Identification of novel inhibitors targeting TIRAP interactions with BTK and PKCδ in inflammation through an in silico approach

ABSTRACT

Advanced computational tools focusing on protein–protein interaction (PPI) based drug development is a powerful platform to accelerate the therapeutic development of small lead molecules and repurposed drugs. Toll/interleukin-1 receptor (TIR) domain-containing adapter protein (TIRAP) and its interactions with other proteins in macrophages signalling are crucial components of severe or persistent inflammation. TIRAP activation through Bruton’s tyrosine kinase (BTK) and Protein Kinase C delta (PKCδ) is essential for downstream inflammatory signalling. We created homology-based structural models of BTK and PKCδ in MODELLER 9.24. TIRAP interactions with BTK and PKCδ in its non-phosphorylated and phosphorylated states were determined by multiple docking tools including HADDOCK 2.4, pyDockWEB and ClusPro 2.0. Food and Drug Administration (FDA)-approved drugs were virtually screened through Discovery Studio LibDock and Autodock Vina tools to target the common TIR domain residues of TIRAP, which interact with both BTK and PKC at the identified interfacial sites of the complexes. Four FDA-approved drugs were identified and found to have stable interactions over a range of 100 ns MD simulation timescales. These drugs block the interactions of both kinases with TIRAP in silico. Hence, these drugs have the potential to dampen downstream inflammatory signalling and inflammation-mediated disease.

KEYWORDS:

• PPIs
• homology modelling
• structural analysis
• TIRAP
• BTK
• PKCδ
• macrophages
• repurposed drugs
• inflammatory responses

Acknowledgements

The authors gratefully acknowledge the Indian Institute of Technology Indore (IITI) for providing facilities and other support. Senior Research Fellowship (SRF) from Council of Scientific and Industrial Research (CSIR) to SR.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed at: https://doi.org/10.1080/1062936X.2022.2035817

Additional information

Funding

This work was supported by the Cumulative Professional DevelopmentAllowance (CPDA) and Research Development Fund (RDF) from the Indian Institute of TechnologyIndore (IITI) to MSB.