HPIPred : Host-pathogen interactome prediction with phenotypic scoring
Macho Rendón, Javier (Universitat Autònoma de Barcelona. Departament de Bioquímica i de Biologia Molecular)
Rebollido-Ríos, Rocio (Universitat Autònoma de Barcelona. Departament de Bioquímica i de Biologia Molecular)
Torrent Burgas, Marc (Universitat Autònoma de Barcelona. Departament de Bioquímica i de Biologia Molecular)

Date:	2022
Abstract:	Protein-protein interactions (PPIs) are involved in most cellular processes. Unfortunately, current knowledge of host-pathogen interactomes is still very limited. Experimental methods used to detect PPIs have several limitations, including increasing complexity and economic cost in large-scale screenings. Hence, computational methods are commonly used to support experimental data, although they generally suffer from high false-positive rates. To address this issue, we have created HPIPred, a host-pathogen PPI prediction tool based on numerical encoding of physicochemical properties. Unlike other available methods, HPIPred integrates phenotypic data to prioritize biologically meaningful results. We used HPIPred to screen the entire Homo sapiens and Pseudomonas aeruginosa PAO1 proteomes to generate a host-pathogen interactome with 763 interactions displaying a highly connected network topology. Our predictive model can be used to prioritize protein-protein interactions as potential targets for antibacterial drug development.
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	PPI, Protein-protein interaction ; CCC, Cross-correlation coefficient ; BC, Betweenness centrality ; FPR, False positive rate ; ROC, Receiver-operating characteristic ; PR, Precision-recall
Published in:	Computational and Structural Biotechnology Journal, Vol. 20 (november 2022) , p. 6534-6542, ISSN 2001-0370

DOI: 10.1016/j.csbj.2022.11.026
PMID: 36514317

9 p, 2.4 MB

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