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18 results on '"Tunde Aderinwale"'

1. Real-time structure search and structure classification for AlphaFold protein models

Author

Tunde Aderinwale, Vijay Bharadwaj, Charles Christoffer, Genki Terashi, Zicong Zhang, Rashidedin Jahandideh, Yuki Kagaya, and Daisuke Kihara

Subjects
Biology (General), QH301-705.5
Abstract

3D-AF-Surfer is presented as a computational resource for real-time protein structure comparison search between AlphaFold2 models and PDB entries within seconds to a few minutes.

Published
2022
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2. RL-MLZerD: Multimeric protein docking using reinforcement learning

Author

Tunde Aderinwale, Charles Christoffer, and Daisuke Kihara

Subjects
protein docking, multiple protein docking, reinforcement learning, docking order prediction, protein bioinformatics, Biology (General), QH301-705.5
Abstract

Numerous biological processes in a cell are carried out by protein complexes. To understand the molecular mechanisms of such processes, it is crucial to know the quaternary structures of the complexes. Although the structures of protein complexes have been determined by biophysical experiments at a rapid pace, there are still many important complex structures that are yet to be determined. To supplement experimental structure determination of complexes, many computational protein docking methods have been developed; however, most of these docking methods are designed only for docking with two chains. Here, we introduce a novel method, RL-MLZerD, which builds multiple protein complexes using reinforcement learning (RL). In RL-MLZerD a multi-chain assembly process is considered as a series of episodes of selecting and integrating pre-computed pairwise docking models in a RL framework. RL is effective in correctly selecting plausible pairwise models that fit well with other subunits in a complex. When tested on a benchmark dataset of protein complexes with three to five chains, RL-MLZerD showed better modeling performance than other existing multiple docking methods under different evaluation criteria, except against AlphaFold-Multimer in unbound docking. Also, it emerged that the docking order of multi-chain complexes can be naturally predicted by examining preferred paths of episodes in the RL computation.

Published
2022
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3. SHREC 2021: Surface-based Protein Domains Retrieval.

Author

Florent Langenfeld, Tunde Aderinwale, Charles Christoffer, Woong-Hee Shin, Genki Terashi, Xiao Wang 0004, Daisuke Kihara, Halim Benhabiles, Karim Hammoudi, Adnane Cabani, Féryal Windal, Mahmoud Melkemi, Ekpo Otu, Reyer Zwiggelaar, David Hunter, Yonghuai Liu, Léa Sirugue, Huu-Nghia H. Nguyen, Tuan-Duy H. Nguyen, Vinh-Thuyen Nguyen-Truong, Danh Le, Hai-Dang Nguyen, Minh-Triet Tran, and Matthieu Montès

Published
2021
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5. SHREC 2021: Retrieval and classification of protein surfaces equipped with physical and chemical properties.

Author

Andrea Raffo, Ulderico Fugacci, Silvia Biasotti, Walter Rocchia, Yonghuai Liu, Ekpo Otu, Reyer Zwiggelaar, David Hunter, Evangelia I. Zacharaki, Eleftheria Psatha, Dimitrios Laskos, Gerasimos Arvanitis, Konstantinos Moustakas, Tunde Aderinwale, Charles Christoffer, Woong-Hee Shin, Daisuke Kihara, Andrea Giachetti 0001, Huu-Nghia Nguyen, Tuan-Duy Nguyen, Vinh-Thuyen Nguyen-Truong, Danh Le-Thanh, Hai-Dang Nguyen, and Minh-Triet Tran

Published
2021
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6. SHREC 2020: Multi-domain protein shape retrieval challenge.

Author

Florent Langenfeld, Yuxu Peng, Yu-Kun Lai, Paul L. Rosin, Tunde Aderinwale, Genki Terashi, Charles Christoffer, Daisuke Kihara, Halim Benhabiles, Karim Hammoudi, Adnane Cabani, Féryal Windal, Mahmoud Melkemi, Andrea Giachetti 0001, Stelios K. Mylonas, Apostolos Axenopoulos, Petros Daras, Ekpo Otu, and Matthieu Montès

Published
2020
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7. Detecting protein and DNA/RNA structures in cryo-EM maps of intermediate resolution using deep learning

Author

Tunde Aderinwale, Xiao Wang, Eman Alnabati, Daisuke Kihara, Sai Raghavendra Maddhuri Venkata Subramaniya, and Genki Terashi

Subjects
0301 basic medicine, Models, Molecular, Cryo-electron microscopy, Science, Biophysics, General Physics and Astronomy, computer.software_genre, 01 natural sciences, Convolutional neural network, General Biochemistry, Genetics and Molecular Biology, Article, Protein Structure, Secondary, 03 medical and health sciences, Computational biophysics, Protein structure, Deep Learning, Voxel, Cryoelectron microscopy, Protein secondary structure, Physics, Multidisciplinary, 010405 organic chemistry, business.industry, Deep learning, Resolution (electron density), RNA, Computational Biology, General Chemistry, DNA, 0104 chemical sciences, 030104 developmental biology, Nucleic Acid Conformation, Artificial intelligence, Biological system, business, computer, Software, Macromolecule
Abstract

An increasing number of density maps of macromolecular structures, including proteins and DNA/RNA complexes, have been determined by cryo-electron microscopy (cryo-EM). Although lately maps at a near-atomic resolution are routinely reported, there are still substantial fractions of maps determined at intermediate or low resolutions, where extracting structure information is not trivial. Here, we report a new computational method, Emap2sec+, which identifies DNA or RNA as well as the secondary structures of proteins in cryo-EM maps of 5 to 10 Å resolution. Emap2sec+ employs the deep Residual convolutional neural network. Emap2sec+ assigns structural labels with associated probabilities at each voxel in a cryo-EM map, which will help structure modeling in an EM map. Emap2sec+ showed stable and high assignment accuracy for nucleotides in low resolution maps and improved performance for protein secondary structure assignments than its earlier version when tested on simulated and experimental maps., It is challenging to extract structural information from EM density maps at intermediate or low resolutions. Here, the authors present Emap2sec+, a program for detecting nucleotides and protein secondary structures in EM density maps at 5 to 10 Å resolution.

Published
2021

8. Surface-based protein domains retrieval methods from a SHREC2021 challenge

Author

Florent Langenfeld, Tunde Aderinwale, Charles Christoffer, Woong-Hee Shin, Genki Terashi, Xiao Wang, Daisuke Kihara, Halim Benhabiles, Karim Hammoudi, Adnane Cabani, Feryal Windal, Mahmoud Melkemi, Ekpo Otu, Reyer Zwiggelaar, David Hunter, Yonghuai Liu, Léa Sirugue, Huu-Nghia H. Nguyen, Tuan-Duy H. Nguyen, Vinh-Thuyen Nguyen-Truong, Danh Le, Hai-Dang Nguyen, Minh-Triet Tran, Matthieu Montès, Laboratoire Génomique, bioinformatique et chimie moléculaire (GBCM), Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM), Department of Computer Science [Purdue], Purdue University [West Lafayette], Suncheon National University [Suncheon, Corée du Sud], Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), Bio-Micro-Electro-Mechanical Systems - IEMN (BIOMEMS - IEMN), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), JUNIA (JUNIA), Université catholique de Lille (UCL), Institut de Recherche en Informatique Mathématiques Automatique Signal - IRIMAS - UR 7499 (IRIMAS), Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA)), Université de Strasbourg (UNISTRA), École Supérieure d’Ingénieurs en Génie Électrique (ESIGELEC), Aberystwyth University, Edge Hill University, Vietnam National University - Ho Chi Minh City (VNU-HCM), and Léa Sirugue, Matthieu Montès and Florent Langenfeld are supported by the European Research Council Executive Agency under the research grant number 640,283. Daisuke Kihara acknowledges supports from the National Institutes of Health (R01GM133840, R01GM123055) and the National Science Foundation (DBI2003635, CMMI1825941, and MCB1925643). Charles Christoffer is supported by NIGMS-funded pre–doctoral fellowship (T32 GM132024). Huu-Nghia H. Nguyen, Tuan-Duy H. Nguyen, Vinh-Thuyen Nguyen-Truong, Danh Le, Hai-Dang Nguyen, and Minh-Triet Tran are supported by National University Ho Chi Minh City (VNU-HCM) (DS2020-42-01).

Subjects
Models, Molecular, Static Electricity, Proteins, Ligands, Computer Graphics and Computer-Aided Design, Article, Proteins surface, [SPI]Engineering Sciences [physics], SHREC2021, Protein Domains, Materials Chemistry, Physical and Theoretical Chemistry, Spectroscopy, 2000 MSC: 92-08
Abstract

publication dans une revue suite à la communication hal-03467479 (SHREC 2021: surface-based protein domains retrieval); International audience; Proteins are essential to nearly all cellular mechanism and the effectors of the cells activities. As such, they often interact through their surface with other proteins or other cellular ligands such as ions or organic molecules. The evolution generates plenty of different proteins, with unique abilities, but also proteins with related functions hence similar 3D surface properties (shape, physico-chemical properties, …). The protein surfaces are therefore of primary importance for their activity. In the present work, we assess the ability of different methods to detect such similarities based on the geometry of the protein surfaces (described as 3D meshes), using either their shape only, or their shape and the electrostatic potential (a biologically relevant property of proteins surface). Five different groups participated in this contest using the shape-only dataset, and one group extended its pre-existing method to handle the electrostatic potential. Our comparative study reveals both the ability of the methods to detect related proteins and their difficulties to distinguish between highly related proteins. Our study allows also to analyze the putative influence of electrostatic information in addition to the one of protein shapes alone. Finally, the discussion permits to expose the results with respect to ones obtained in the previous contests for the extended method. The source codes of each presented method have been made available online.

Published
2022
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9. Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment

Author

Xiaoqin Zou, Théo Mauri, Hang Shi, Shaowen Zhu, Justas Dapkūnas, Yuanfei Sun, Didier Barradas-Bautista, Raphael A. G. Chaleil, Ragul Gowthaman, Sohee Kwon, Xianjin Xu, Zuzana Jandova, Genki Terashi, Ryota Ashizawa, Petras J. Kundrotas, Shuang Zhang, Tunde Aderinwale, Jian Liu, Sandor Vajda, Paul A. Bates, Jianlin Cheng, Daisuke Kihara, Luis A. Rodríguez-Lumbreras, Carlos A. Del Carpio Muñoz, Liming Qiu, Guillaume Brysbaert, Jorge Roel-Touris, Česlovas Venclovas, Tereza Clarence, Rui Yin, Amar Singh, Patryk A. Wesołowski, Rafał Ślusarz, Adam Liwo, Guangbo Yang, Agnieszka S. Karczyńska, Yoshiki Harada, Sergei Kotelnikov, Yuya Hanazono, Charlotte W. van Noort, Marc F. Lensink, Jonghun Won, Adam K. Sieradzan, Israel Desta, Xufeng Lu, Charles Christoffer, Anna Antoniak, Taeyong Park, Sheng-You Huang, Tsukasa Nakamura, Brian G. Pierce, Usman Ghani, Yang Shen, Luigi Cavallo, Chaok Seok, Hao Li, Nurul Nadzirin, Ghazaleh Taherzadeh, Jacob Verburgt, Rodrigo V. Honorato, Artur Giełdoń, Jeffrey J. Gray, Dima Kozakov, Ming Liu, Shan Chang, Eiichiro Ichiishi, Manon Réau, Rui Duan, Francesco Ambrosetti, Johnathan D. Guest, Juan Fernández-Recio, Alexandre M. J. J. Bonvin, Ilya A. Vakser, Farhan Quadir, Yumeng Yan, Ren Kong, Sameer Velankar, Sergei Grudinin, Mateusz Kogut, Mikhail Ignatov, Yasuomi Kiyota, Hyeonuk Woo, Shoshana J. Wodak, Ameya Harmalkar, Shinpei Kobayashi, Panagiotis I. Koukos, Zhen Cao, Kliment Olechnovič, Cezary Czaplewski, Xiao Wang, Agnieszka G. Lipska, Kathryn A. Porter, Peicong Lin, Emilia A. Lubecka, Nasser Hashemi, Bin Liu, Mayuko Takeda-Shitaka, Karolina Zięba, Dzmitry Padhorny, Zhuyezi Sun, Daipayan Sarkar, Romina Oliva, Andrey Alekseenko, Siri Camee van Keulen, Mireia Rosell, Raj S. Roy, Brian Jiménez-García, Jinsol Yang, Martyna Maszota-Zieleniak, Cancer Research UK, Department of Energy and Climate Change (UK), European Commission, Institut National de Recherche en Informatique et en Automatique (France), Medical Research Council (UK), Japan Society for the Promotion of Science, Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), National Institute of General Medical Sciences (US), National Institutes of Health (US), National Natural Science Foundation of China, National Science Foundation (US), Unité de Glycobiologie Structurale et Fonctionnelle (UGSF), Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), European Bioinformatics Institute [Hinxton] (EMBL-EBI), EMBL Heidelberg, Biomolecular Modelling Laboratory [London], The Francis Crick Institute [London], Jiangsu University of Technology [Changzhou], Department of Electrical Engineering and Computer Science [Columbia] (EECS), University of Missouri [Columbia] (Mizzou), University of Missouri System-University of Missouri System, Institute for Data Science and Informatics [Columbia], University of Gdańsk (UG), Faculty of Electronics, Telecommunications and Informatics [GUT Gdańsk] (ETI), Gdańsk University of Technology (GUT), Medical University of Gdańsk, Graduate School of Medical Sciences [Nagoya], Nagoya City University [Nagoya, Japan], International University of Health and Welfare Hospital (IUHW Hospital), Department of Chemical and Biomolecular Engineering [Baltimore], Johns Hopkins University (JHU), Bijvoet Center of Biomolecular Research [Utrecht], Utrecht University [Utrecht], Stony Brook University [SUNY] (SBU), State University of New York (SUNY), Innopolis University, Boston University [Boston] (BU), Russian Academy of Sciences [Moscow] (RAS), Barcelona Supercomputing Center - Centro Nacional de Supercomputacion (BSC - CNS), Universidad de La Rioja (UR), Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Données, Apprentissage et Optimisation (DAO), Laboratoire Jean Kuntzmann (LJK), Université Grenoble Alpes (UGA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Huazhong University of Science and Technology [Wuhan] (HUST), Indiana University - Purdue University Indianapolis (IUPUI), Indiana University System, Graduate School of Information Sciences [Sendaï], Tohoku University [Sendai], National Institutes for Quantum and Radiological Science and Technology (QST), University of Maryland [Baltimore], King Abdullah University of Science and Technology (KAUST), University of Naples Federico II, Texas A&M University [Galveston], Seoul National University [Seoul] (SNU), Kitasato University, University of Kansas [Lawrence] (KU), Vilnius University [Vilnius], University of Missouri System, VIB-VUB Center for Structural Biology [Bruxelles], VIB [Belgium], Sub NMR Spectroscopy, Sub Overig UiLOTS, Sub Mathematics Education, NMR Spectroscopy, Université de Lille, CNRS, Unité de Glycobiologie Structurale et Fonctionnelle (UGSF) - UMR 8576, European Bioinformatics Institute [Hinxton] [EMBL-EBI], Department of Electrical Engineering and Computer Science [Columbia] [EECS], Faculty of Chemistry [Univ Gdańsk], Faculty of Electronics, Telecommunications and Informatics [GUT Gdańsk] [ETI], International University of Health and Welfare Hospital [IUHW Hospital], Johns Hopkins University [JHU], Stony Brook University [SUNY] [SBU], Department of Biomedical Engineering [Boston], Instituto de Ciencias de la Vid y el Vino [ICVV], Huazhong University of Science and Technology [Wuhan] [HUST], Indiana University - Purdue University Indianapolis [IUPUI], National Institutes for Quantum and Radiological Science and Technology [QST], King Abdullah University of Science and Technology [KAUST], Università degli Studi di Napoli 'Parthenope' = University of Naples [PARTHENOPE], Seoul National University [Seoul] [SNU], University of Kansas [Lawrence] [KU], University of Missouri [Columbia] [Mizzou], Unité de Glycobiologie Structurale et Fonctionnelle - UMR 8576 (UGSF), Université de Lille-Centre National de la Recherche Scientifique (CNRS), University of Naples Federico II = Università degli studi di Napoli Federico II, European Project: 675728,H2020,H2020-EINFRA-2015-1,BioExcel(2015), European Project: 823830,H2020-EU.1.4.1.3. Development, deployment and operation of ICT-based e-infrastructures, H2020-EU.1.4. EXCELLENT SCIENCE - Research Infrastructures ,BioExcel-2(2019), European Project: 777536,H2020-EU.1.4.1.3. Development, deployment and operation of ICT-based e-infrastructures, and H2020-EU.1.4. EXCELLENT SCIENCE - Research Infrastructures,EOSC-hub(2018)

Subjects
Models, Molecular, blind prediction, CAPRI, CASP, docking, oligomeric state, protein assemblies, protein complexes, protein docking, protein–protein interaction, template-based modeling, Computer science, [SDV]Life Sciences [q-bio], Machine learning, computer.software_genre, Biochemistry, Article, protein-protein interaction, 03 medical and health sciences, Sequence Analysis, Protein, Structural Biology, Server, Protein Interaction Domains and Motifs, Molecular Biology, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, 0303 health sciences, Binding Sites, business.industry, 030302 biochemistry & molecular biology, Computational Biology, Proteins, 3. Good health, Molecular Docking Simulation, Artificial intelligence, business, computer, Software
Abstract

We present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers). Eight were difficult targets for which only distantly related templates were found for the individual subunits. Twenty-five CAPRI groups including eight automatic servers submitted ~1250 models per target. Twenty groups including six servers participated in the CAPRI scoring challenge submitted ~190 models per target. The accuracy of the predicted models was evaluated using the classical CAPRI criteria. The prediction performance was measured by a weighted scoring scheme that takes into account the number of models of acceptable quality or higher submitted by each group as part of their five top-ranking models. Compared to the previous CASP-CAPRI challenge, top performing groups submitted such models for a larger fraction (70–75%) of the targets in this Round, but fewer of these models were of high accuracy. Scorer groups achieved stronger performance with more groups submitting correct models for 70–80% of the targets or achieving high accuracy predictions. Servers performed less well in general, except for the MDOCKPP and LZERD servers, who performed on par with human groups. In addition to these results, major advances in methodology are discussed, providing an informative overview of where the prediction of protein assemblies currently stands., Cancer Research UK, Grant/Award Number: FC001003; Changzhou Science and Technology Bureau, Grant/Award Number: CE20200503; Department of Energy and Climate Change, Grant/Award Numbers: DE-AR001213, DE-SC0020400, DE-SC0021303; H2020 European Institute of Innovation and Technology, Grant/Award Numbers: 675728, 777536, 823830; Institut national de recherche en informatique et en automatique (INRIA), Grant/Award Number: Cordi-S; Lietuvos Mokslo Taryba, Grant/Award Numbers: S-MIP-17-60, S-MIP-21-35; Medical Research Council, Grant/Award Number: FC001003; Japan Society for the Promotion of Science KAKENHI, Grant/Award Number: JP19J00950; Ministerio de Ciencia e Innovación, Grant/Award Number: PID2019-110167RB-I00; Narodowe Centrum Nauki, Grant/Award Numbers: UMO-2017/25/B/ST4/01026, UMO-2017/26/M/ST4/00044, UMO-2017/27/B/ST4/00926; National Institute of General Medical Sciences, Grant/Award Numbers: R21GM127952, R35GM118078, RM1135136, T32GM132024; National Institutes of Health, Grant/Award Numbers: R01GM074255, R01GM078221, R01GM093123, R01GM109980, R01GM133840, R01GN123055, R01HL142301, R35GM124952, R35GM136409; National Natural Science Foundation of China, Grant/Award Number: 81603152; National Science Foundation, Grant/Award Numbers: AF1645512, CCF1943008, CMMI1825941, DBI1759277, DBI1759934, DBI1917263, DBI20036350, IIS1763246, MCB1925643; NWO, Grant/Award Number: TOP-PUNT 718.015.001; Wellcome Trust, Grant/Award Number: FC001003

Published
2021
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10. Real-Time Structure Search and Structure Classification for AlphaFold Protein Models

Author

Tunde Aderinwale, Zicong Zhang, Rhashidedin Jahandideh, Genki Terashi, Daisuke Kihara, Yuki Kagaya, Charles Christoffer, and Vijay Bharadwaj

Subjects
Models, Molecular, Surface (mathematics), business.industry, Computer science, Zernike polynomials, Structure (category theory), Medicine (miscellaneous), Proteins, Pattern recognition, Protein structure prediction, General Biochemistry, Genetics and Molecular Biology, symbols.namesake, Software, Protein structure, symbols, Protein model, Neural Networks, Computer, Artificial intelligence, General Agricultural and Biological Sciences, business, Representation (mathematics)
Abstract

Last year saw a breakthrough in protein structure prediction, where the AlphaFold2 method showed a substantial improvement in the modeling accuracy. Following the software release of AlphaFold2, predicted structures by AlphaFold2 for proteins in 21 species were made publicly available via the AlphaFold Database. Here, to facilitate structural analysis and application of AlphaFold2 models, we provide the infrastructure, 3D-AF-Surfer, which allows real-time structure-based search for the AlphaFold2 models. In 3D-AF-Surfer, structures are represented with 3D Zernike descriptors (3DZD), which is a rotationally invariant, mathematical representation of 3D shapes. We developed a neural network that takes 3DZDs of proteins as input and retrieves proteins of the same fold more accurately than direct comparison of 3DZDs. Using 3D-AF-Surfer, we report structure classifications of AlphaFold2 models and discuss the correlation between confidence levels of AlphaFold2 models and intrinsic disordered regions.

Published
2021
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11. Author response for 'Prediction of protein assemblies, the next frontier: The CASP14‐CAPRI experiment'

Author

Yumeng Yan, Mateusz Kogut, Sohee Kwon, Israel Desta, Petras J. Kundrotas, Xiaoqin Zou, Xiao Wang, Dima Kozakov, Eiichiro Ichiishi, Kathryn A. Porter, Johnathan D. Guest, Brian G. Pierce, Daisuke Kihara, Česlovas Venclovas, Agnieszka G. Lipska, Luigi Cavallo, Panagiotis I. Koukos, Yang Shen, Ren Kong, Brian Jiménez-García, Kliment Olechnovič, Cezary Czaplewski, Peicong Lin, Sameer Velankar, Shoshana J. Wodak, Agnieszka S. Karczyńska, Emilia A. Lubecka, Mikhail Ignatov, Shan Chang, Daipayan Sarkar, Sheng-You Huang, Chaok Seok, Nurul Nadzirin, Hao Li, Anna Antoniak, Manon Réau, Hyeonuk Woo, Siri Camee van Keulen, Ryota Ashizawa, Nasser Hashemi, Adam Liwo, Zhen Cao, Yoshiki Harada, Genki Terashi, Ameya Harmalkar, Farhan Quadir, Shinpei Kobayashi, Sandor Vajda, Zuzana Jandova, Juan Fernández-Recio, Amar Singh, Martyna Maszota-Zieleniak, Rodrigo V. Honorato, Usman Ghani, Sergei Grudinin, Xufeng Lu, Jorge Roel-Touris, Ming Liu, Paul A. Bates, Ghazaleh Taherzadeh, Adam K. Sieradzan, Patryk A. Wesołowski, Théo Mauri, Ilya A. Vakser, Francesco Ambrosetti, Jinsol Yang, Sergei Kotelnikov, Hang Shi, Shuang Zhang, Marc F. Lensink, Justas Dapkūnas, Yasuomi Kiyota, Taeyong Park, Mayuko Takeda-Shitaka, Andrey Alekseenko, Jian Liu, Artur Giełdoń, Ragul Gowthaman, Jonghun Won, Tsukasa Nakamura, Tunde Aderinwale, Yuanfei Sun, Guillaume Brysbaert, Jeffrey J. Gray, Luis A. Rodríguez-Lumbreras, Yuya Hanazono, Charlotte W. van Noort, Carlos A. Del Carpio Muñoz, Rui Duan, Alexandre M. J. J. Bonvin, Jianlin Cheng, Liming Qiu, Tereza Clarence, Rui Yin, Guangbo Yang, Shaowen Zhu, Didier Barradas-Bautista, Rafał Ślusarz, Raphael A. G. Chaleil, Charles Christoffer, Jacob Verburgt, Dzmitry Padhorny, Zhuyezi Sun, Romina Oliva, Mireia Rosell, Raj S. Roy, Bin Liu, and Karolina Zięba

Subjects
Frontier, Computer science, Econometrics
Published
2021
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12. Emap2sec+: Detecting Protein and DNA/RNA Structures in Cryo-EM Maps of Intermediate Resolution Using Deep Learning

Author

Tunde Aderinwale, Genki Terashi, S. R. Maddhuri Venkata Subramaniya, Daisuke Kihara, Eman Alnabati, and Xiao Wang

Subjects
Physics, Cryo-electron microscopy, Resolution (electron density), RNA, computer.software_genre, Convolutional neural network, chemistry.chemical_compound, chemistry, Voxel, Biological system, computer, Protein secondary structure, DNA, Macromolecule
Abstract

An increasing number of density maps of macromolecular structures, including proteins and protein and DNA/RNA complexes, have been determined by cryo-electron microscopy (cryo-EM). Although lately maps at a near-atomic resolution are routinely reported, there are still substantial fractions of maps determined at intermediate or low resolutions, where extracting structure information is not trivial. Here, we report a new computational method, Emap2sec+, which identifies DNA or RNA as well as the secondary structures of proteins in cryo-EM maps of 5 to 10 Å resolution. Emap2sec+ employs the deep Residual convolutional neural network. Emap2sec+ assigns structural labels with associated probabilities at each voxel in a cryo-EM map, which will help structure modeling in an EM map. Emap2sec+ showed stable and high assignment accuracy for nucleotides in low resolution maps and improved performance for protein secondary structure assignments than its earlier version when tested on simulated and experimental maps.

Published
2020
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13. Computational structure modeling for diverse categories of macromolecular interactions

Author

Daisuke Kihara, Tunde Aderinwale, Charles Christoffer, Daipayan Sarkar, and Eman Alnabati

Subjects
0303 health sciences, Computer science, Extramural, Macromolecular Substances, Computational Biology, Proteins, Computational biology, Article, Molecular Docking Simulation, 03 medical and health sciences, Structural bioinformatics, 0302 clinical medicine, Structural Biology, Docking (molecular), Macromolecular docking, Peptides, Molecular Biology, 030217 neurology & neurosurgery, Software, 030304 developmental biology, Protein Binding
Abstract

Computational protein-protein docking is one of the most intensively studied topics in structural bioinformatics. The field has made substantial progress through over three decades of development. The development began with methods for rigid-body docking of two proteins, which have now been extended in different directions to cover the various macromolecular interactions observed in a cell. Here, we overview the recent developments of the variations of docking methods, including multiple protein docking, peptide-protein docking, and disordered protein docking methods.

Published
2020

14. SHREC2020 track: Multi-domain protein shape retrieval challenge

Author

Florent Langenfeld, David Hunter, Matthieu Montes, Karim Hammoudi, Daisuke Kihara, Feryal Windal, Yu-Kun Lai, Ekpo Otu, Paul L. Rosin, Stelios K. Mylonas, Petros Daras, Apostolos Axenopoulos, Halim Benhabiles, Reyer Zwiggelaar, Andrea Giachetti, Charles Christoffer, Adnane Cabani, Tunde Aderinwale, Yuxu Peng, Yonghuai Liu, Mahmoud Melkemi, Genki Terashi, Cardiff Univ, Sch Chem, Cardiff CF10 3XQ, S Glam, Wales, Purdue University [West Lafayette], Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), Bio-Micro-Electro-Mechanical Systems - IEMN (BIOMEMS - IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), Institut de Recherche en Informatique Mathématiques Automatique Signal (IRIMAS), Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA)), Université de Strasbourg (UNISTRA), Institut de Recherche en Systèmes Electroniques Embarqués (IRSEEM), Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Normandie Université (NU)-École Supérieure d’Ingénieurs en Génie Électrique (ESIGELEC), Clinica Oculistica, Università degli Studi di Verona, Aberystwyth Univ, Inst Biol Environm & Rural Sci, Aberystwyth SY23 3EB, Dyfed, Wales, Young teachers growth plan project - Changsha University of Science Technology [2019QJCZ014], ATXN1-MED15 PPI project - GSRT Hellenic Foundation for Research and Innovation, European Research Council Executive Agency [640283], Laboratoire Génomique, bioinformatique et chimie moléculaire (GBCM), Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM), School of Information Science and Engineering [Changsha], Central South University [Changsha], School of Computer Sciences & Informatics [Cardiff], Cardiff University, Università degli studi di Verona = University of Verona (UNIVR), Centre for Research and Technology Hellas (CERTH), Aberystwyth University, and Edge Hill University

Subjects
Computer science, 3D shape analysis, 02 engineering and technology, Computational biology, Domain (software engineering), 3D shape descriptor, [SPI]Engineering Sciences [physics], Protein structure, Species level, 0202 electrical engineering, electronic engineering, information engineering, Protein structure comparison, Protein shape, business.industry, Specific function, 3D shape matching, General Engineering, 020207 software engineering, Modular design, Computer Graphics and Computer-Aided Design, Human-Computer Interaction, Multi domain, SHREC, 3D shape retrieval, 020201 artificial intelligence & image processing, business, Scope (computer science)
Abstract

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

Published
2020
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15. Performance and enhancement of the LZerD protein assembly pipeline in CAPRI 38-46

Author

Charles Christoffer, Daisuke Kihara, Jacob Verburgt, Lenna X. Peterson, Woong-Hee Shin, Genki Terashi, Sai Raghavendra Maddhuri Venkata Subramaniya, and Tunde Aderinwale

Subjects
Protein Conformation, alpha-Helical, Computer science, computer.software_genre, Ligands, Biochemistry, Article, 03 medical and health sciences, Critical Assessment of Prediction of Interactions, Structural Biology, Protein Interaction Mapping, Humans, Human group, Macromolecular docking, Protein Interaction Domains and Motifs, Amino Acid Sequence, Molecular Biology, 030304 developmental biology, 0303 health sciences, Binding Sites, 030302 biochemistry & molecular biology, Protein pair, Proteins, Protein structure prediction, Molecular Docking Simulation, Template, Docking (molecular), Research Design, Structural Homology, Protein, Protein Conformation, beta-Strand, Data mining, Peptides, computer, Algorithms, Software, Protein Binding
Abstract

We report the performance of the protein docking prediction pipeline of our group and the results for Critical Assessment of Prediction of Interactions (CAPRI) rounds 38-46. The pipeline integrates programs developed in our group as well as other existing scoring functions. The core of the pipeline is the LZerD protein-protein docking algorithm. If templates of the target complex are not found in PDB, the first step of our docking prediction pipeline is to run LZerD for a query protein pair. Meanwhile, in the case of human group prediction, we survey the literature to find information that can guide the modeling, such as protein-protein interface information. In addition to any literature information and binding residue prediction, generated docking decoys were selected by a rank aggregation of statistical scoring functions. The top 10 decoys were relaxed by a short molecular dynamics simulation before submission to remove atom clashes and improve side-chain conformations. In these CAPRI rounds, our group, particularly the LZerD server, showed robust performance. On the other hand, there are failed cases where some other groups were successful. To understand weaknesses of our pipeline, we analyzed sources of errors for failed targets. Since we noted that structure refinement is a step that needs improvement, we newly performed a comparative study of several refinement approaches. Finally, we show several examples that illustrate successful and unsuccessful cases by our group.

Published
2019

16. Emap2sec+: detecting protein and DNA/RNA structures in cryo-EM maps of intermediate resolution using deep learning

Author

Xiao Wang, Tunde Aderinwale, Genki Terashi, Eman Alnabati, Sai Raghavendra Maddhuri Venkata Subramaniya, and Daisuke Kihara

Subjects
Materials science, business.industry, Cryo-electron microscopy, Deep learning, Resolution (electron density), Condensed Matter Physics, Biochemistry, Inorganic Chemistry, Structural Biology, Biophysics, General Materials Science, Artificial intelligence, Physical and Theoretical Chemistry, business
Published
2021
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17. Community Assessment of the Predictability of Cancer Protein and Phosphoprotein Levels from Genomics and Transcriptomics

Author

Mi Yang, Francesca Petralia, Zhi Li, Hongyang Li, Weiping Ma, Xiaoyu Song, Sunkyu Kim, Heewon Lee, Han Yu, Bora Lee, Seohui Bae, Eunji Heo, Jan Kaczmarczyk, Piotr Stępniak, Michał Warchoł, Thomas Yu, Anna P. Calinawan, Paul C. Boutros, Samuel H. Payne, Boris Reva, Emily Boja, Henry Rodriguez, Gustavo Stolovitzky, Yuanfang Guan, Jaewoo Kang, Pei Wang, David Fenyö, Julio Saez-Rodriguez, Tunde Aderinwale, Ebrahim Afyounian, Piyush Agrawal, Mehreen Ali, Alicia Amadoz, Francisco Azuaje, John Bachman, Sherry Bhalla, José Carbonell-Caballero, Priyanka Chakraborty, Kumardeep Chaudhary, Yonghwa Choi, Yoonjung Choi, Cankut Çubuk, Sandeep Kumar Dhanda, Joaquín Dopazo, Laura L. Elo, Ábel Fóthi, Olivier Gevaert, Kirsi Granberg, Russell Greiner, Marta R. Hidalgo, Vivek Jayaswal, Hwisang Jeon, Minji Jeon, Sunil V. Kalmady, Yasuhiro Kambara, Keunsoo Kang, Tony Kaoma, Harpreet Kaur, Hilal Kazan, Devishi Kesar, Juha Kesseli, Daehan Kim, Keonwoo Kim, Sang-Yoon Kim, Sajal Kumar, Yunpeng Liu, Roland Luethy, Swapnil Mahajan, Mehrad Mahmoudian, Arnaud Muller, Petr V. Nazarov, Hien Nguyen, Matti Nykter, Shujiro Okuda, Sungsoo Park, Gajendra Pal Singh Raghava, Jagath C. Rajapakse, Tommi Rantapero, Hobin Ryu, Francisco Salavert, Sohrab Saraei, Ruby Sharma, Ari Siitonen, Artem Sokolov, Kartik Subramanian, Veronika Suni, Tomi Suomi, Léon-Charles Tranchevent, Salman Sadullah Usmani, Tommi Välikangas, Roberto Vega, and Hua Zhong

Subjects
Male, Proteomics, Histology, Genomics, Computational biology, Biology, Pathology and Forensic Medicine, Machine Learning, Transcriptome, 03 medical and health sciences, 0302 clinical medicine, Neoplasms, medicine, Humans, Gene, 030304 developmental biology, 0303 health sciences, Proteins, Cancer, Cell Biology, Phosphoproteins, Proteogenomics, medicine.disease, Phosphoprotein, Crowdsourcing, Phosphorylation, Female, 030217 neurology & neurosurgery
Abstract

Cancer is driven by genomic alterations, but the processes causing this disease are largely performed by proteins. However, proteins are harder and more expensive to measure than genes and transcripts. To catalyze developments of methods to infer protein levels from other omics measurements, we leveraged crowdsourcing via the NCI-CPTAC DREAM proteogenomic challenge. We asked for methods to predict protein and phosphorylation levels from genomic and transcriptomic data in cancer patients. The best performance was achieved by an ensemble of models, including as predictors transcript level of the corresponding genes, interaction between genes, conservation across tumor types, and phosphosite proximity for phosphorylation prediction. Proteins from metabolic pathways and complexes were the best and worst predicted, respectively. The performance of even the best-performing model was modest, suggesting that many proteins are strongly regulated through translational control and degradation. Our results set a reference for the limitations of computational inference in proteogenomics. A record of this paper's transparent peer review process is included in the Supplemental Information.

Published
2020
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18. Predicting clinical outcomes in neuroblastoma with genomic data integration

Author

Tunde Aderinwale, D Alp Emre Acar, Ilyes Baali, Saber HafezQorani, Hilal Kazan, Kazan, Hilal, 107780 [Kazan, Hilal], and 35094213400 [Kazan, Hilal]

Subjects
0301 basic medicine, Immunology, Cancer subtypes, Context (language use), Computational biology, Disease, Biology, computer.software_genre, General Biochemistry, Genetics and Molecular Biology, Cohort Studies, Neuroblastoma, 03 medical and health sciences, 0302 clinical medicine, Clinical endpoint, medicine, Humans, Cluster analysis, lcsh:QH301-705.5, Ecology, Evolution, Behavior and Systematics, Kernel k-means, Models, Statistical, Proportional hazards model, Research, Applied Mathematics, Nöroblastom, Genomics, Veri entegrasyonu, Prognosis, Kanser alt tipleri, medicine.disease, Progression-Free Survival, 3. Good health, 030104 developmental biology, lcsh:Biology (General), 030220 oncology & carcinogenesis, Modeling and Simulation, Cohort, Data integration, General Agricultural and Biological Sciences, computer
Abstract

Background Neuroblastoma is a heterogeneous disease with diverse clinical outcomes. Current risk group models require improvement as patients within the same risk group can still show variable prognosis. Recently collected genome-wide datasets provide opportunities to infer neuroblastoma subtypes in a more unified way. Within this context, data integration is critical as different molecular characteristics can contain complementary signals. To this end, we utilized the genomic datasets available for the SEQC cohort patients to develop supervised and unsupervised models that can predict disease prognosis. Results Our supervised model trained on the SEQC cohort can accurately predict overall survival and event-free survival profiles of patients in two independent cohorts. We also performed extensive experiments to assess the prediction accuracy of high risk patients and patients without MYCN amplification. Our results from this part suggest that clinical endpoints can be predicted accurately across multiple cohorts. To explore the data in an unsupervised manner, we used an integrative clustering strategy named multi-view kernel k-means (MVKKM) that can effectively integrate multiple high-dimensional datasets with varying weights. We observed that integrating different gene expression datasets results in a better patient stratification compared to using these datasets individually. Also, our identified subgroups provide a better Cox regression model fit compared to the existing risk group definitions. Conclusion Altogether, our results indicate that integration of multiple genomic characterizations enables the discovery of subtypes that improve over existing definitions of risk groups. Effective prediction of survival times will have a direct impact on choosing the right therapies for patients. Reviewers This article was reviewed by Susmita Datta, Wenzhong Xiao and Ziv Shkedy. Electronic supplementary material The online version of this article (10.1186/s13062-018-0223-8) contains supplementary material, which is available to authorized users.

Published
2018
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