Literature DB >> 28869835

Inhibition of TRAF6-Ubc13 interaction in NFkB inflammatory pathway by analyzing the hotspot amino acid residues and protein-protein interactions using molecular docking simulations.

Ria Biswas1, Angshuman Bagchi2.   

Abstract

Protein-protein interactions (PPIs) are important in most of the biochemical processes. Hotspot amino acid residues in proteins are the most important contributors for proper protein-protein interactions. Hotspot amino acid residues have been looked down upon as important therapeutic targets in inhibiting PPIs. Interaction between TRAF6 and Ubc13 is a crucial point in the NFkB inflammatory pathway. Dysfunction of the NFkB pathway is associated with numerous human diseases including cancer and neurodenegeration disorders. Ubc13 also interacts specifically to TRAF6 and not with other proteins of the TRAF family and this makes the TRAF6-Ubc13 complex an important target for specific inhibition. Hence, interfering with the TRAF6-Ubc13 association may prove effective in suppressing the NFkB disease pathway. In the present study, we searched the TRAF6-Ubc13 interaction interface to analyze their binding hotspot amino acid residues using various computational techniques. Heterocyclic compounds are known for their medicinal properties. We screened for heterocyclic analogues to the known TRAF6 inhibitor PDTC, to predict a better inhibitor using in silico protein-ligand and protein-protein interaction studies. Our in silico prediction results suggest that tetrahydro-2-thiophenecarbothioamide (Chemspider ID 36027528) binds one of the major hot-spot residues of TRAF6-Ubc13 interface and can be a better alternative in suppressing TRA6-Ubc13 complex formation in chronic inflammation than PDTC.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Hotspot; Molecular docking; PDTC; Protein–protein interaction; TRAF6; Ubc13

Mesh:

Substances:

Year:  2017        PMID: 28869835     DOI: 10.1016/j.compbiolchem.2017.08.014

Source DB:  PubMed          Journal:  Comput Biol Chem        ISSN: 1476-9271            Impact factor:   2.877


  3 in total

1.  Identifying Protein Features and Pathways Responsible for Toxicity Using Machine Learning and Tox21: Implications for Predictive Toxicology.

Authors:  Lama Moukheiber; William Mangione; Mira Moukheiber; Saeed Maleki; Zackary Falls; Mingchen Gao; Ram Samudrala
Journal:  Molecules       Date:  2022-05-08       Impact factor: 4.927

2.  High expression of IL-1β and NFκB in tumor tissue predicts a low recurrence rate of hepatocellular carcinoma.

Authors:  Bangxu Yu; Aiqin Li; Mao Zhang; Haoran Li; Cheng Tao; Bing Han
Journal:  Int J Clin Exp Pathol       Date:  2018-12-01

Review 3.  Targeting TRIM Proteins: A Quest towards Drugging an Emerging Protein Class.

Authors:  Francesca D'Amico; Rishov Mukhopadhyay; Huib Ovaa; Monique P C Mulder
Journal:  Chembiochem       Date:  2021-03-18       Impact factor: 3.164

  3 in total

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