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\begin{document}

\title{Discriminating physiological from non-physiological interfaces in
structures of protein complexes: a community-wide study}



\author[1]{Hugo Schweke}%
\author[2]{Qifang Xu}%
\author[3]{Gerardo Tauriello}%
\author[3]{Lorenzo Pantolini}%
\author[3]{Torsten Schwede}%
\author[4]{Frédéric Cazals}%
\author[5]{Alix Lhéritier}%
\author[6]{Juan Fernandez-Recio}%
\author[6]{Luis Ángel Rodríguez-Lumbreras}%
\author[7]{Ora Schueler-Furman}%
\author[7]{Julia K. Varga}%
\author[8]{Brian Jiménez-García}%
\author[9]{Manon F. Réau}%
\author[9]{Alexandre Bonvin}%
\author[10]{Castrense Savojardo}%
\author[10]{Pier-Luigi Martelli}%
\author[10]{Rita Casadio}%
\author[11]{Jérôme Tubiana}%
\author[11]{Haim Wolfson}%
\author[12]{Romina Oliva}%
\author[13]{Didier Barradas-Bautista}%
\author[13]{Tiziana Ricciardelli}%
\author[13]{Luigi Cavallo}%
\author[14]{Česlovas Venclovas}%
\author[14]{Kliment Olechnovič}%
\author[15]{Raphael Guerois}%
\author[15]{Jessica Andreani}%
\author[16]{Juliette Martin}%
\author[17]{Xiao Wang}%
\author[17]{Daisuke Kihara }%
\author[18]{Anthony Marchand}%
\author[18]{Bruno Correia}%
\author[19]{Xiaoqin Zou}%
\author[20]{Sucharita Dey}%
\author[2]{Roland Dunbrack}%
\author[1]{Emmanuel Levy}%
\author[21]{Shoshana Wodak}%
\affil[1]{Weizmann Institute of Science}%
\affil[2]{Fox Chase Cancer Center}%
\affil[3]{University of Basel Department Biozentrum}%
\affil[4]{Inria Sophia Antipolis Mediterranean Research Centre}%
\affil[5]{Amadeus SAS}%
\affil[6]{Instituto de Ciencias de la Vid y del Vino}%
\affil[7]{Hebrew University of Jerusalem Institute for Medical Research Israel-Canada}%
\affil[8]{Utrecht University \& Zymvol Biomodeling SL}%
\affil[9]{Utrecht University Faculty of Science}%
\affil[10]{University of Bologna}%
\affil[11]{Tel Aviv University Blavatnik School of Computer Science}%
\affil[12]{University of Naples Parthenope}%
\affil[13]{King Abdullah University of Science and Technology}%
\affil[14]{Vilnius University}%
\affil[15]{CEA}%
\affil[16]{Université Claude Bernard Lyon 1}%
\affil[17]{Purdue University}%
\affil[18]{EPFL}%
\affil[19]{Dalton Cardiovascular Research Center, Institute for Data Science and  Informatics, University of Missouri}%
\affil[20]{Indian Institute of Technology Jodhpur}%
\affil[21]{VIB-VUB Center for Structural Biology}%


\vspace{-1em}



  
  \date{May 30, 2024}


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\selectlanguage{english}
\begin{abstract}
Reliably scoring and ranking candidate models of protein complexes and
assigning their oligomeric state from the structure of the crystal
lattice represent outstanding challenges. A community-wide effort was
launched to tackle these challenges. The latest resources on protein
complexes and interfaces were exploited to derive a benchmark dataset
consisting of 1677 homodimer protein crystal structures, including a
balanced mix of physiological and non-physiological complexes. The
non-physiological complexes in the benchmark were selected to bury a
similar or larger interface area than their physiological counterparts,
making it more difficult for scoring functions to differentiate between
them. Next, 252 functions for scoring protein-protein interfaces
previously developed by 13 groups were collected and evaluated for their
ability to discriminate between physiological and non-physiological
complexes. A simple consensus score generated using the best performing
score of each of the 13 groups, and a cross-validated Random Forest (RF)
classifier were created. Both approaches showed excellent performance,
with an area under the Receiver Operating Characteristic (ROC) curve of
0.93 and 0.94 respectively, outperforming individual scores developed by
different groups. Additionally, AlphaFold2 engines were shown to recall
the physiological dimers with significantly higher accuracy than the
non-physiological set, lending support for the pertinence of our
benchmark dataset. Optimizing the combined power of interface scoring
functions and evaluating it on challenging benchmark datasets appears to
be a promising strategy.%
\end{abstract}%



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