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 Hao; Florent 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

doi:10.1038/s41467-023-37079-7

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

Physics and Astronomy

Research output: Contribution to journal › Article › peer-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 language	English (US)
Article number	1582
Journal	Nature communications
Volume	14
Issue number	1
DOIs	https://doi.org/10.1038/s41467-023-37079-7
State	Published - 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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Wang, X. W., Madeddu, L., Spirohn, K., Martini, L., Fazzone, A., Becchetti, L., Wytock, T. P., Kovács, I. A., Balogh, O. M., Benczik, B., Pétervári, M., Ágg, B., Ferdinandy, P., Vulliard, L., Menche, J., Colonnese, S., Petti, M., Scarano, G., Cuomo, F., ... Liu, Y. Y. (2023). Assessment of community efforts to advance network-based prediction of protein–protein interactions. Nature communications, 14(1), Article 1582. https://doi.org/10.1038/s41467-023-37079-7

@article{ab750757fa714d3a946c7e6624b5b1c9,

title = "Assessment of community efforts to advance network-based prediction of protein–protein interactions",

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.",

author = "Wang, {Xu Wen} and Lorenzo Madeddu and Kerstin Spirohn and Leonardo Martini and Adriano Fazzone and Luca Becchetti and Wytock, {Thomas P.} and Kov{\'a}cs, {Istv{\'a}n A.} and Balogh, {Oliv{\'e}r M.} and Bettina Benczik and M{\'a}ty{\'a}s P{\'e}terv{\'a}ri and Bence {\'A}gg and P{\'e}ter Ferdinandy and Loan Vulliard and J{\"o}rg Menche and Stefania Colonnese and Manuela Petti and Gaetano Scarano and Francesca Cuomo and Tong Hao and Florent Laval and Luc Willems and Twizere, {Jean Claude} and Marc Vidal and Calderwood, {Michael A.} and Enrico Petrillo and Barab{\'a}si, {Albert L{\'a}szl{\'o}} and Silverman, {Edwin K.} and Joseph Loscalzo and Paola Velardi and Liu, {Yang Yu}",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s).",

year = "2023",

month = dec,

doi = "10.1038/s41467-023-37079-7",

language = "English (US)",

volume = "14",

journal = "Nature communications",

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Wang, XW, Madeddu, L, Spirohn, K, Martini, L, Fazzone, A, Becchetti, L, Wytock, TP, Kovács, IA, Balogh, OM, Benczik, B, Pétervári, M, Ágg, B, Ferdinandy, P, Vulliard, L, Menche, J, Colonnese, S, Petti, M, Scarano, G, Cuomo, F, Hao, T, Laval, F, Willems, L, Twizere, JC, Vidal, M, Calderwood, MA, Petrillo, E, Barabási, AL, Silverman, EK, Loscalzo, J, Velardi, P & Liu, YY 2023, 'Assessment of community efforts to advance network-based prediction of protein–protein interactions', Nature communications, vol. 14, no. 1, 1582. https://doi.org/10.1038/s41467-023-37079-7

TY - JOUR

T1 - Assessment of community efforts to advance network-based prediction of protein–protein interactions

AU - Wang, Xu Wen

AU - Madeddu, Lorenzo

AU - Spirohn, Kerstin

AU - Martini, Leonardo

AU - Fazzone, Adriano

AU - Becchetti, Luca

AU - Wytock, Thomas P.

AU - Kovács, István A.

AU - Balogh, Olivér M.

AU - Benczik, Bettina

AU - Pétervári, Mátyás

AU - Ágg, Bence

AU - Ferdinandy, Péter

AU - Vulliard, Loan

AU - Menche, Jörg

AU - Colonnese, Stefania

AU - Petti, Manuela

AU - Scarano, Gaetano

AU - Cuomo, Francesca

AU - Hao, Tong

AU - Laval, Florent

AU - Willems, Luc

AU - Twizere, Jean Claude

AU - Vidal, Marc

AU - Calderwood, Michael A.

AU - Petrillo, Enrico

AU - Barabási, Albert László

AU - Silverman, Edwin K.

AU - Loscalzo, Joseph

AU - Velardi, Paola

AU - Liu, Yang Yu

PY - 2023/12

Y1 - 2023/12

N2 - 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.

AB - 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.

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

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