Background: Reconstruction of protein-protein interaction networks (PPIN) has been riddled with controversy for decades. Particularly, false-negative and -positive interactions make this progress even more complicated. Also, lack of a standard PPIN limits us in the comparison studies and results in the incompatible outcomes. Using an evolution-based concept, i.e. interolog which refers to interacting orthologous protein sets, pave the way toward an optimal benchmark.
Results: Here, we provide an R package, IMMAN, as a tool for reconstructing Interolog Protein Network (IPN) by integrating several Protein-protein Interaction Networks (PPINs). Users can unify different PPINs to mine conserved common networks among species. IMMAN is designed to retrieve IPNs with different degrees of conservation to engage prediction analysis of protein functions according to their networks.
Conclusions: IPN consists of evolutionarily conserved nodes and their related edges regarding low false positive rates, which can be considered as a gold standard network in the contexts of biological network analysis regarding to those PPINs which is derived from.
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http://dx.doi.org/10.1186/s12859-019-2659-y | DOI Listing |
PLoS Comput Biol
December 2024
Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France.
Protein-RNA interactions play a critical role in many cellular processes and pathologies. However, experimental determination of protein-RNA structures is still challenging, therefore computational tools are needed for the prediction of protein-RNA interfaces. Although evolutionary pressures can be exploited for structural prediction of protein-protein interfaces, and recent deep learning methods using protein multiple sequence alignments have radically improved the performance of protein-protein interface structural prediction, protein-RNA structural prediction is lagging behind, due to the scarcity of structural data and the flexibility involved in these complexes.
View Article and Find Full Text PDFInt J Mol Sci
September 2024
Department of Zoology, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand.
Methods Mol Biol
July 2024
Centre for Computational Biology and Bioinformatics, Central University of Himachal Pradesh, Dharamshala, Himachal Pradesh, India.
Transcriptomic data is a treasure trove in modern molecular biology, as it offers a comprehensive viewpoint into the intricate nuances of gene expression dynamics underlying biological systems. This genetic information must be utilized to infer biomolecular interaction networks that can provide insights into the complex regulatory mechanisms underpinning the dynamic cellular processes. Gene regulatory networks and protein-protein interaction networks are two major classes of such networks.
View Article and Find Full Text PDFFEBS J
September 2024
Plant Transcription Regulation, International Centre for Genetic Engineering and Biotechnology, New Delhi, India.
Dev Cell
September 2024
Department of Cell Biology, Harvard Medical School, Boston, MA, USA. Electronic address:
We describe a next-generation Drosophila protein interaction map-"DPIM2"-established from affinity purification-mass spectrometry of 5,805 baits, covering the largest fraction of the Drosophila proteome. The network contains 32,668 interactions among 3,644 proteins, organized into 632 clusters representing putative functional modules. Our analysis expands the pool of known protein interactions in Drosophila, provides annotation for poorly studied genes, and postulates previously undescribed protein interaction relationships.
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