithm to handle the electrostatic potential. The results reveal both the ability of the methods to detect related proteins and their difficulties to distinguish between topologically related proteins.