Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment

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

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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.

Original languageEnglish
Pages (from-to)1800-1823
Number of pages24
JournalProteins: Structure, Function and Bioinformatics
Volume89
Issue number12
DOIs
Publication statusPublished - Dec 2021

Bibliographical note

Funding Information:
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 Funding information

Publisher Copyright:
© 2021 Wiley Periodicals LLC.

Funding

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 Funding information

Keywords

  • blind prediction
  • CAPRI
  • CASP
  • docking
  • oligomeric state
  • protein assemblies
  • protein complexes
  • protein docking
  • protein–protein interaction
  • template-based modeling

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