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Approaches for Identification and Validation of Antimicrobial Compounds of Plant Origin: A Long Way from the Field to the Market
Published in Mahendra Rai, Chistiane M. Feitosa, Eco-Friendly Biobased Products Used in Microbial Diseases, 2022
Lívia Maria Batista Vilela, Carlos André dos Santos-Silva, Ricardo Salas Roldan-Filho, Pollyanna Michelle da Silva, Marx de Oliveira Lima, José Rafael da Silva Araújo, Wilson Dias de Oliveira, Suyane de Deus e Melo, Madson Allan de Luna Aragão, Thiago Henrique Napoleão, Patrícia Maria Guedes Paiva, Ana Christina Brasileiro-Vidal, Ana Maria Benko-Iseppon
Some databases have been dedicated to compiling information from these interactions between biomolecules that may come from different types of experiments (Pedamallu and Ozdamar 2014), the best known example of a repository that contains metabolic pathways regards the Kyoto Encyclopedia of Genes and Genomes (Kanehisa et al. 2017). Other databases like STRING are able to map interactions between proteins based on annotations of coexpression, co-occurrence, text mining, mapping of interactions already known by different experiments (Szklarczyk et al. 2017). Like STRING, other databases also compile such information and may add data from genes, and chemical associations, in addition to post-translational modifications; BioGRID is a well-cured repository that presents these characteristics (Oughtred et al. 2019). Another very useful tool to understand the interactions between proteins is IntAct (Kerrien et al. 2011), a database that groups information cured from literature and reports in the form of binary interactions. A thorough analysis of such banks can help understand and analyze the complexity of the different interactions between proteins and metabolic pathways.
Human bronchial-pulmonary proteomics in coronavirus disease 2019 (COVID-19) pandemic: applications and implications
Published in Expert Review of Proteomics, 2021
Heng Wee Tan, Yan-Ming Xu, Andy T. Y. Lau
Interactions between virus and host membrane proteins are crucial for the viral lifecycle. Using BioID proximity labelling technique, St-Germain et al. [97] showed that SARS-CoV-2 proteins predicted with at least one transmembrane domain (spike, envelope, membrane, NSP3, NSP4, NSP6, ORF3A, ORF7A, and ORF7B), along with a few non-membrane-associated but poorly understood proteins (ORF3B, ORF6, ORF8, and ORF9B), were able to interact with a range of host membrane-associated proteins, such as those involved in intracellular vesicle trafficking pathways (e.g. cholesterol and lipid) and endoplasmic reticulum-related membrane contact site (MCS) components. It was proposed that the human MCS lipid transfer system might be an attractive drug target for COVID-19 [97]. Curated data of their work can be found at BioGRID (www.thebiogrid.org) [98], a database that holds over 1.7 million protein, genetic, or chemical interactions obtained from humans and other model organisms.
Protein interactions study through proximity-labeling
Published in Expert Review of Proteomics, 2019
Benoît Béganton, Isabelle Solassol, Alain Mangé, Jérôme Solassol
Several techniques have thus been developed to address this challenge. For many years, in vitro techniques such as affinity purification (AP) or the yeast two-hybrid system have been references in the study of PPIs. Although effective, these conventional techniques appear unsatisfactory. Indeed, these techniques do not retain information about intracellular localization and are designed mostly for soluble proteins. They only identify the strong and permanent interactions and only give a partial image of weak PPIs which can be technically difficult to study and harder to detect. However, yeast two-hybrid system seems to be able to detect transient PPIs [1]. Only direct interactions within high affinity complexes are revealed, thus missing the spatio-temporal dynamics essential to the functioning of these protein complexes. Others mass spectrometry-based approaches are emerging to analysis endogenous protein complexes, either from their native environment after cross-linking of proteins (XL-MS), either from partially purified complexes under native conditions (Protein correlation profiling, PCP) [2–4]. Complementary binary methods are also available for the study of PPIs, such as protein fragment complementation (PCA) or luciferase-mediated interactome (LUMIER) [5,6]. However, there is no one ‘perfect’ method for all situations, and each has its own strengths and weaknesses [7]. Many PPIs have still been identified, many of which are still referenced in databases such as BioGrid or IntAct.
Identification of key miRNA signature and pathways involved in multiple myeloma by integrated bioinformatics analysis
Published in Hematology, 2021
Xiushuai Dong, Gang Lu, Xianwei Su, Jie Liu, Xi Chen, Yaoyao Tian, Yuying Chang, Lianjie Wang, Wei Wang, Jin Zhou
The data of PPI networks described previously [26] were derived from 12 databases, including BioGRID, DFCI_NET_2016, HI-II-network, HPRD, InnateDB, INstruct, IntAct, KinomeNetworkX, MINT, PhosphoSitePlus, PINA and SignaLink2.0. For the robustness of analysis, the interactions between proteins found in at least two data sets were considered as the final PPI networks, which contained 12,512 protein nodes and 83,065 interactions. The sub-network of DEMirTGs targeted by DEMirs involved in the global PPI network was constructed. Cytoscape software version 3.7.1 was applied to establish the sub-network. MCODE, a plug-in in Cytoscape, was utilized to screen the modules from the sub-network and identify the three most significant modules based on the MCODE score and node number.