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\begin{document}

\title{Impact of AlphaFold on Structure Prediction of Protein Complexes: The
CASP15-CAPRI Experiment}



\author[1]{Marc Lensink}%
\author[1]{Guillaume Brysbaert}%
\author[1]{Nessim Raouraoua}%
\author[2]{Paul Bates}%
\author[3]{Marco Giulini}%
\author[3]{Rodrigo Vargas Honorato}%
\author[3]{Charlotte van Noort}%
\author[3]{João Teixeira}%
\author[3]{Alexandre M.J.J. Bonvin}%
\author[4]{Ren Kong}%
\author[4]{Hang Shi}%
\author[4]{Xufeng Lu}%
\author[4]{Shan Chang}%
\author[5]{Jian Liu}%
\author[5]{Zhiye Guo}%
\author[5]{Xiao Chen}%
\author[5]{Alex Morehead}%
\author[5]{Raj Roy}%
\author[5]{Tianqi Wu}%
\author[5]{Nabin Giri}%
\author[5]{Farhan Quadir}%
\author[5]{Chen Chen}%
\author[5]{Jianlin Cheng}%
\author[6]{Carlos Del Carpio}%
\author[7]{Eichiro Ichiishi}%
\author[8]{Luis Ángel Rodríguez-Lumbreras}%
\author[9]{Juan  Fernández-Recio}%
\author[10]{Ameya Harmalkar}%
\author[10]{Lee-Shin Chu}%
\author[11]{Sam Canner}%
\author[10]{Rituparna Smanta}%
\author[10]{Jeffrey Gray}%
\author[12]{Hao Li}%
\author[12]{Peicong Lin}%
\author[12]{Jiahua He}%
\author[12]{Huanyu Tao}%
\author[12]{Shengyou Huang}%
\author[13]{Jorge Roel}%
\author[14]{Brian Jimenez-Garcia}%
\author[15]{Charles Christoffer}%
\author[16]{Anika Jain J}%
\author[16]{Yuki Kagaya}%
\author[16]{Harini Kannan}%
\author[16]{Tsukasa Nakamura}%
\author[16]{Genki Terashi}%
\author[16]{Jacob Verburgt}%
\author[15]{Yuanyuan Zhang}%
\author[15]{Zicong Zhang}%
\author[17]{Hayato Fujuta}%
\author[17]{Masakazu Sekijima}%
\author[15]{Daisuke Kihara}%
\author[18]{Omeir Khan}%
\author[19]{Sergei Kotelnikov}%
\author[18]{Usman Ghani}%
\author[19]{Dzmitry Padhorny}%
\author[18]{Dmitri Beglov}%
\author[18]{Sandor Vajda}%
\author[19]{Dima Kozakov}%
\author[20]{Surendra Negi S}%
\author[21]{Tiziana Ricciardelli}%
\author[21]{Didier Barradas-Bautista}%
\author[21]{Zhen Cao}%
\author[21]{Mohit Chawla}%
\author[21]{Luigi Cavallo}%
\author[22]{Romina Oliva}%
\author[23]{Rui Yin}%
\author[23]{Melyssa Cheung}%
\author[23]{Johnathan Guest}%
\author[23]{Jessica Lee}%
\author[23]{Brian Pierce}%
\author[24]{Ben Shor}%
\author[24]{Tomer Cohen}%
\author[24]{Matan Halfon}%
\author[24]{Dina Schneidman-Duhovny}%
\author[25]{Shaowen Zhu}%
\author[25]{Rujie Yin}%
\author[25]{Yuanfei Sun}%
\author[25]{Yang Shen}%
\author[26]{Martyna Maszota-Zieleniak}%
\author[27]{Krzysztof Bojarski K}%
\author[27]{Emilia  Lubecka}%
\author[26]{Mateusz Marcisz}%
\author[26]{Annemarie Danielsson}%
\author[26]{Lukasz Dziadek}%
\author[26]{Margrethe Gaardlos}%
\author[26]{Artur Giełdoń}%
\author[26]{Jozef Liwo}%
\author[26]{Sergey Samsonov}%
\author[26]{Rafal Slusarz}%
\author[26]{Karolina Zieba}%
\author[26]{Adam Sieradzan}%
\author[26]{Cezary Czaplewski }%
\author[28]{Shinpei Kobayashi}%
\author[28]{Yuta Miyakawa}%
\author[28]{Yasuomi Kiyota}%
\author[28]{Mayuko Takeda-Shitaka}%
\author[29]{Kliment Olechnovič}%
\author[29]{Lukas Valančauskas}%
\author[29]{Justas Dapkūnas}%
\author[29]{Ceslovas Venclovas}%
\author[30]{Björn Wallner}%
\author[31]{Lin Yang}%
\author[32]{Chengyu Hou}%
\author[31]{Xiaodong He}%
\author[31]{Shuai Guo}%
\author[31]{Shenda Jiang}%
\author[31]{Xiaoliang Ma}%
\author[33]{Rui Duan}%
\author[33]{Liming Qiu}%
\author[33]{Xianjin Xu}%
\author[33]{Xiaoqin Zou}%
\author[34]{Sameer  Velankar}%
\author[35]{Shoshana Wodak J}%
\affil[1]{Unite de Glycobiologie Structurale et Fonctionnelle}%
\affil[2]{The Francis Crick Institute}%
\affil[3]{Universiteit Utrecht Departement Scheikunde}%
\affil[4]{Jiangsu University School of Electrical and Information Engineering}%
\affil[5]{University of Missouri}%
\affil[6]{Fukushimuta Hospital}%
\affil[7]{International University of Health and Welfare (IUHV Hospital}%
\affil[8]{Affiliation not available}%
\affil[9]{Universidad Internacional de la Rioja - Campus de Logrono}%
\affil[10]{Johns Hopkins University Department of Chemical and Biomolecular Engineering}%
\affil[11]{Johns Hopkins University}%
\affil[12]{Huazhong University of Science and Technology School of Physics}%
\affil[13]{Institut de Biologia Molecular de Barcelona}%
\affil[14]{Zymvol Biomodeling}%
\affil[15]{Purdue University}%
\affil[16]{Purdue University Department of Biological Sciences}%
\affil[17]{Tokyo Institute of Technology}%
\affil[18]{Boston University}%
\affil[19]{Stony Brook University}%
\affil[20]{The University of Texas Medical Branch at Galveston Sealy Center for Structural Biology and Molecular Biophysics}%
\affil[21]{The KAUST School}%
\affil[22]{Universita degli Studi di Napoli Parthenope}%
\affil[23]{Institute for Bioscience and Biotechnology Research Shady Grove}%
\affil[24]{Hebrew University of Jeruslaem Rachel and Selim Benin School of Computer Science and Engineering}%
\affil[25]{Texas A\&M University Department of Electrical and Computer Engineering}%
\affil[26]{Uniwersytet Gdanski Wydzial Chemii}%
\affil[27]{Technical University of Gdansk}%
\affil[28]{Kitasato University}%
\affil[29]{Vilnius University}%
\affil[30]{Linkopings universitet Institutionen for fysik kemi och biologi}%
\affil[31]{Harbin Institute of Technology National Key Laboratory of Science and Technology on Advanced Composites in Special Environments}%
\affil[32]{Harbin Institute of Technology School of Electronics and Information Engineering}%
\affil[33]{University of Missouri Dalton Cardiovascular Research Center}%
\affil[34]{European Bioinformatics Institute}%
\affil[35]{Structural Biology Research Center}%


\vspace{-1em}



  
  \date{May 21, 2024}


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\selectlanguage{english}
\begin{abstract}
We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI
protein assembly prediction challenge. The Round offered 37 targets,
including 14 homo-dimers, 3 homo-trimers, 13 hetero-dimers including 3
antibody-antigen complexes, and 7 large assemblies. On average
\textasciitilde{}70 CASP and CAPRI predictor groups, including more than
20 automatics servers, submitted models for each target. A total of
21941 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 5 best
models. Results show substantial progress achieved across a significant
fraction of the 60+ participating groups. High-quality models were
produced for about 40\% for the targets compared to 8\% two years
earlier, a remarkable improvement resulting from the wide use of the
AlphaFold2 and AlphaFold-Multimer software. Creative use was made of the
deep learning inference engines affording the sampling of a much larger
number of models and enriching the multiple sequence alignments with
sequences from various sources. Wide use was also made of the AlphaFold
confidence metrics to rank models, permitting top performing groups to
exceed the results of the public AlphaFold-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.%
\end{abstract}%



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