Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment
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.
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dig-icvv-es-10261-262789 |
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España |
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ES |
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Biblioteca del ICVV España |
topic |
Blind prediction CAPRI CASP Docking Oligomeric state Protein assemblies Protein complexes Protein docking Protein–protein interaction Template-based modeling Blind prediction CAPRI CASP Docking Oligomeric state Protein assemblies Protein complexes Protein docking Protein–protein interaction Template-based modeling |
spellingShingle |
Blind prediction CAPRI CASP Docking Oligomeric state Protein assemblies Protein complexes Protein docking Protein–protein interaction Template-based modeling Blind prediction CAPRI CASP Docking Oligomeric state Protein assemblies Protein complexes Protein docking Protein–protein interaction Template-based modeling Lensink, Marc F. Brysbaert, Guillaume Mauri, Théo Nadzirin, Nurul Velankar, Sameer Chaleil, Raphaël A. G. Clarence, Tereza Bates, Paul A. Kong, Ren Liu, Bin Yang, Guangbo Liu, Ming Shi, Hang Lu, Xufeng Chang, Xang Roy, Raj S. Quadir, Farhan Liu, Jian Cheng, Jianlin Antoniak, Anna Czaplewski, Cezary Giełdón, Artur Kogut, Mateusz Lipska, Agnieszka Liwo, Adam Lubecka, Emilia Maszota-Zieleniak, Martyna Sieradzan, Adam K. Ślusarz, Rafał Wesołowski, Patryk A. Zięba, Karolina Carpio Muñoz, Carlos A. del Ichiishi, Eiichiro Harmalkar, Ameya Gray, Jeffrey J. Bonvin, Alexandre M. J. J. Ambrosetti, Francesco Vargas Honorato, Rodrigo Jandova, Zuzana Jiménez-García, Brian Koukos, Panagiotis I. Keulen, Siri van Noort, Charlotte W. van Réau, Manon Roel-Touris, Jorge Kotelnikov, Sergey Padhorny, Dzmitry Porter, Kathryn Alekseenko, Andrey Ignatov, Mikhail Desta, Israel Ashizawa, Ryota Sun, Zhuyezi Ghani, Usman Hashemi, Nasser Vajda, Sandor Kozakov, Dima Rosell, Mireia Rodríguez-Lumbreras, Luis A. Fernández-Recio, Juan Karczynska, Agnieszka Grudinin, Sergei Yan, Yumeng Li, Hao Lin, Peicong Huang, Sheng-You Christoffer, Charles Terashi, Genki Verburgt, Jacob Sarkar, Daipayan Aderinwale, Tunde Wang, Xiao Kihara, Daisuke Nakamura, Tsukasa Hanazono, Huya Gowthaman, Ragul Guest, Johnathan D. Yin, Rui Taherzadeh, Ghazaleh Pierce, Brian G. Barradas-Bautista, Didier Cao, Zhen Cavallo, Luigi Oliva, Romina Sun, Yuanfei Zhu, Shaowen Shen, Yang Park, Taeyong Woo, Hyeonuk Yang, Jinsol Kwon, Sohee Won, Jonghun Seok, Chaok Kiyota, Yasuomi Kobayashi, Shinpei Harada, Yoshiki Takeda-Shitaka, Mayuko Kundrotas, Petras J. Singh, Amar Vakser, Ilya A. Dapkunas, Justas Olechnovic, Kliment Venclovas, Česlovas Duan, Rui Qiu, Liming Xu, Xianjin Zhang, Shuang Zou, Xiaoqin Wodak, Shoshana J. Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment |
description |
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. |
author2 |
Cancer Research UK |
author_facet |
Cancer Research UK Lensink, Marc F. Brysbaert, Guillaume Mauri, Théo Nadzirin, Nurul Velankar, Sameer Chaleil, Raphaël A. G. Clarence, Tereza Bates, Paul A. Kong, Ren Liu, Bin Yang, Guangbo Liu, Ming Shi, Hang Lu, Xufeng Chang, Xang Roy, Raj S. Quadir, Farhan Liu, Jian Cheng, Jianlin Antoniak, Anna Czaplewski, Cezary Giełdón, Artur Kogut, Mateusz Lipska, Agnieszka Liwo, Adam Lubecka, Emilia Maszota-Zieleniak, Martyna Sieradzan, Adam K. Ślusarz, Rafał Wesołowski, Patryk A. Zięba, Karolina Carpio Muñoz, Carlos A. del Ichiishi, Eiichiro Harmalkar, Ameya Gray, Jeffrey J. Bonvin, Alexandre M. J. J. Ambrosetti, Francesco Vargas Honorato, Rodrigo Jandova, Zuzana Jiménez-García, Brian Koukos, Panagiotis I. Keulen, Siri van Noort, Charlotte W. van Réau, Manon Roel-Touris, Jorge Kotelnikov, Sergey Padhorny, Dzmitry Porter, Kathryn Alekseenko, Andrey Ignatov, Mikhail Desta, Israel Ashizawa, Ryota Sun, Zhuyezi Ghani, Usman Hashemi, Nasser Vajda, Sandor Kozakov, Dima Rosell, Mireia Rodríguez-Lumbreras, Luis A. Fernández-Recio, Juan Karczynska, Agnieszka Grudinin, Sergei Yan, Yumeng Li, Hao Lin, Peicong Huang, Sheng-You Christoffer, Charles Terashi, Genki Verburgt, Jacob Sarkar, Daipayan Aderinwale, Tunde Wang, Xiao Kihara, Daisuke Nakamura, Tsukasa Hanazono, Huya Gowthaman, Ragul Guest, Johnathan D. Yin, Rui Taherzadeh, Ghazaleh Pierce, Brian G. Barradas-Bautista, Didier Cao, Zhen Cavallo, Luigi Oliva, Romina Sun, Yuanfei Zhu, Shaowen Shen, Yang Park, Taeyong Woo, Hyeonuk Yang, Jinsol Kwon, Sohee Won, Jonghun Seok, Chaok Kiyota, Yasuomi Kobayashi, Shinpei Harada, Yoshiki Takeda-Shitaka, Mayuko Kundrotas, Petras J. Singh, Amar Vakser, Ilya A. Dapkunas, Justas Olechnovic, Kliment Venclovas, Česlovas Duan, Rui Qiu, Liming Xu, Xianjin Zhang, Shuang Zou, Xiaoqin Wodak, Shoshana J. |
format |
artículo |
topic_facet |
Blind prediction CAPRI CASP Docking Oligomeric state Protein assemblies Protein complexes Protein docking Protein–protein interaction Template-based modeling |
author |
Lensink, Marc F. Brysbaert, Guillaume Mauri, Théo Nadzirin, Nurul Velankar, Sameer Chaleil, Raphaël A. G. Clarence, Tereza Bates, Paul A. Kong, Ren Liu, Bin Yang, Guangbo Liu, Ming Shi, Hang Lu, Xufeng Chang, Xang Roy, Raj S. Quadir, Farhan Liu, Jian Cheng, Jianlin Antoniak, Anna Czaplewski, Cezary Giełdón, Artur Kogut, Mateusz Lipska, Agnieszka Liwo, Adam Lubecka, Emilia Maszota-Zieleniak, Martyna Sieradzan, Adam K. Ślusarz, Rafał Wesołowski, Patryk A. Zięba, Karolina Carpio Muñoz, Carlos A. del Ichiishi, Eiichiro Harmalkar, Ameya Gray, Jeffrey J. Bonvin, Alexandre M. J. J. Ambrosetti, Francesco Vargas Honorato, Rodrigo Jandova, Zuzana Jiménez-García, Brian Koukos, Panagiotis I. Keulen, Siri van Noort, Charlotte W. van Réau, Manon Roel-Touris, Jorge Kotelnikov, Sergey Padhorny, Dzmitry Porter, Kathryn Alekseenko, Andrey Ignatov, Mikhail Desta, Israel Ashizawa, Ryota Sun, Zhuyezi Ghani, Usman Hashemi, Nasser Vajda, Sandor Kozakov, Dima Rosell, Mireia Rodríguez-Lumbreras, Luis A. Fernández-Recio, Juan Karczynska, Agnieszka Grudinin, Sergei Yan, Yumeng Li, Hao Lin, Peicong Huang, Sheng-You Christoffer, Charles Terashi, Genki Verburgt, Jacob Sarkar, Daipayan Aderinwale, Tunde Wang, Xiao Kihara, Daisuke Nakamura, Tsukasa Hanazono, Huya Gowthaman, Ragul Guest, Johnathan D. Yin, Rui Taherzadeh, Ghazaleh Pierce, Brian G. Barradas-Bautista, Didier Cao, Zhen Cavallo, Luigi Oliva, Romina Sun, Yuanfei Zhu, Shaowen Shen, Yang Park, Taeyong Woo, Hyeonuk Yang, Jinsol Kwon, Sohee Won, Jonghun Seok, Chaok Kiyota, Yasuomi Kobayashi, Shinpei Harada, Yoshiki Takeda-Shitaka, Mayuko Kundrotas, Petras J. Singh, Amar Vakser, Ilya A. Dapkunas, Justas Olechnovic, Kliment Venclovas, Česlovas Duan, Rui Qiu, Liming Xu, Xianjin Zhang, Shuang Zou, Xiaoqin Wodak, Shoshana J. |
author_sort |
Lensink, Marc F. |
title |
Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment |
title_short |
Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment |
title_full |
Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment |
title_fullStr |
Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment |
title_full_unstemmed |
Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment |
title_sort |
prediction of protein assemblies, the next frontier: the casp14-capri experiment |
publisher |
Wiley-Liss |
publishDate |
2021-12 |
url |
http://hdl.handle.net/10261/262789 http://dx.doi.org/10.13039/501100011033 http://dx.doi.org/10.13039/100008428 http://dx.doi.org/10.13039/501100000780 http://dx.doi.org/10.13039/100012950 http://dx.doi.org/10.13039/501100001691 http://dx.doi.org/10.13039/501100000265 http://dx.doi.org/10.13039/100000057 http://dx.doi.org/10.13039/100000002 http://dx.doi.org/10.13039/501100001809 http://dx.doi.org/10.13039/100000001 http://dx.doi.org/10.13039/501100000289 |
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dig-icvv-es-10261-2627892022-12-23T14:12:53Z Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment Lensink, Marc F. Brysbaert, Guillaume Mauri, Théo Nadzirin, Nurul Velankar, Sameer Chaleil, Raphaël A. G. Clarence, Tereza Bates, Paul A. Kong, Ren Liu, Bin Yang, Guangbo Liu, Ming Shi, Hang Lu, Xufeng Chang, Xang Roy, Raj S. Quadir, Farhan Liu, Jian Cheng, Jianlin Antoniak, Anna Czaplewski, Cezary Giełdón, Artur Kogut, Mateusz Lipska, Agnieszka Liwo, Adam Lubecka, Emilia Maszota-Zieleniak, Martyna Sieradzan, Adam K. Ślusarz, Rafał Wesołowski, Patryk A. Zięba, Karolina Carpio Muñoz, Carlos A. del Ichiishi, Eiichiro Harmalkar, Ameya Gray, Jeffrey J. Bonvin, Alexandre M. J. J. Ambrosetti, Francesco Vargas Honorato, Rodrigo Jandova, Zuzana Jiménez-García, Brian Koukos, Panagiotis I. Keulen, Siri van Noort, Charlotte W. van Réau, Manon Roel-Touris, Jorge Kotelnikov, Sergey Padhorny, Dzmitry Porter, Kathryn Alekseenko, Andrey Ignatov, Mikhail Desta, Israel Ashizawa, Ryota Sun, Zhuyezi Ghani, Usman Hashemi, Nasser Vajda, Sandor Kozakov, Dima Rosell, Mireia Rodríguez-Lumbreras, Luis A. Fernández-Recio, Juan Karczynska, Agnieszka Grudinin, Sergei Yan, Yumeng Li, Hao Lin, Peicong Huang, Sheng-You Christoffer, Charles Terashi, Genki Verburgt, Jacob Sarkar, Daipayan Aderinwale, Tunde Wang, Xiao Kihara, Daisuke Nakamura, Tsukasa Hanazono, Huya Gowthaman, Ragul Guest, Johnathan D. Yin, Rui Taherzadeh, Ghazaleh Pierce, Brian G. Barradas-Bautista, Didier Cao, Zhen Cavallo, Luigi Oliva, Romina Sun, Yuanfei Zhu, Shaowen Shen, Yang Park, Taeyong Woo, Hyeonuk Yang, Jinsol Kwon, Sohee Won, Jonghun Seok, Chaok Kiyota, Yasuomi Kobayashi, Shinpei Harada, Yoshiki Takeda-Shitaka, Mayuko Kundrotas, Petras J. Singh, Amar Vakser, Ilya A. Dapkunas, Justas Olechnovic, Kliment Venclovas, Česlovas Duan, Rui Qiu, Liming Xu, Xianjin Zhang, Shuang Zou, Xiaoqin Wodak, Shoshana J. 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) Blind prediction CAPRI CASP Docking Oligomeric state Protein assemblies Protein complexes Protein docking Protein–protein interaction Template-based modeling 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 2022-03-03T12:50:19Z 2022-03-03T12:50:19Z 2021-12 2022-03-03T12:50:19Z artículo http://purl.org/coar/resource_type/c_6501 doi: 10.1002/prot.26222 e-issn: 1097-0134 issn: 0887-3585 Proteins - Structure Function and Bioinformatics 89(12): 1800-1823 (2021) http://hdl.handle.net/10261/262789 10.1002/prot.26222 http://dx.doi.org/10.13039/501100011033 http://dx.doi.org/10.13039/100008428 http://dx.doi.org/10.13039/501100000780 http://dx.doi.org/10.13039/100012950 http://dx.doi.org/10.13039/501100001691 http://dx.doi.org/10.13039/501100000265 http://dx.doi.org/10.13039/100000057 http://dx.doi.org/10.13039/100000002 http://dx.doi.org/10.13039/501100001809 http://dx.doi.org/10.13039/100000001 http://dx.doi.org/10.13039/501100000289 #PLACEHOLDER_PARENT_METADATA_VALUE# info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-110167RB-I00/ES/NUEVA METODOLOGIA DE DOCKING ENTRE PROTEINAS PARA LA INTERPRETACION DE VARIANTES GENETICAS DE RELEVANCIA PARA LA SALUD HUMANA/ http://dx.doi.org/10.1002/prot.26222 Sí none Wiley-Liss |