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Discovery of natural RORγt inhibitor using machine learning, virtual screening, and in vivo validation
Hojin Yoo1, Sang-Jun Han2, Jeong-Eun Lee3
1Bionsight, Inc., Chuncheon 24341, South Korea.
Introduction:
Retinoic acid receptor-related orphan receptor gamma t (RORγt) is a crucial transcription factor regulating Th17 cells, which secrete the cytokine IL-17. RORγt inhibitors are regarded as a therapeutic modality in a wide range of autoimmunity including psoriasis.
Objectives:
The objective of the study is to investigate novel RORγt inhibitors from natural products (NPs), combining machine learning (ML)-based virtual screening, chemotaxonomic analysis, molecular docking, and molecular dynamics simulations, and biological validation.
Methods:
This study employed an integrated approach combining ML-based ligand-based screening, docking study, molecular simulation, and chemotaxonomic analysis to identify RORγt inhibitors from NPs.
Results:
ML ensemble models predicted potential RORγt inhibitors from an NP library; subsequent chemotaxonomic classification of top-ranked hits prioritized protoberberine alkaloids. Six protoberberine alkaloids, which are predicted to bind RORγt via docking studies, were selected for experimental validation. Among them, berberine (Ber) and coptisine (Cop) potently inhibited Th17 differentiation in vitro. Surface plasmon resonance analysis demonstrated that both Ber and Cop directly bind to RORγt, with Cop exhibiting a stronger affinity for RORγt than Ber. Moreover, Cop demonstrated therapeutic efficacy in a preclinical mouse model of psoriasis. These results validate an integrated workflow, combining ML, chemotaxonomy, and experimental testing in vitro and in vivo, for the efficient discovery of novel RORγt inhibitors.
Insights
Novel natural product inhibitors of Retinoic acid receptor-related orphan receptor gamma t (RORγt) were identified using machine learning and experimental validation. Coptisine showed potent inhibition of Th17 cells and efficacy in a psoriasis mouse model.
Area of Science:
- Pharmacology
- Computational Chemistry
- Immunology
Background:
- Retinoic acid receptor-related orphan receptor gamma t (RORγt) is a key regulator of Th17 cells and IL-17 secretion.
- RORγt inhibitors represent a promising therapeutic strategy for autoimmune diseases like psoriasis.
Purpose of the Study:
- To discover novel RORγt inhibitors from natural products (NPs).
- To employ an integrated approach combining machine learning (ML), virtual screening, molecular docking, and biological validation.
Main Methods:
- Utilized ML-based virtual screening and ligand-based screening on an NP library.
- Performed molecular docking and molecular dynamics simulations for hit prioritization.
- Conducted chemotaxonomic analysis to classify top-ranked compounds.
- Validated findings through in vitro inhibition of Th17 differentiation and in vivo efficacy in a psoriasis model.
Main Results:
- ML models identified protoberberine alkaloids as potential RORγt inhibitors.
- Berberine and coptisine demonstrated potent inhibition of Th17 differentiation in vitro.
- Coptisine exhibited stronger binding affinity to RORγt than berberine.
- Coptisine showed therapeutic efficacy in a preclinical mouse model of psoriasis.
Conclusions:
- An integrated workflow combining ML, chemotaxonomy, and experimental validation is effective for discovering RORγt inhibitors.
- Protoberberine alkaloids, particularly coptisine, are promising candidates for treating RORγt-mediated autoimmune diseases.
