Blind prediction of homo‐ and hetero‐ protein complexes: The CASP13‐CAPRI experiment - NANO-D
Article Dans Une Revue Proteins - Structure, Function and Bioinformatics Année : 2019

Blind prediction of homo‐ and hetero‐ protein complexes: The CASP13‐CAPRI experiment

Marc Lensink (1) , Guillaume Brysbaert (1) , Nurul Nadzirin (2) , Sameer Velankar (2) , Raphaël A.G. Chaleil (3) , Tereza Gerguri (3) , Paul Bates (4) , Elodie Laine (5) , Alessandra Carbone (6, 5) , Sergei Grudinin (7) , Ren Kong (8) , Ran‐ran Liu (8) , Xi‐ming Xu (8) , Hang Shi (8) , Shan Chang (8) , Miriam Eisenstein (9) , Agnieszka Karczynska (10) , Cezary Czaplewski (11) , Emilia Lubecka (10) , Agnieszka Lipska (10) , Paweł Krupa (12) , Magdalena Mozolewska (13) , Łukasz Golon (10) , Sergey Samsonov (10) , Adam Liwo (11) , Silvia Crivelli (14) , Guillaume Pagès (7) , Mikhail Karasikov (15, 7) , Maria Kadukova (15, 7) , Yumeng Yan (16) , Sheng‐you Huang (16) , Mireia Rosell (17) , Luis Angel Rodríguez‐lumbreras (17) , Miguel Romero‐durana (17) , Lucía Díaz‐bueno (17) , Juan Fernandez‐recio (17) , Charles Christoffer (18) , Genki Terashi (19) , Woong‐hee Shin (18) , Tunde Aderinwale (18) , Sai Raghavendra Maddhuri Venkata Subram (18) , Daisuke Kihara (18) , Dima Kozakov (20) , Sandor Vajda (21) , Kathyn Porter (21) , Dzmitry Padhorny (21) , Israel Desta (21) , Dmitri Beglov (21) , Mikhail Ignatov (20) , Sergey Kotelnikov (20) , Iain Moal (17) , David Ritchie (22) , Isaure Chauvot de Beauchêne (23, 22) , Bernard Maigret (22) , Marie-Dominique Devignes (22) , Maria Elisa Ruiz Echartea (22) , Didier Barradas‐bautista (24) , Zhen Cao (24) , Luigi Cavallo (24) , Romina Oliva (25) , Yue Cao (24) , Yang Shen (26) , Minkyung Baek (27) , Taeyong Park (27) , Hyeonuk Woo (27) , Chaok Seok (28) , Merav Braitbard (29) , Lirane Bitton (29) , Dina Scheidman‐duhovny (29) , Justas Dapkūnas (30) , Kliment Olechnovič (30) , Česlovas Venclovas (30) , Petras J. Kundrotas (31) , Saveliy Belkin (32) , Devlina Chakravarty (32) , Varsha Badal (32) , Ilya A. Vakser (33) , Thom Vreven (34) , Sweta Vangaveti (34) , Tyler M. Borrman (35) , Zhiping Weng (34) , Johnathan D Guest (36) , Ragul Gowthaman (36) , Brian G Pierce (36) , Xianjin Xu (37) , Rui Duan (38) , Liming Qiu (39) , Jie Hou (38) , Benjamin Ryan Merideth (38) , Zhiwei Ma (39) , Jianlin Cheng (38) , Xiaoqin Zou (40) , Panos Koukos (41) , Jorge Roel‐touris (41) , Francesco Ambrosetti (41) , Cunliang Geng (42) , Jörg Schaarschmidt (41) , Mikael Trellet (43) , Adrien S.J. Melquiond (41) , Li Xue (44) , Brian Jiménez‐garcía (41) , Charlotte Noort (41) , Rodrigo Honorato (41) , Alexandre M.J.J. Bonvin (41) , Shoshana J. Wodak (45)
1 UGSF - Unité de Glycobiologie Structurale et Fonctionnelle - UMR 8576
2 EMBL-EBI - European Bioinformatics Institute [Hinxton]
3 The Francis Crick Institute [London]
4 School of Geographical Sciences [Bristol]
5 LCQB - Biologie Computationnelle et Quantitative = Laboratory of Computational and Quantitative Biology
6 IUF - Institut universitaire de France
7 NANO-D-POST [2018-2020] - Algorithms for Modeling and Simulating Nanosystems [2018-...]
8 JiangSu University
9 Chemical Research Support [Rehovot]
10 UG - University of Gdańsk
11 Department of Environmental Analytics [Univ Gdańsk]
12 IFPAN - Institute of Physics [Warsaw]
13 PAN - Polska Akademia Nauk = Polish Academy of Sciences = Académie polonaise des sciences
14 CS - UC Davis - Department of Computer Science [Univ California Davis]
15 MIPT - Moscow Institute of Physics and Technology [Moscow]
16 HUST - Huazhong University of Science and Technology [Wuhan]
17 BSC-CNS - Barcelona Supercomputing Center - Centro Nacional de Supercomputacion
18 Purdue University [West Lafayette]
19 Kitasato University
20 SBU - Stony Brook University [SUNY]
21 Department of Biomedical Engineering [Boston]
22 CAPSID - Computational Algorithms for Protein Structures and Interactions
23 TUM - Technische Universität Munchen - Technical University Munich - Université Technique de Munich
24 KAUST - King Abdullah University of Science and Technology [Saudi Arabia]
25 University of Naples Federico II = Università degli studi di Napoli Federico II
26 SRMA - Service des Recherches Métallurgiques Appliquées
27 Department of Chemistry
28 SNU - Seoul National University [Seoul]
29 HUJ - The Hebrew University of Jerusalem
30 Vilnius University [Vilnius]
31 Department of Molecular Biosciences [Lawrence]
32 KU - University of Kansas [Lawrence]
33 University of Kansas [Kansas City]
34 UMASS - University of Massachusetts Medical School [Worcester]
35 Program in Bioinformatics and Integrative Biology [Worcester]
36 University of Maryland [Baltimore]
37 Beijing University of Technology
38 Mizzou - University of Missouri [Columbia]
39 Bijvoet Center for Biomolecular Research [Utrecht]
40 Dalton Cardiovascular Research Center [Columbia]
41 Universiteit Utrecht / Utrecht University [Utrecht]
42 Shandong University
43 LIMSI - Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur
44 UC Santa Cruz - University of California [Santa Cruz]
45 SickKids - The Hospital for sick children [Toronto]
Paul Bates
Ren Kong
  • Fonction : Auteur
Ran‐ran Liu
  • Fonction : Auteur
Xi‐ming Xu
  • Fonction : Auteur
Hang Shi
  • Fonction : Auteur
Shan Chang
Charles Christoffer
Daisuke Kihara
Dima Kozakov
Chaok Seok
Justas Dapkūnas
Česlovas Venclovas

Résumé

We present the results for CAPRI Round 46, the third joint CASP‐CAPRI protein assembly prediction challenge. The Round comprised a total of 20 targets including 14 homo‐oligomers and 6 heterocomplexes. Eight of the homo‐oligomer targets and one heterodimer comprised proteins that could be readily modeled using templates from the Protein Data Bank, often available for the full assembly. The remaining 11 targets comprised 5 homodimers, 3 heterodimers, and two higher‐order assemblies. These were more difficult to model, as their prediction mainly involved “ab‐initio” docking of subunit models derived from distantly related templates. A total of ~30 CAPRI groups, including 9 automatic servers, submitted on average ~2000 models per target. About 17 groups participated in the CAPRI scoring rounds, offered for most targets, submitting ~170 models per target. The prediction performance, measured by the fraction of models of acceptable quality or higher submitted across all predictors groups, was very good to excellent for the nine easy targets. Poorer performance was achieved by predictors for the 11 difficult targets, with medium and high quality models submitted for only 3 of these targets. A similar performance “gap” was displayed by scorer groups, highlighting yet again the unmet challenge of modeling the conformational changes of the protein components that occur upon binding or that must be accounted for in template‐based modeling. Our analysis also indicates that residues in binding interfaces were less well predicted in this set of targets than in previous Rounds, providing useful insights for directions of future improvements.
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Dates et versions

hal-02320974 , version 1 (09-11-2020)

Identifiants

Citer

Marc Lensink, Guillaume Brysbaert, Nurul Nadzirin, Sameer Velankar, Raphaël A.G. Chaleil, et al.. Blind prediction of homo‐ and hetero‐ protein complexes: The CASP13‐CAPRI experiment. Proteins - Structure, Function and Bioinformatics, 2019, 87 (12), pp.1200-1221. ⟨10.1002/prot.25838⟩. ⟨hal-02320974⟩
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