Assessment of community efforts to advance network-based prediction of protein–protein interactions

Xu Wen Wang, Lorenzo Madeddu, Kerstin Spirohn, Leonardo Martini, Adriano Fazzone, Luca Becchetti, Thomas P. Wytock, István A. Kovács, Olivér M. Balogh, Bettina Benczik, Mátyás Pétervári, Bence Ágg, Péter Ferdinandy, Loan Vulliard, Jörg Menche, Stefania Colonnese, Manuela Petti, Gaetano Scarano, Francesca Cuomo, Tong HaoFlorent Laval, Luc Willems, Jean Claude Twizere, Marc Vidal, Michael A. Calderwood, Enrico Petrillo, Albert László Barabási, Edwin K. Silverman, Joseph Loscalzo, Paola Velardi*, Yang Yu Liu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number1582
JournalNature communications
Volume14
Issue number1
DOIs
StatePublished - Dec 2023

Funding

L.M., A.F., and L.B. were partially supported by the ERC Advanced Grant 788893 AMDROMA \u201CAlgorithmic and Mechanism Design Research in Online Markets\u201D, the EC H2020RIA project \u201CSoBigData++\u201D (871042), and the MIUR PRIN project ALGADIMAR \u201CAlgorithms, Games, and Digital Markets\u201D. F.L. was supported by a Wallonia-Brussels International (WBI)-World Excellence Fellowship, a Fonds de la Recherche Scientifique (FRS-FNRS)-T\u00E9l\u00E9vie Grant (FC31747, Cr\u00E9dit no. 7459421F), a Herman-van Beneden Prize and a L\u00E9on Fr\u00E9d\u00E9ricq Foundation-Jos\u00E9e & Jean Schmets Prize. M.V. is a Chercheur Qualifi\u00E9 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.\u00C1. 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\u2019s Affairs Medical Center of Boston Contract #36C24122N0769, the NIH grant #1P01HL132825 And the European Union\u2019s Horizon 2020 research and innovation programme under grant agreement No 810115 \u2013 DYNASNET.\u00A0Y.-Y.L. acknowledges grants from National Institutes of Health (R01AI141529, R01HD093761, RF1AG067744, UH3OD023268, U19AI095219, and U01HL089856). PF is the founder and CEO of Pharmahungary Group, a group of R&D companies. EKS has received institutional grant support from Bayer and GlaxoSimthKline. A-LB is co-scientific founder of and is supported by Scipher Medicine, Inc., which applies network medicine strategies to biomarker development and personalized drug selection, and is the founder of Naring Inc., which applies data science to health and nutrition. The remaining authors declare no competing interests. L.M., A.F., and L.B. were partially supported by the ERC Advanced Grant 788893 AMDROMA \u201CAlgorithmic and Mechanism Design Research in Online Markets\u201D, the EC H2020RIA project \u201CSoBigData++\u201D (871042), and the MIUR PRIN project ALGADIMAR \u201CAlgorithms, Games, and Digital Markets\u201D. F.L. was supported by a Wallonia-Brussels International (WBI)-World Excellence Fellowship, a Fonds de la Recherche Scientifique (FRS-FNRS)-T\u00E9l\u00E9vie Grant (FC31747, Cr\u00E9dit no. 7459421F), a Herman-van Beneden Prize and a L\u00E9on Fr\u00E9d\u00E9ricq Foundation-Jos\u00E9e & Jean Schmets Prize. M.V. is a Chercheur Qualifi\u00E9 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.\u00C1. 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\u2019s Affairs Medical Center of Boston Contract #36C24122N0769, the NIH grant #1P01HL132825 And the European Union\u2019s Horizon 2020 research and innovation programme under grant agreement No 810115 \u2013 DYNASNET. Y.-Y.L. acknowledges grants from National Institutes of Health (R01AI141529, R01HD093761, RF1AG067744, UH3OD023268, U19AI095219, and U01HL089856).

ASJC Scopus subject areas

  • General Chemistry
  • General Biochemistry, Genetics and Molecular Biology
  • General Physics and Astronomy

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