Virtual Screening Strategy to Identify Retinoic Acid-Related Orphan Receptor γt Modulators

• Published in: Molecules (Basel, Switzerland) Vol. 28; no. 8; p. 3420
• Main Authors: Jokinen, Elmeri M, Niemeläinen, Miika, Kurkinen, Sami T, Lehtonen, Jukka V, Lätti, Sakari, Postila, Pekka A, Pentikäinen, Olli T, Niinivehmas, Sanna P
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.04.2023; MDPI
• Subjects: Algorithms; Analysis; Arthritis; Binding; brute force negative image-based optimization (BR-NiB); Case studies; Complex formation; docking rescoring; Drug Discovery; Electrostatic properties; Encephalomyelitis; Free energy; Genes; Greedy algorithms; In vitro methods and tests; Inflammation; Inflammatory diseases; Ligands; Metabolism; Molecular docking; Molecular Docking Simulation; Multiple sclerosis; negative image-based rescoring (R-NiB); Nuclear Receptor Subfamily 1, Group F, Member 3; Optimization; Peptides; pharmacophore (PHA) filtering; Proteins; Psoriasis; Receptors, Retinoic Acid; Retinoic acid; retinoic acid receptor-related orphan receptor gamma t (RORγt); Skin diseases; Transcription Factors; Tretinoin; New York; Finland; negative image-based rescoring (R-NiB); docking rescoring; molecular docking; inflammation; pharmacophore (PHA) filtering; retinoic acid receptor-related orphan receptor gamma t (RORγt); brute force negative image-based optimization (BR-NiB); virtual screening (VS)
• ISSN: 1420-3049; 1420-3049
• DOI: 10.3390/molecules28083420

Molecular docking is a key method used in virtual screening (VS) campaigns to identify small-molecule ligands for drug discovery targets. While docking provides a tangible way to understand and predict the protein-ligand complex formation, the docking algorithms are often unable to separate active ligands from inactive molecules in practical VS usage. Here, a novel docking and shape-focused pharmacophore VS protocol is demonstrated for facilitating effective hit discovery using retinoic acid receptor-related orphan receptor gamma t (RORγt) as a case study. RORγt is a prospective target for treating inflammatory diseases such as psoriasis and multiple sclerosis. First, a commercial molecular database was flexibly docked. Second, the alternative docking poses were rescored against the shape/electrostatic potential of negative image-based (NIB) models that mirror the target's binding cavity. The compositions of the NIB models were optimized via iterative trimming and benchmarking using a greedy search-driven algorithm or brute force NIB optimization. Third, a pharmacophore point-based filtering was performed to focus the hit identification on the known RORγt activity hotspots. Fourth, free energy binding affinity evaluation was performed on the remaining molecules. Finally, twenty-eight compounds were selected for in vitro testing and eight compounds were determined to be low μM range RORγt inhibitors, thereby showing that the introduced VS protocol generated an effective hit rate of ~29%.