Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment.
Creators: name: Lensink, Marc F
name: Brysbaert, Guillaume
name: Raouraoua, Nessim
name: Bates, Paul A
name: Giulini, Marco
name: Honorato, Rodrigo V
name: van Noort, Charlotte
name: MC Teixeira, Joao
name: Bonvin, Alexandre MJJ
name: Kong, Ren
name: Shi, Hang
name: Lu, Xufeng
name: Chang, Shan
name: Liu, Jian
name: Guo, Zhiye
name: Chen, Xiao
name: Morehead, Alex
name: Roy, Raj S
name: Wu, Tianqi
name: Giri, Nabin
name: Quadir, Farhan
name: Chen, Chen
name: Cheng, Jianlin
name: Del Carpio, Carlos A
name: Ichiishi, Eichiro
name: Rodriguez-Lumbreras, Luis A
name: Fernandez-Recio, Juan
name: Harmalkar, Ameya
name: Chu, Lee-Shin
name: Canner, Sam
name: Smanta, Rituparna
name: Gray, Jeffrey J
name: Li, Hao
name: Lin, Peicong
name: He, Jiahua
name: Tao, Huanyu
name: Huang, Sheng-You
name: Roel-Touris, Jorge
name: Jimenez-Garcia, Brian
name: Christoffer, Charles W
name: Jain, Anika J
name: Kagaya, Yuki
name: Kannan, Harini
name: Nakamura, Tsukasa
name: Terashi, Genki
name: Verburgt, Jacob C
name: Zhang, Yuanyuan
name: Zhang, Zicong
name: Fujuta, Hayato
name: Sekijima, Masakazu
name: Kihara, Daisuke
name: Khan, Omeir
name: Kotelnikov, Sergei
name: Ghani, Usman
name: Padhorny, Dzmitry
name: Beglov, Dmitri
name: Vajda, Sandor
name: Kozakov, Dima
name: Negi, Surendra S
name: Ricciardelli, Tiziana
name: Barradas-Bautista, Didier
name: Cao, Zhen
name: Chawla, Mohit
name: Cavallo, Luigi
name: Oliva, Romina
name: Yin, Rui
name: Cheung, Melyssa
name: Guest, Johnathan D
name: Lee, Jessica
name: Pierce, Brian G
name: Shor, Ben
name: Cohen, Tomer
name: Halfon, Matan
name: Schneidman-Duhovny, Dina
name: Zhu, Shaowen
name: Yin, Rujie
name: Sun, Yuanfei
name: Shen, Yang
name: Maszota-Zieleniak, Martyna
name: Bojarski, Krzysztof K
name: Lubecka, Emilia A
name: Marcisz, Mateusz
name: Danielsson, Annemarie
name: Dziadek, Lukasz
name: Gaardlos, Margrethe
name: Gieldon, Artur
name: Liwo, Adam
name: Samsonov, Sergey A
name: Slusarz, Rafal
name: Zieba, Karolina
name: Sieradzan, Adam K
name: Czaplewski, Cezary
name: Kobayashi, Shinpei
name: Miyakawa, Yuta
name: Kiyota, Yasuomi
name: Takeda-Shitaka, Mayuko
name: Olechnovic, Kliment
name: Valancauskas, Lukas
name: Dapkunas, Justas
name: Venclovas, Ceslovas
name: Wallner, Bjorn
name: Yang, Lin
name: Hou, Chengyu
name: He, Xiaodong
name: Guo, Shuai
name: Jiang, Shenda
name: Ma, Xiaoliang
name: Duan, Rui
name: Qui, Liming
name: Xu, Xianjin
name: Zou, Xiaoqin
name: Velankar, Sameer
name: Wodak, Shoshana J
Publisher: name: The Francis Crick Institute
Year: 2023
Subjects: classificationCode: Structural Biology & Biophysics
subject: Structural Biology &amp; Biophysics
classificationCode: Computational & Systems Biology
subject: Computational &amp; Systems Biology

description: We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo-trimers, 13 heterodimers including 3 antibody-antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2-Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem.