Discovery of Retinoic Acid-Related Orphan Receptor γt Inverse Agonists via Docking and Negative Image-Based Screening

Abstract

Retinoic acid-related orphan receptor γt (RORγt) has a vital role in the differentiation of T-helper 17 (TH17) cells. Potent and specific RORγt inverse agonists are sought for treating TH17-related diseases such as psoriasis, rheumatoid arthritis, and type 1 diabetes. Here, the aim was to discover novel RORγt ligands using both standard molecular docking and negative image-based screening. Interestingly, both of these in silico techniques put forward mostly the same compounds for experimental testing. In total, 11 of the 34 molecules purchased for testing were verified as RORγt inverse agonists, thus making the effective hit rate 32%. The pIC50 values for the compounds varied from 4.9 (11 μM) to 6.2 (590 nM). Importantly, the fact that the verified hits represent four different cores highlights the structural diversity of the RORγt inverse agonism and the ability of the applied screening methodologies to facilitate much-desired scaffold hopping for drug design.

Getting Docking into Shape Using Negative Image-Based Rescoring

“…The AUC and EFd values of the shape and ESP similarity scores were calculated separately for each test set and individual docking solution using R-NiB (Table S5). Here and in the other NIB studies, 21,22,31,54 the best shape similarity score reported for a molecule was typically two times higher in comparison to the best ESP similarity score. As a result, the impact of the shape similarity score on the total score (shape + ESP) has a major role with rescoring.…”

“…The negative image-based rescoring (R-NiB; Figure 1) provides a tangible way to address this issue in the docking-based virtual screening assays. 21,22,31…”

“…The negative imagebased rescoring (R-NiB; Figure 1) provides a tangible way to address this issue in the docking-based virtual screening assays. 21,22,31 Negative images are an intuitive way to abstract and visualize the shape/electrostatics features of the ligand-binding pockets/ cavities, grooves, or even tunnels present in the protein 3D structures (Figure 2). Therefore, various cavity detection or analysis algorithms such as VOIDOO/FLOOD, 68 FPOCK-ET, 69 CAVER, 70 POVME, 71−73 and SITEMAP 74,75 have been developed to perform cavity visualization or druggability, accessibility, size/volume, or flexibility estimation.…”

A Practical Perspective: The Effect of Ligand Conformers on the Negative Image-Based Screening

“…The methodology is especially suitable for the targets with well-defined cavities such as nuclear receptors, but, in practice, even a sub-cavity or a shallow groove can be used to build an effective negative image. As such, the NIB has been used to assist the structure-activity relationship analysis of the 3-phenylcoumarin analog series with the 17-hydroxysteroid dehydrogenase 1 [9], monoamine oxidase B [10], and UDP-glucuronosyltransferase 1A10 [11], as well as to facilitate the discovery of novel estrogen receptor α ligands [12] and retinoic acid-related orphan receptor γ(t) inverse agonists [13].…”

Fragment‐ and negative image‐based screening of phosphodiesterase 10A inhibitors

“…The NIB benefits especially from the Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) postprocessing, which, in general, has been shown to be an efficient way to recognize correct binding poses of small‐molecule ligands (Ahinko, Niinivehmas, Jokinen, & Pentikäinen, ; Niinivehmas et al., ). The NIB has also been paired with the PHA filtering, where specific PHA points are generated based on well‐known ligand‐receptor hot spots or interaction sites (Rauhamäki et al., ). Moreover, the negative images can be used to rescore explicit docking poses to improve the flexible docking enrichment—a setup that works excellently with a multitude of targets based on the benchmark testing (Kurkinen et al., ).…”