Using the MINT Database to Search Protein Interactions

Calderone, Alberto; Iannuccelli, Marta; Peluso, Daniele; Licata, Luana

doi:10.1002/cpbi.93

Cited by 15 publications

(13 citation statements)

References 13 publications

(16 reference statements)

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“…In IntAct database, all protein interaction information comes from literature reports or users' direct online submission. MINT mainly stores mammals' protein-protein interacting (PPI) data extracted from scientific literature and annotated in the database by professional curators (Calderone et al, 2020). HPRD database only contains human PPI and is the largest human PPI database from literature mining containing PTM, subcellular localization, domain, and other information.…”

Section: The Interacting Proteins Of Prmt5mentioning

confidence: 99%

Protein Arginine Methyltransferase 5 Functions via Interacting Proteins

Liang

Wen

Jiang

et al. 2021

Front. Cell Dev. Biol.

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The protein arginine methyltransferases (PRMTs) are involved in such biological processes as transcription regulation, DNA repair, RNA splicing, and signal transduction, etc. In this study, we mainly focused on PRMT5, a member of the type II PRMTs, which functions mainly alongside other interacting proteins. PRMT5 has been shown to be overexpressed in a wide variety of cancers and other diseases, and is involved in the regulation of Epstein-Barr virus infection, viral carcinogenesis, spliceosome, hepatitis B, cell cycles, and various signaling pathways. We analyzed the regulatory roles of PRMT5 and interacting proteins in various biological processes above-mentioned, to elucidate for the first time the interaction between PRMT5 and its interacting proteins. This systemic analysis will enrich the biological theory and contribute to the development of novel therapies.

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Section: The Interacting Proteins Of Prmt5mentioning

confidence: 99%

Protein Arginine Methyltransferase 5 Functions via Interacting Proteins

Liang

Wen

Jiang

et al. 2021

Front. Cell Dev. Biol.

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“…This process uses several databases of protein-protein interactions as well as databases of relationships between chemicals, diseases, biological processes and proteins. (ChEMBL [ 130 ], PubChem [ 131 ], PUBMED/MEDLINE, CTD [ 132 ], DGIdb [ 133 ], SIGNOR [ 134 ], UniProt [ 135 ], BioGRID [ 136 ], Complex Portal [ 137 ], IntAct [ 138 ], mentha [ 139 ], MINT [ 140 ], Reactome [ 141 ], and STRING [ 142 ]).…”

Section: Methodsmentioning

confidence: 99%

Quantitative Proteomic Approach Reveals Altered Metabolic Pathways in Response to the Inhibition of Lysine Deacetylases in A549 Cells under Normoxia and Hypoxia

Martín-Bernabé

Tarragó-Celada

Cunin

et al. 2021

IJMS

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Growing evidence is showing that acetylation plays an essential role in cancer, but studies on the impact of KDAC inhibition (KDACi) on the metabolic profile are still in their infancy. Here, we analyzed, by using an iTRAQ-based quantitative proteomics approach, the changes in the proteome of KRAS-mutated non-small cell lung cancer (NSCLC) A549 cells in response to trichostatin-A (TSA) and nicotinamide (NAM) under normoxia and hypoxia. Part of this response was further validated by molecular and biochemical analyses and correlated with the proliferation rates, apoptotic cell death, and activation of ROS scavenging mechanisms in opposition to the ROS production. Despite the differences among the KDAC inhibitors, up-regulation of glycolysis, TCA cycle, oxidative phosphorylation and fatty acid synthesis emerged as a common metabolic response underlying KDACi. We also observed that some of the KDACi effects at metabolic levels are enhanced under hypoxia. Furthermore, we used a drug repositioning machine learning approach to list candidate metabolic therapeutic agents for KRAS mutated NSCLC. Together, these results allow us to better understand the metabolic regulations underlying KDACi in NSCLC, taking into account the microenvironment of tumors related to hypoxia, and bring new insights for the future rational design of new therapies.

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“…These public resources can be divided in: (i ) primary databases that collect only manually curated molecular interactions extracted from peer-reviewed journals and related to different viruses and their relative hosts, such as MINT ( Calderone et al, 2020 ), IntAct ( Del Toro et al, 2021 ), and BioGRID ( Oughtred et al, 2021 ); ( ii ) metadatabases integrating data from primary resources, such as VirusMentha ( Calderone et al, 2015 ) and APID ( Alonso-López et al, 2019 ); ( iii ) databases combining experimental interaction data with predicted PPIs, such as virusSTRING ( Szklarczyk et al, 2021 ), human-virus PPI database (HVIDB) ( Yang et al, 2021 ) and the pathogen-host interaction search tool PHISTO ( Durmuş Tekir et al, 2013 ); ( iv ) databases, such as VirHostnet3.0 database ( Guirimand et al, 2015 ), which are both primary resources collecting manually annotated PPIs and metadatabases integrating data from other molecular interaction databases; and ( v ) databases collecting information only related to a specific virus-host interactome, such as DenHunt ( Karyala et al, 2016 ) and DenvInt ( Dey and Mukhopadhyay, 2017 ) for the Dengue virus, the HIV-1 Human Interaction Database ( Ako-Adjei et al, 2015 ) and the Hepatitis C Virus Protein Interaction Database (HCVpro) ( Kwofie et al, 2011 ).…”

Section: Public Resources Collecting Virus-host Protein-protein Inter...mentioning

confidence: 99%

“…Over the years, these interaction maps described in the scientific literature have been systematically captured into several publicly available molecular interaction databases (e.g., Guirimand et al, 2015 ; Calderone et al, 2020 ; Del Toro et al, 2021 ; Oughtred et al, 2021 ). The interaction data is organized in structured formats ( Orchard et al, 2007 ; Porras et al, 2020 ), that can be easily processed and exploited to perform downstream computational and network analyses ( Porras et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

The Intricacy of the Viral-Human Protein Interaction Networks: Resources, Data, and Analyses

et al. 2022

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Viral infections are one of the major causes of human diseases that cause yearly millions of deaths and seriously threaten global health, as we have experienced with the COVID-19 pandemic. Numerous approaches have been adopted to understand viral diseases and develop pharmacological treatments. Among them, the study of virus-host protein-protein interactions is a powerful strategy to comprehend the molecular mechanisms employed by the virus to infect the host cells and to interact with their components. Experimental protein-protein interactions described in the scientific literature have been systematically captured into several molecular interaction databases. These data are organized in structured formats and can be easily downloaded by users to perform further bioinformatic and network studies. Network analysis of available virus-host interactomes allow us to understand how the host interactome is perturbed upon viral infection and what are the key host proteins targeted by the virus and the main cellular pathways that are subverted. In this review, we give an overview of publicly available viral-human protein-protein interactions resources and the community standards, curation rules and adopted ontologies. A description of the main virus-human interactome available is provided, together with the main network analyses that have been performed. We finally discuss the main limitations and future challenges to assess the quality and reliability of protein-protein interaction datasets and resources.

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Using the MINT Database to Search Protein Interactions

Cited by 15 publications

References 13 publications

Protein Arginine Methyltransferase 5 Functions via Interacting Proteins

Protein Arginine Methyltransferase 5 Functions via Interacting Proteins

Quantitative Proteomic Approach Reveals Altered Metabolic Pathways in Response to the Inhibition of Lysine Deacetylases in A549 Cells under Normoxia and Hypoxia

The Intricacy of the Viral-Human Protein Interaction Networks: Resources, Data, and Analyses

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