Literature DB >> 32438115

Rational molecular targeting of the inter-subunit interaction between human cardiac troponin hcTnC and hcTnI using switch peptide-competitive biogenic medicines.

Danrui Xiao1, Zixun Fan1, Wu Jiaqi1, Hua Liu1, Linghong Shen1, Ben He1, Min Zhang2.   

Abstract

The human cardiac troponin (hcTn) has been implicated in diverse cardiovascular diseases (CDs). The protein function is regulated by the inter-subunit interaction between the N-terminal domain of hcTnC and the C-terminal switch peptide of hcTnI; disruption of the interaction has been recognized as a potential therapeutic strategy for CDs. Here, we report use of biogenic medicines as small-molecule competitors to directly disrupt the protein-protein interaction by competitively targeting the core binding site (CBS) of hcTnC NTD domain. A multistep virtual screening protocol is performed against a biogenic compound library to identify competitor candidates and competition assay is employed to verify the screening results. Consequently, two compounds Collismycin and Compound e are identified as strong competitors (CC50 < 10 μM) with hcTnI for hcTnC CBS site, while other tested compounds are found to have moderate (CC50 = 10-100 μM), low (CC50 > 100 μM) or no (CC50 = N.D.) potency. The competitor ligands are anchored at the core groove of hcTnC CBS site through aromatic and hydrophobic interactions, while few peripheral hydrogen bonds are formed to further confer specificity for domain-compound recognition. These molecular-level findings would benefit from further in vitro and in vivo studies at cellular and animal levels, which can help to practice the ultimate therapeutic purpose.
Copyright © 2020 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Biogenic; Cardiovascular disease; Collismycin; Human cardiac troponin; Switch peptide; Virtual screening; medicine

Year:  2020        PMID: 32438115     DOI: 10.1016/j.compbiolchem.2020.107272

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


  1 in total

1.  Predicting cell-penetrating peptides using machine learning algorithms and navigating in their chemical space.

Authors:  Ewerton Cristhian Lima de Oliveira; Kauê Santana; Luiz Josino; Anderson Henrique Lima E Lima; Claudomiro de Souza de Sales Júnior
Journal:  Sci Rep       Date:  2021-04-07       Impact factor: 4.379

  1 in total

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