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Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment
Proteins: Structure, Function, and Bioinformatics ( IF 3.2 ) Pub Date : 2021-08-28 , DOI: 10.1002/prot.26222 Marc F. Lensink 1 , Guillaume Brysbaert 1 , Théo Mauri 1 , Nurul Nadzirin 2 , Sameer Velankar 2 , Raphael A. G. Chaleil 3 , Tereza Clarence 3 , Paul A. Bates 3 , Ren Kong 4 , Bin Liu 4 , Guangbo Yang 4 , Ming Liu 4 , Hang Shi 4 , Xufeng Lu 4 , Shan Chang 4 , Raj S. Roy 5 , Farhan Quadir 5 , Jian Liu 5 , Jianlin Cheng 5, 6 , Anna Antoniak 7 , Cezary Czaplewski 7 , Artur GiełdoŃ 7 , Mateusz Kogut 7 , Agnieszka G. Lipska 7 , Adam Liwo 7 , Emilia A. Lubecka 8 , Martyna Maszota‐Zieleniak 7 , Adam K. Sieradzan 7 , Rafał Ślusarz 7 , Patryk A. Wesołowski 7, 9 , Karolina ZiĘba 7 , Carlos A. Del Carpio Muñoz 10 , Eiichiro Ichiishi 11 , Ameya Harmalkar 12 , Jeffrey J. Gray 12 , Alexandre M. J. J. Bonvin 13 , Francesco Ambrosetti 13 , Rodrigo Vargas Honorato 13 , Zuzana Jandova 13 , Brian Jiménez‐García 13 , Panagiotis I. Koukos 13 , Siri Van Keulen 13 , Charlotte W. Van Noort 13 , Manon Réau 13 , Jorge Roel‐Touris 13 , Sergei Kotelnikov 14, 15, 16 , Dzmitry Padhorny 14, 15 , Kathryn A. Porter 17 , Andrey Alekseenko 14, 15, 18 , Mikhail Ignatov 14, 15 , Israel Desta 17 , Ryota Ashizawa 14, 15 , Zhuyezi Sun 17 , Usman Ghani 17 , Nasser Hashemi 17 , Sandor Vajda 17, 19 , Dima Kozakov 14, 15 , Mireia Rosell 20, 21 , Luis A. Rodríguez‐Lumbreras 20, 21 , Juan Fernandez‐Recio 20, 21 , Agnieszka Karczynska 22 , Sergei Grudinin 22 , Yumeng Yan 23 , Hao Li 23 , Peicong Lin 23 , Sheng‐You Huang 23 , Charles Christoffer 24 , Genki Terashi 25 , Jacob Verburgt 25 , Daipayan Sarkar 25 , Tunde Aderinwale 24 , Xiao Wang 24 , Daisuke Kihara 24, 25 , Tsukasa Nakamura 26 , Yuya Hanazono 27 , Ragul Gowthaman 28, 29 , Johnathan D. Guest 28, 29 , Rui Yin 28, 29 , Ghazaleh Taherzadeh 28, 29 , Brian G. Pierce 28, 29 , Didier Barradas‐Bautista 30 , Zhen Cao 30 , Luigi Cavallo 30 , Romina Oliva 31 , Yuanfei Sun 32 , Shaowen Zhu 32 , Yang Shen 32 , Taeyong Park 33 , Hyeonuk Woo 33 , Jinsol Yang 33 , Sohee Kwon 33 , Jonghun Won 33 , Chaok Seok 33 , Yasuomi Kiyota 34 , Shinpei Kobayashi 34 , Yoshiki Harada 34 , Mayuko Takeda‐Shitaka 34 , Petras J. Kundrotas 35 , Amar Singh 35 , Ilya A. Vakser 35 , Justas DapkŪnas 36 , Kliment Olechnovič 36 , česlovas Venclovas 36 , Rui Duan 37 , Liming Qiu 37 , Shuang Zhang 37 , Xiaoqin Zou 6, 37, 38, 39 , Shoshana J. Wodak 40
Proteins: Structure, Function, and Bioinformatics ( IF 3.2 ) Pub Date : 2021-08-28 , DOI: 10.1002/prot.26222 Marc F. Lensink 1 , Guillaume Brysbaert 1 , Théo Mauri 1 , Nurul Nadzirin 2 , Sameer Velankar 2 , Raphael A. G. Chaleil 3 , Tereza Clarence 3 , Paul A. Bates 3 , Ren Kong 4 , Bin Liu 4 , Guangbo Yang 4 , Ming Liu 4 , Hang Shi 4 , Xufeng Lu 4 , Shan Chang 4 , Raj S. Roy 5 , Farhan Quadir 5 , Jian Liu 5 , Jianlin Cheng 5, 6 , Anna Antoniak 7 , Cezary Czaplewski 7 , Artur GiełdoŃ 7 , Mateusz Kogut 7 , Agnieszka G. Lipska 7 , Adam Liwo 7 , Emilia A. Lubecka 8 , Martyna Maszota‐Zieleniak 7 , Adam K. Sieradzan 7 , Rafał Ślusarz 7 , Patryk A. Wesołowski 7, 9 , Karolina ZiĘba 7 , Carlos A. Del Carpio Muñoz 10 , Eiichiro Ichiishi 11 , Ameya Harmalkar 12 , Jeffrey J. Gray 12 , Alexandre M. J. J. Bonvin 13 , Francesco Ambrosetti 13 , Rodrigo Vargas Honorato 13 , Zuzana Jandova 13 , Brian Jiménez‐García 13 , Panagiotis I. Koukos 13 , Siri Van Keulen 13 , Charlotte W. Van Noort 13 , Manon Réau 13 , Jorge Roel‐Touris 13 , Sergei Kotelnikov 14, 15, 16 , Dzmitry Padhorny 14, 15 , Kathryn A. Porter 17 , Andrey Alekseenko 14, 15, 18 , Mikhail Ignatov 14, 15 , Israel Desta 17 , Ryota Ashizawa 14, 15 , Zhuyezi Sun 17 , Usman Ghani 17 , Nasser Hashemi 17 , Sandor Vajda 17, 19 , Dima Kozakov 14, 15 , Mireia Rosell 20, 21 , Luis A. Rodríguez‐Lumbreras 20, 21 , Juan Fernandez‐Recio 20, 21 , Agnieszka Karczynska 22 , Sergei Grudinin 22 , Yumeng Yan 23 , Hao Li 23 , Peicong Lin 23 , Sheng‐You Huang 23 , Charles Christoffer 24 , Genki Terashi 25 , Jacob Verburgt 25 , Daipayan Sarkar 25 , Tunde Aderinwale 24 , Xiao Wang 24 , Daisuke Kihara 24, 25 , Tsukasa Nakamura 26 , Yuya Hanazono 27 , Ragul Gowthaman 28, 29 , Johnathan D. Guest 28, 29 , Rui Yin 28, 29 , Ghazaleh Taherzadeh 28, 29 , Brian G. Pierce 28, 29 , Didier Barradas‐Bautista 30 , Zhen Cao 30 , Luigi Cavallo 30 , Romina Oliva 31 , Yuanfei Sun 32 , Shaowen Zhu 32 , Yang Shen 32 , Taeyong Park 33 , Hyeonuk Woo 33 , Jinsol Yang 33 , Sohee Kwon 33 , Jonghun Won 33 , Chaok Seok 33 , Yasuomi Kiyota 34 , Shinpei Kobayashi 34 , Yoshiki Harada 34 , Mayuko Takeda‐Shitaka 34 , Petras J. Kundrotas 35 , Amar Singh 35 , Ilya A. Vakser 35 , Justas DapkŪnas 36 , Kliment Olechnovič 36 , česlovas Venclovas 36 , Rui Duan 37 , Liming Qiu 37 , Shuang Zhang 37 , Xiaoqin Zou 6, 37, 38, 39 , Shoshana J. Wodak 40
Affiliation
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.
中文翻译：
预测蛋白质组装，下一个前沿：CASP14-CAPRI 实验
我们展示了 CAPRI 第 50 轮的结果，这是第四次联合 CASP-CAPRI 蛋白质组装预测挑战。该回合共包括十二个目标，包括六个二聚体、三个三聚体和三个更高阶的寡聚体。其中四个是简单的目标，对于它们，无论是完整组装还是主界面（高阶低聚物的），都可以使用良好的结构模板。八个是困难的目标，仅针对单个亚基发现了远缘相关的模板。包括 8 个自动服务器在内的 25 个 CAPRI 小组为每个目标提交了约 1250 个模型。包括 6 个服务器在内的 20 个小组参加了 CAPRI 评分挑战，每个目标提交了大约 190 个模型。使用经典的 CAPRI 标准评估预测模型的准确性。预测性能是通过加权评分方案来衡量的，该方案考虑了每个组作为其五个顶级模型的一部分提交的可接受质量或更高质量的模型数量。与之前的 CASP-CAPRI 挑战相比，表现最好的团体在本轮中为更大比例（70-75%）的目标提交了此类模型，但这些模型中具有高精度的模型较少。得分组取得了更强的表现，更多的组为 70-80% 的目标提交了正确的模型或实现了高精度预测。除了 MDOCKPP 和 LZERD 服务器，它们的表现与人类群体相当。除了这些结果之外，还讨论了方法论的主要进步，
更新日期：2021-09-13
中文翻译：
预测蛋白质组装，下一个前沿：CASP14-CAPRI 实验
我们展示了 CAPRI 第 50 轮的结果，这是第四次联合 CASP-CAPRI 蛋白质组装预测挑战。该回合共包括十二个目标，包括六个二聚体、三个三聚体和三个更高阶的寡聚体。其中四个是简单的目标，对于它们，无论是完整组装还是主界面（高阶低聚物的），都可以使用良好的结构模板。八个是困难的目标，仅针对单个亚基发现了远缘相关的模板。包括 8 个自动服务器在内的 25 个 CAPRI 小组为每个目标提交了约 1250 个模型。包括 6 个服务器在内的 20 个小组参加了 CAPRI 评分挑战，每个目标提交了大约 190 个模型。使用经典的 CAPRI 标准评估预测模型的准确性。预测性能是通过加权评分方案来衡量的，该方案考虑了每个组作为其五个顶级模型的一部分提交的可接受质量或更高质量的模型数量。与之前的 CASP-CAPRI 挑战相比，表现最好的团体在本轮中为更大比例（70-75%）的目标提交了此类模型，但这些模型中具有高精度的模型较少。得分组取得了更强的表现，更多的组为 70-80% 的目标提交了正确的模型或实现了高精度预测。除了 MDOCKPP 和 LZERD 服务器，它们的表现与人类群体相当。除了这些结果之外，还讨论了方法论的主要进步，
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