Article (Scientific journals)
Assessment of community efforts to advance network-based prediction of protein-protein interactions
Wang, Xu-Wen; Madeddu, Lorenzo; Spirohn, Kerstin et al.
2023In Nature Communications, 14 (1), p. 1582
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Keywords :
General Physics and Astronomy; General Biochemistry, Genetics and Molecular Biology; General Chemistry; Multidisciplinary
Abstract :
[en] Comprehensive understanding of the human protein-protein interaction (PPI) network, aka the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of previously uncharacterized PPIs. Many such methods have been proposed. Yet, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 26 representative network-based methods to predict PPIs across six different interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. Through extensive computational and experimental validations, we found that advanced similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods in the interactomes we considered.
Disciplines :
Anatomy (cytology, histology, embryology...) & physiology
Author, co-author :
Wang, Xu-Wen 
Madeddu, Lorenzo
Spirohn, Kerstin 
Martini, Leonardo
Fazzone, Adriano 
Becchetti, Luca
Wytock, Thomas 
Kovács, István
Balogh, Olivér 
Benczik, Bettina
Pétervári, Mátyás
Ágg, Bence 
Ferdinandy, Péter
Vulliard, Loan 
Menche, Jörg 
Colonnese, Stefania
Petti, Manuela 
Scarano, Gaetano 
Cuomo, Francesca
Hao, Tong 
Laval, Florent  ;  Université de Liège - ULiège > TERRA Research Centre
Willems, Luc  ;  Université de Liège - ULiège > GIGA > GIGA Cancer - Cellular and Molecular Epigenetics
Twizere, Jean-Claude  ;  Université de Liège - ULiège > GIGA > GIGA Molecular Biology of Diseases - Viral Interactomes Network
Vidal, Marc
Calderwood, Michael 
Petrillo, Enrico 
Barabási, Albert-László
Silverman, Edwin
Loscalzo, Joseph 
Velardi, Paola
Liu, Yang-Yu 
More authors (21 more) Less
Language :
Title :
Assessment of community efforts to advance network-based prediction of protein-protein interactions
Publication date :
22 March 2023
Journal title :
Nature Communications
Publisher :
Springer Science and Business Media LLC
Volume :
Issue :
Pages :
Peer reviewed :
Peer Reviewed verified by ORBi
Funders :
WBI - Wallonia-Brussels International [BE]
Télévie [BE]
Leon Fredericq Foundation [BE]
F.R.S.-FNRS - Fonds de la Recherche Scientifique [BE]
Funding text :
L.M., A.F., and L.B. were partially supported by the ERC Advanced Grant 788893 AMDROMA “Algorithmic and Mechanism Design Research in Online Markets”, the EC H2020RIA project “SoBigData++” (871042), and the MIUR PRIN project ALGADIMAR “Algorithms, Games, and Digital Markets”. F.L. was supported by a Wallonia-Brussels International (WBI)-World Excellence Fellowship, a Fonds de la Recherche Scientifique (FRS-FNRS)-Télévie Grant (FC31747, Crédit no. 7459421F), a Herman-van Beneden Prize and a Léon Frédéricq Foundation-Josée & Jean Schmets Prize. M.V. is a Chercheur Qualifié Honoraire from the Fonds de la Recherche Scientifique (FRS-FNRS, Wallonia-Brussels Federation, Belgium). M.V acknowledges support from the National Institute of Health (R01 GM130885). P.F. and B.Á. were supported by the National Research, Development and Innovation Office of Hungary (National Heart Program NVKP 16-1-2016-0017) and the Thematic Excellence Programme (2020-4.1.1.-TKP2020) of the Ministry for Innovation and Technology in Hungary, within the framework of the Therapeutic Development and Bioimaging thematic programmes of the Semmelweis University. Project no. RRF-2.3.1-21-2022-00003 has been implemented with the support provided by the European Union. JL acknowledges support from the National Institutes of Health (R01 HL155107, R01 HL155096, U01 HG007690, and U54 HL119145); and from the American Heart Association (D700382 and CV-19). A-LB is supported by the Veteran’s Affairs Medical Center of Boston Contract #36C24122N0769, the NIH grant #1P01HL132825 And the European Union’s Horizon 2020 research and innovation programme under grant agreement No 810115 – DYNASNET. Y.-Y.L. acknowledges grants from National Institutes of Health (R01AI141529, R01HD093761, RF1AG067744, UH3OD023268, U19AI095219, and U01HL089856).
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