Browsing by Author "Kihara, Daisuke"

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    Classification in Cryo-Electron Tomograms
    (The Eurographics Association, 2019) Gubins, Ilja; Schot, Gijs van der; Veltkamp, Remco C.; Förster, Friedrich; Du, Xuefeng; Zeng, Xiangrui; Zhu, Zhenxi; Chang, Lufan; Xu, Min; Moebel, Emmanuel; Martinez-Sanchez, Antonio; Kervrann, Charles; Lai, Tuan M.; Han, Xusi; Terashi, Genki; Kihara, Daisuke; Himes, Benjamin A.; Wan, Xiaohua; Zhang, Jingrong; Gao, Shan; Hao, Yu; Lv, Zhilong; Wan, Xiaohua; Yang, Zhidong; Ding, Zijun; Cui, Xuefeng; Zhang, Fa; Biasotti, Silvia and Lavoué, Guillaume and Veltkamp, Remco
    Different imaging techniques allow us to study the organization of life at different scales. Cryo-electron tomography (cryo-ET) has the ability to three-dimensionally visualize the cellular architecture as well as the structural details of macro-molecular assemblies under near-native conditions. Due to beam sensitivity of biological samples, an inidividual tomogram has a maximal resolution of 5 nanometers. By averaging volumes, each depicting copies of the same type of a molecule, resolutions beyond 4 Å have been achieved. Key in this process is the ability to localize and classify the components of interest, which is challenging due to the low signal-to-noise ratio. Innovation in computational methods remains key to mine biological information from the tomograms. To promote such innovation, we organize this SHREC track and provide a simulated dataset with the goal of establishing a benchmark in localization and classification of biological particles in cryo-electron tomograms. The publicly available dataset contains ten reconstructed tomograms obtained from a simulated cell-like volume. Each volume contains twelve different types of proteins, varying in size and structure. Participants had access to 9 out of 10 of the cell-like ground-truth volumes for learning-based methods, and had to predict protein class and location in the test tomogram. Five groups submitted eight sets of results, using seven different methods. While our sample size gives only an anecdotal overview of current approaches in cryo-ET classification, we believe it shows trends and highlights interesting future work areas. The results show that learning-based approaches is the current trend in cryo-ET classification research and specifically end-to-end 3D learning-based approaches achieve the best performance.
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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: Classification in Cryo-electron Tomograms
    (The Eurographics Association, 2021) Gubins, Ilja; Chaillet, Marten L.; Schot, Gijs van der; Trueba, M. Cristina; Veltkamp, Remco C.; Förster, Friedrich; Wang, Xiao; Kihara, Daisuke; Moebel, Emmanuel; Nguyen, Nguyen P.; White, Tommi; Bunyak, Filiz; Papoulias, Giorgos; Gerolymatos, Stavros; Zacharaki, Evangelia I.; Moustakas, Konstantinos; Zeng, Xiangrui; Liu, Sinuo; Xu, Min; Wang, Yaoyu; Chen, Cheng; Cui, Xuefeng; Zhang, Fa; Biasotti, Silvia and Dyke, Roberto M. and Lai, Yukun and Rosin, Paul L. and Veltkamp, Remco C.
    Cryo-electron tomography (cryo-ET) is an imaging technique that allows three-dimensional visualization of macro-molecular assemblies under near-native conditions. Cryo-ET comes with a number of challenges, mainly low signal-to-noise and inability to obtain images from all angles. Computational methods are key to analyze cryo-electron tomograms. To promote innovation in computational methods, we generate a novel simulated dataset to benchmark different methods of localization and classification of biological macromolecules in tomograms. Our publicly available dataset contains ten tomographic reconstructions of simulated cell-like volumes. Each volume contains twelve different types of complexes, varying in size, function and structure. In this paper, we have evaluated seven different methods of finding and classifying proteins. Seven research groups present results obtained with learning-based methods and trained on the simulated dataset, as well as a baseline template matching (TM), a traditional method widely used in cryo-ET research. We show that learning-based approaches can achieve notably better localization and classification performance than TM. We also experimentally confirm that there is a negative relationship between particle size and performance for all methods.
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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.
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    SHREC2024: Non-rigid Complementary Shapes Retrieval in Protein-protein Interactions
    (The Eurographics Association, 2024) Yacoub, Taher; Zarubina, Nika; Depenveiller, Camille; Nguyen, Hoang-Phuc; Vong, Vinh-Toan; Tran, Minh-Triet; Kagaya, Yuki; Nakamura, Tsukasa; Kihara, Daisuke; Langenfeld, Florent; Montes, Matthieu; Biasotti, Silvia; Bustos, Benjamin; Schreck, Tobias; Sipiran, Ivan; Veltkamp, Remco C.
    The aim of this SHREC 2024 track is to compare different algorithms for retrieving non-rigid complementary shape pairs, applied in the context of 3D objects being more complex (e.g. with many folds and roughness) such as proteins. The dataset used for this benchmark is based on 52 selected protein-protein complexes for which an experimental structure is publicly available. One of the main difficulties of this challenge is the non-inclusion of the shapes derived from the ground truth conformations in the dataset. Different metrics were used to evaluate the retrieval performance (nearest-neighbor, first-tier, second-tier, and true positives) and to evaluate the quality of the predicted poses (TM-score, lDDT, ICS, IPS and DockQ - those metrics are classically used in the Critical Assessment of PRediction of Interactions challenges). Two teams took part in this challenge and were able to return the expected results. This paper discusses these results and prospects of retrieval methods based only on the protein shape information in the absence of atomic data, in a large context of protein-protein docking.