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Small molecule inhibitors of IL-1R1/IL-1β interaction identified via transfer machine learning QSAR modelling
  • Pirzada, Rameez Hassan ;
  • Yasmeen, Farzana ;
  • Haseeb, Muhammad ;
  • Javaid, Nasir ;
  • Kim, Eunha ;
  • Choi, Sangdun
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Publication Year
2024-12-01
Journal
International Journal of Biological Macromolecules
Publisher
Elsevier B.V.
Citation
International Journal of Biological Macromolecules, Vol.282
Keyword
Interleukin-1 receptorMolecular dynamics simulationMulti-task transfer learningQuantitative structure-activity relationship
Mesh Keyword
Dynamics simulationInterleukin-1 receptorInterleukin1Molecular dynamic simulationMulti tasksMulti-task transfer learningQuantitative structure activity relationshipSmall-molecule inhibitorsTask transferTransfer learningHumansInterleukin-1betaMachine LearningMolecular Docking SimulationMolecular Dynamics SimulationProtein BindingQuantitative Structure-Activity RelationshipReceptors, Interleukin-1 Type ISmall Molecule Libraries
All Science Classification Codes (ASJC)
Structural BiologyBiochemistryMolecular Biology
Abstract
The human interleukin-1 receptor I (IL-1R1) is a cytokine receptor recognized by interleukin 1β (IL-1β), among other cytokines. Over activation of IL-1R1 has been implicated in various inflammatory conditions. This research aims to identify small-molecule inhibitors targeting the hIL1R1/IL1β interaction, employing a multi-task transfer learning approach for quantitative structure-activity relationship (QSAR) modelling. A comprehensive bioactivity dataset from functionally related proteins was utilised to build a robust ensemble machine learning model for predicting IC50 values against the target protein. Despite the availability of antibody-based therapies, the absence of orally available small-molecule inhibitors necessitates their development. By combining model predictions with docking and simulation approaches, the interleukin-1 receptor inhibitor (IRI-1) emerged as a lead compound. It potently inhibited human IL1-R1 with micromolar activity in THP-1 and Saos-2 cells and demonstrated good biocompatibility. Western blot analysis revealed that IRI-1 inhibits IL-1β-mediated phosphorylation of IL1-R1, JNK, IRAK-4, and ERK in THP-1 cells. Furthermore, molecular dynamics simulations confirmed the structural stability of the protein-ligand complexes. This study highlights the effectiveness of multi-task transfer learning approaches for building robust QSAR models against novel proteins or those with limited bioactivity data, such as hIL-1β/IL-1R1 protein.
ISSN
1879-0003
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/34590
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85208680306&origin=inward
DOI
https://doi.org/10.1016/j.ijbiomac.2024.137295
Journal URL
https://www.sciencedirect.com/science/journal/01418130
Type
Article
Funding
This study was supported by the National Research Foundation of Korea under the grants NRF- 2022M3A9G1014520 , 2023R1A2C2003034 , 2019M3D1A1078940 , and 2019R1A6A1A11051471 .
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Kim, Eun ha김은하
College of Bio-convergence Engineering
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