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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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dc.contributor.authorPirzada, Rameez Hassan-
dc.contributor.authorYasmeen, Farzana-
dc.contributor.authorHaseeb, Muhammad-
dc.contributor.authorJavaid, Nasir-
dc.contributor.authorKim, Eunha-
dc.contributor.authorChoi, Sangdun-
dc.date.issued2024-12-01-
dc.identifier.issn1879-0003-
dc.identifier.urihttps://aurora.ajou.ac.kr/handle/2018.oak/34590-
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85208680306&origin=inward-
dc.description.abstractThe 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.-
dc.description.sponsorshipThis study was supported by the National Research Foundation of Korea under the grants NRF- 2022M3A9G1014520 , 2023R1A2C2003034 , 2019M3D1A1078940 , and 2019R1A6A1A11051471 .-
dc.language.isoeng-
dc.publisherElsevier B.V.-
dc.subject.meshDynamics simulation-
dc.subject.meshInterleukin-1 receptor-
dc.subject.meshInterleukin1-
dc.subject.meshMolecular dynamic simulation-
dc.subject.meshMulti tasks-
dc.subject.meshMulti-task transfer learning-
dc.subject.meshQuantitative structure activity relationship-
dc.subject.meshSmall-molecule inhibitors-
dc.subject.meshTask transfer-
dc.subject.meshTransfer learning-
dc.subject.meshHumans-
dc.subject.meshInterleukin-1beta-
dc.subject.meshMachine Learning-
dc.subject.meshMolecular Docking Simulation-
dc.subject.meshMolecular Dynamics Simulation-
dc.subject.meshProtein Binding-
dc.subject.meshQuantitative Structure-Activity Relationship-
dc.subject.meshReceptors, Interleukin-1 Type I-
dc.subject.meshSmall Molecule Libraries-
dc.titleSmall molecule inhibitors of IL-1R1/IL-1β interaction identified via transfer machine learning QSAR modelling-
dc.typeArticle-
dc.citation.titleInternational Journal of Biological Macromolecules-
dc.citation.volume282-
dc.identifier.bibliographicCitationInternational Journal of Biological Macromolecules, Vol.282-
dc.identifier.doi10.1016/j.ijbiomac.2024.137295-
dc.identifier.pmid39515709-
dc.identifier.scopusid2-s2.0-85208680306-
dc.identifier.urlhttps://www.sciencedirect.com/science/journal/01418130-
dc.subject.keywordInterleukin-1 receptor-
dc.subject.keywordMolecular dynamics simulation-
dc.subject.keywordMulti-task transfer learning-
dc.subject.keywordQuantitative structure-activity relationship-
dc.type.otherArticle-
dc.identifier.pissn0141-8130-
dc.description.isoafalse-
dc.subject.subareaStructural Biology-
dc.subject.subareaBiochemistry-
dc.subject.subareaMolecular Biology-
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