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

Building shape-focused pharmacophore models for effective docking screening

The performance of molecular docking can be improved by comparing the shape similarity of the flexibly sampled poses against the target proteins' inverted binding cavities. The effectiveness of these pseudo-ligands or negative image-based models in docking rescoring is boosted further by performing enrichment-driven optimization. Here, we introduce a novel shape-focused pharmacophore modeling algorithm O-LAP that generates a new class of cavity-filling models by clumping together overlapping atomic content via pairwise distance graph clustering. Top-ranked poses of flexibly docked active ligands were used as the modeling input and multiple alternative clustering settings were benchmark-tested thoroughly with five demanding drug targets using random training/test divisions. In docking rescoring, the O-LAP modeling typically improved massively on the default docking enrichment; furthermore, the results indicate that the clustered models work well in rigid docking. The C+ +/Qt5-based algorithm O-LAP is released under the GNU General Public License v3.0 via GitHub ( https://github.com/jvlehtonen/overlap-toolkit ). SCIENTIFIC CONTRIBUTION: This study introduces O-LAP, a C++/Qt5-based graph clustering software for generating new type of shape-focused pharmacophore models. In the O-LAP modeling, the target protein cavity is filled with flexibly docked active ligands, the overlapping ligand atoms are clustered, and the shape/electrostatic potential of the resulting model is compared against the flexibly sampled molecular docking poses. The O-LAP modeling is shown to ensure high enrichment in both docking rescoring and rigid docking based on comprehensive benchmark-testing.

Lithocholic acid derivatives as potent modulators of the nuclear receptor RORγt

Retinoic acid receptor-related orphan receptor γt (RORγt) is a nuclear receptor found in various tissues that plays a crucial role in the differentiation and proliferation of T helper 17 (Th17) cells, as well as in their generation of the pro-inflammatory cytokine IL-17A. RORγt represents a promising therapeutic target for autoimmune diseases, metabolic disorders, and multiple tumors. Despite extensive research efforts focused on the development of small molecule RORγt modulators, no drug candidates have advanced to phase 3 clinical trials owing to a lack of efficacy or safety margin. This outcome highlights the unmet need to optimize small molecule drug candidates targeting RORγt to develop effective therapies for autoimmune and inflammatory diseases. In this study, we synthesized and evaluated 3-oxo-lithocholic acid amidates as a new class of RORγt modulators. Our evaluation entailed biophysical screening, cellular screening in different platforms, molecular docking, and in vitro pharmacokinetic profiling. The top compound from our study (3-oxo-lithocholic acid amidate, A2) binds to RORγt at an equilibrium dissociation constant (KD) of 16.5 ± 1.34 nM based on microscale thermophoresis (MST). Assessment of the efficacy of A2 in the cellular RORγt reporter luciferase assay revealed a half-maximal inhibitory concentration (IC50) value of 225 ± 10.4 nM. Unlike 3-oxo-lithocholic acid, A2 demonstrated the ability to reduce the IL-17A mRNA expression levels in EL4 cells with RORγt expression using quantitative reverse transcriptase PCR (RT-PCR). Validation of the desirable physicochemical properties and stability of A2 sets the stage for the preclinical evaluation of this new class of RORγt modulators in animal models of autoimmune diseases.

Knockdown of USF2 inhibits pyroptosis of podocytes and attenuates kidney injury in lupus nephritis

As an essential factor in the prognosis of Systemic lupus erythematosus (SLE), lupus nephritis (LN) can accelerate the rate at which patients with SLE can transition to chronic kidney disease or even end-stage renal disease (ESRD). Proteinuria due to decreased glomerular filtration rate following podocyte injury is LN's most common clinical manifestation. Podocyte pyroptosis and related inflammatory factors in its process can promote lupus to involve kidney cells and worsen the occurrence and progression of LN, but its regulatory mechanism remains unknown. Accumulating evidence has shown that upstream stimulatory factor 2 (USF2) plays a vital role in the pathophysiology of kidney diseases. In this research, multiple experiments were performed to investigate the role of USF2 in the process of LN. USF2 was abnormally highly expressed in MRL/lpr mice kidney tissues. Renal function impairment and USF2 mRNA levels were positively correlated. Silencing of USF2 in MRL/lpr serum-stimulated cells significantly reduced serum-induced podocyte pyroptosis. USF2 enhanced NLRP3 expression at the transcriptional level. Silencing of USF2 in vivo attenuated kidney injury in MRL/lpr mice, which suggests that USF2 is important for LN development and occurrence.

Discovery, Synthesis, and In Vitro Characterization of 2,3 Derivatives of 4,5,6,7-Tetrahydro-Benzothiophene as Potent Modulators of Retinoic Acid Receptor-Related Orphan Receptor γt

Retinoic acid receptor-related orphan receptor γt (RORγt) is a nuclear receptor that is expressed in a variety of tissues and is a potential drug target for the treatment of inflammatory and auto-immune diseases, metabolic diseases, and resistant cancer types. We herein report the discovery of 2,3 derivatives of 4,5,6,7-tetrahydro-benzothiophene modulators of RORγt. We also report the solubility in acidic/neutral pH, mouse/human/dog/rat microsomal stability, Caco-2, and MDR1-MDCKII permeabilities of a set of these derivatives. For this group of modulators, inverse agonism by steric clashes and push-pull mechanisms induce greater instability to protein conformation compared to agonist lock hydration. Independent of the two mechanisms, we observed a basal modulatory activity of the tested 2,3 derivatives of 4,5,6,7-tetrahydro-benzothiophene toward RORγt due to the interactions with the Cys320-Glu326 and Arg364-Phe377 hydrophilic regions. The drug discovery approach reported in the current study can be employed to discover modulators of nuclear receptors and other globular protein targets.

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

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%.

Review of in silico studies dedicated to the nuclear receptor family: Therapeutic prospects and toxicological concerns

Being in the center of both therapeutic and toxicological concerns, NRs are widely studied for drug discovery application but also to unravel the potential toxicity of environmental compounds such as pesticides, cosmetics or additives. High throughput screening campaigns (HTS) are largely used to detect compounds able to interact with this protein family for both therapeutic and toxicological purposes. These methods lead to a large amount of data requiring the use of computational approaches for a robust and correct analysis and interpretation. The output data can be used to build predictive models to forecast the behavior of new chemicals based on their in vitro activities. This atrticle is a review of the studies published in the last decade and dedicated to NR ligands in silico prediction for both therapeutic and toxicological purposes. Over 100 articles concerning 14 NR subfamilies were carefully read and analyzed in order to retrieve the most commonly used computational methods to develop predictive models, to retrieve the databases deployed in the model building process and to pinpoint some of the limitations they faced.

Sdfconf: A Novel, Flexible, and Robust Molecular Data Management Tool

Projects in chemo- and bioinformatics often consist of scattered data in various types and are difficult to access in a meaningful way for efficient data analysis. Data is usually too diverse to be even manipulated effectively. Sdfconf is data manipulation and analysis software to address this problem in a logical and robust manner. Other software commonly used for such tasks are either not designed with molecular and/or conformational data in mind or provide only a narrow set of tasks to be accomplished. Furthermore, many tools are only available within commercial software packages. Sdfconf is a flexible, robust, and free-of-charge tool for linking data from various sources for meaningful and efficient manipulation and analysis of molecule data sets. Sdfconf packages molecular structures and metadata into a complete ensemble, from which one can access both the whole data set and individual molecules and/or conformations. In this software note, we offer some practical examples of the utilization of sdfconf.

Negative Image-Based Screening: Rigid Docking Using Cavity Information

Rational drug discovery relies heavily on molecular docking-based virtual screening, which samples flexibly the ligand binding poses against the target protein's structure. The upside of flexible docking is that the geometries of the generated docking poses are adjusted to match the residue alignment inside the target protein's ligand-binding pocket. The downside is that the flexible docking requires plenty of computing resources and, regardless, acquiring a decent level of enrichment typically demands further rescoring or post-processing. Negative image-based screening is a rigid docking technique that is ultrafast and computationally light but also effective as proven by vast benchmarking and screening experiments. In the NIB screening, the target protein cavity's shape/electrostatics is aligned and compared against ab initio-generated ligand 3D conformers. In this chapter, the NIB methodology is explained at the practical level and both its weaknesses and strengths are discussed candidly.

Getting Docking into Shape Using Negative Image-Based Rescoring

The failure of default scoring functions to ensure virtual screening enrichment is a persistent problem for the molecular docking algorithms used in structure-based drug discovery. To remedy this problem, elaborate rescoring and postprocessing schemes have been developed with a varying degree of success, specificity, and cost. The negative image-based rescoring (R-NiB) has been shown to improve the flexible docking performance markedly with a variety of drug targets. The yield improvement is achieved by comparing the alternative docking poses against the negative image of the target protein's ligand-binding cavity. In other words, the shape and electrostatics of the binding pocket is directly used in the similarity comparison to rank the explicit docking poses. Here, the PANTHER/ShaEP-based R-NiB methodology is tested with six popular docking softwares, including GLIDE, PLANTS, GOLD, DOCK, AUTODOCK, and AUTODOCK VINA, using five validated benchmark sets. Overall, the results indicate that R-NiB outperforms the default docking scoring consistently and inexpensively, demonstrating that the methodology is ready for wide-scale virtual screening usage.

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

A novel virtual screening methodology called fragment- and negative image-based (F-NiB) screening is introduced and tested experimentally using phosphodiesterase 10A (PDE10A) as a case study. Potent PDE10A-specific small-molecule inhibitors are actively sought after for their antipsychotic and neuroprotective effects. The F-NiB combines features from both fragment-based drug discovery and negative image-based (NIB) screening methodologies to facilitate rational drug discovery. The selected structural parts of protein-bound ligand(s) are seamlessly combined with the negative image of the target's ligand-binding cavity. This cavity- and fragment-based hybrid model, namely its shape and electrostatics, is used directly in the rigid docking of ab initio generated ligand 3D conformers. In total, 14 compounds were acquired using the F-NiB methodology, 3D quantitative structure-activity relationship modeling, and pharmacophore modeling. Three of the small molecules inhibited PDE10A at ~27 to ~67 μM range in a radiometric assay. In a larger context, the study shows that the F-NiB provides a flexible way to incorporate small-molecule fragments into the drug discovery.

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

Negative image-based (NIB) screening is a rigid molecular docking methodology that can also be employed in docking rescoring. During the NIB screening, a negative image is generated based on the target protein’s ligand-binding cavity by inverting its shape and electrostatics. The resulting NIB model is a drug-like entity or pseudo-ligand that is compared directly against ligand 3D conformers, as is done with a template compound in the ligand-based screening. This cavity-based rigid docking has been demonstrated to work with genuine drug targets in both benchmark testing and drug candidate/lead discovery. Firstly, the study explores in-depth the applicability of different ligand 3D conformer generation software for acquiring the best NIB screening results using cyclooxygenase-2 (COX-2) as the example system. Secondly, the entire NIB workflow from the protein structure preparation, model build-up, and ligand conformer generation to the similarity comparison is performed for COX-2. Accordingly, hands-on instructions are provided on how to employ the NIB methodology from start to finish, both with the rigid docking and docking rescoring using noncommercial software. The practical aspects of the NIB methodology, especially the effect of ligand conformers, are discussed thoroughly, thus, making the methodology accessible for new users.