Discovery of novel DprE1 inhibitors via computational bioactivity fingerprints and structure-based virtual screening

Structure-activity relationship mediated molecular insights of DprE1 inhibitors: A Comprehensive Review

Emerging threats of multi-drug resistant (MDR), extensively drug-resistant (XDR), and totally drug-resistant (TDR) tuberculosis led to the discovery of a novel target which was entitled Decaprenylphosphoryl-β-D-ribose 2'-epimerase (DprE1) enzyme. DprE1 is composed of two isoforms, decaprenylphosphoryl-β-D-ribose oxidase (DprE1) and decaprenylphosphoryl-D-2-keto erythro pentose reductase (DprE2). The enzymes, DprE1 and DprE2, regulate the two-step epimerization process to form DPA (Decaprenylphosphoryl arabinose) from DPX (Decaprenylphosphoryl-D-ribose), which is the sole precursor in the cell wall synthesis of arabinogalactan (AG) and lipoarabinomannan (LAM). Target-based and whole-cell-based screening played an imperative role in the identification of the druggable target, DprE1, whereas the druggability of the DprE2 enzyme is not proved yet. To date, diverse scaffolds of heterocyclic and aromatic ring systems have been reported as DprE1 inhibitors based on their interaction mode, i.e. covalent, and non-covalent inhibitors. This review describes the structure-activity relationship (SAR) of reported covalent and non-covalent inhibitors to enlighten about the crucial pharmacophoric features required for DprE1 inhibition, along with in-silico studies which characterize the amino acid residues responsible for covalent and non-covalent interactions.Communicated by Ramaswamy H. Sarma.

Recent advances in the development of DprE1 inhibitors using AI/CADD approaches

Tuberculosis (TB) is a global lethal disease caused by Mycobacterium tuberculosis (Mtb). The flavoenzyme decaprenylphosphoryl-β-d-ribose 2'-oxidase (DprE1) plays a crucial part in the biosynthesis of lipoarabinomannan and arabinogalactan for the cell wall of Mtb and represents a promising target for anti-TB drug development. Therefore, there is an urgent need to discover DprE1 inhibitors with novel scaffolds, improved bioactivity and high drug-likeness. Recent studies have shown that artificial intelligence/computer-aided drug design (AI/CADD) techniques are powerful tools in the discovery of novel DprE1 inhibitors. This review provides an overview of the discovery of DprE1 inhibitors and their underlying mechanism of action and highlights recent advances in the discovery and optimization of DprE1 inhibitors using AI/CADD approaches.

Discovery of N-(1-(6-Oxo-1,6-dihydropyrimidine)-pyrazole) Acetamide Derivatives as Novel Noncovalent DprE1 Inhibitors against Mycobacterium tuberculosis

Decaprenylphosphoryl-β-d-ribose oxidase (DprE1) is a promising target for treating tuberculosis (TB). Currently, most novel DprE1 inhibitors are discovered through high-throughput screening, while computer-aided drug design (CADD) strategies are expected to promote the discovery process. In this study, with the aid of structure-based virtual screening and computationally guided design, a series of novel scaffold N-(1-(6-oxo-1,6-dihydropyrimidine)-pyrazole) acetamide derivatives with significant antimycobacterial activities were identified. Among them, compounds LK-60 and LK-75 are capable of effectively suppressing the proliferation of Mtb with MICMtb values of 0.78-1.56 μM, comparable with isoniazid and much superior to the phase II candidate TBA-7371 (MICMtb = 12.5 μM). LK-60 is also the most active DprE1 inhibitor derived from CADD so far. Further studies confirmed their high affinity to DprE1, good safety profiles to gut microbiota and human cells, and synergy effects with either rifampicin or ethambutol, indicating their broad potential for clinical applications.

Novel Derivatives of Quinoxaline-2-carboxylic Acid 1,4-Dioxides as Antimycobacterial Agents: Mechanistic Studies and Therapeutic Potential

The World Health Organization (WHO) reports that tuberculosis (TB) is one of the top 10 leading causes of global mortality. The increasing incidence of multidrug-resistant TB highlights the urgent need for an intensified quest to discover innovative anti-TB medications In this study, we investigated four new derivatives from the quinoxaline-2-carboxylic acid 1,4-dioxide class. New 3-methylquinoxaline 1,4-dioxides with a variation in substituents at positions 2 and 6(7) were synthesized via nucleophilic aromatic substitution with amines and assessed against a Mycobacteria spp. Compound 4 showed high antimycobacterial activity (1.25 μg/mL against M. tuberculosis) and low toxicity in vivo in mice. Selection and whole-genomic sequencing of spontaneous drug-resistant M. smegmatis mutants revealed a high number of single-nucleotide polymorphisms, confirming the predicted mode of action of the quinoxaline-2-carboxylic acid 1,4-dioxide 4 as a DNA-damaging agent. Subsequent reverse genetics methods confirmed that mutations in the genes MSMEG_4646, MSMEG_5122, and MSMEG_1380 mediate resistance to these compounds. Overall, the derivatives of quinoxaline-2-carboxylic acid 1,4-dioxide present a promising scaffold for the development of innovative antimycobacterial drugs.

MetalProGNet: a structure-based deep graph model for metalloprotein-ligand interaction predictions

Metalloproteins play indispensable roles in various biological processes ranging from reaction catalysis to free radical scavenging, and they are also pertinent to numerous pathologies including cancer, HIV infection, neurodegeneration, and inflammation. Discovery of high-affinity ligands for metalloproteins powers the treatment of these pathologies. Extensive efforts have been made to develop in silico approaches, such as molecular docking and machine learning (ML)-based models, for fast identification of ligands binding to heterogeneous proteins, but few of them have exclusively concentrated on metalloproteins. In this study, we first compiled the largest metalloprotein-ligand complex dataset containing 3079 high-quality structures, and systematically evaluated the scoring and docking powers of three competitive docking tools (i.e., PLANTS, AutoDock Vina and Glide SP) for metalloproteins. Then, a structure-based deep graph model called MetalProGNet was developed to predict metalloprotein-ligand interactions. In the model, the coordination interactions between metal ions and protein atoms and the interactions between metal ions and ligand atoms were explicitly modelled through graph convolution. The binding features were then predicted by the informative molecular binding vector learned from a noncovalent atom-atom interaction network. The evaluation on the internal metalloprotein test set, the independent ChEMBL dataset towards 22 different metalloproteins and the virtual screening dataset indicated that MetalProGNet outperformed various baselines. Finally, a noncovalent atom-atom interaction masking technique was employed to interpret MetalProGNet, and the learned knowledge accords with our understanding of physics.

Recent Advances of DprE1 Inhibitors against Mycobacterium tuberculosis: Computational Analysis of Physicochemical and ADMET Properties

Decaprenylphosphoryl-β-d-ribose 2'-epimerase (DprE1) is a critical flavoenzyme in Mycobacterium tuberculosis, catalyzing a vital step in the production of lipoarabinomannan and arabinogalactan, both of which are essential for cell wall biosynthesis. Due to its periplasmic localization, DprE1 is a susceptible target, and several compounds with diverse scaffolds have been discovered that inhibit this enzyme, covalently or noncovalently. We evaluated a total of ∼1519 DprE1 inhibitors disclosed in the literature from 2009 to April 2022 by performing an in-depth analysis of physicochemical descriptors and absorption, distribution, metabolism, excretion, and toxicity (ADMET), to gain new insights into these properties in DprE1 inhibitors. Several molecular properties that should facilitate the design and optimization of future DprE1 inhibitors are described, allowing for the development of improved analogues targeting M. tuberculosis.

Discovery of novel non-steroidal selective glucocorticoid receptor modulators by structure- and IGN-based virtual screening, structural optimization, and biological evaluation

Glucocorticoids (GCs) are the most commonly used anti-inflammatory drugs. However, their excellent therapeutic effects are often accompanied by undesirable side effects. To discover selective glucocorticoid receptor modulators (SGRMs) that preferentially induce transrepression with little or no transactivation activity, a structure-based virtual screening by combining molecular docking and InteractionGraphNet (IGN) rescoring was performed, and compound HP210 was identified. HP210 did not induce the transactivation functions of GR while still acted on the NF-κB mediated tethered transrepression function (IC₅₀ = 2.32 μM), and suppressed the secretion of pro-inflammation cytokines IL-1β and IL-6. Compared with dexamethasone, HP210 showed no cross activities with phylogenetically related mineralcorticoid receptor and progesterone receptor and no significant effect on osteoprotegerin, exhibiting a reduced side-effect profile. Then, guided by the molecular dynamics simulations and binding free energy calculations, compound HP210_b4 with over two-fold higher transrepression activity (IC₅₀ = 0.99 μM) was discovered. This study reported a group of non-steroidal new-scaffold SGRMs, providing valuable clues for the development of novel anti-inflammatory drugs.