Ligand-based G Protein Coupled Receptor pharmacophore modeling: Assessing the role of ligand function in model development

D, Chakravarty M, Jamma T, Yogeeswari P. Targeting hexokinase 2 for oral cancer therapy: structure-based design and validation of lead compounds

The pursuit of small molecule inhibitors targeting hexokinase 2 (HK2) has significantly captivated the field of cancer drug discovery. Nevertheless, the creation of selective inhibitors aimed at specific isoforms of hexokinase (HK) remains a formidable challenge. Here, we present a multiple-pharmacophore modeling approach for designing ligands against HK2 with a marked anti-proliferative effect on FaDu and Cal27 oral cancer cell lines. Molecular dynamics (MD) simulations showed that the prototype ligand exhibited a higher affinity towards HK2. Complementing this, we put forth a sustainable synthetic pathway: an environmentally conscious, single-step process facilitated through a direct amidation of the ester with an amine under transition-metal-free conditions with an excellent yield in ambient temperature, followed by a column chromatography avoided separation technique of the identified lead bioactive compound (H2) that exhibited cell cycle arrest and apoptosis. We observed that the inhibition of HK2 led to the loss of mitochondrial membrane potential and increased mitophagy as a potential mechanism of anticancer action. The lead H2 also reduced the growth of spheroids. Collectively, these results indicated the proof-of-concept for the prototypical lead towards HK2 inhibition with anti-cancer potential.

Clustering Protein Binding Pockets and Identifying Potential Drug Interactions: A Novel Ligand-Based Featurization Method

Protein-ligand interactions are essential to drug discovery and drug development efforts. Desirable on-target or multitarget interactions are the first step in finding an effective therapeutic, while undesirable off-target interactions are the first step in assessing safety. In this work, we introduce a novel ligand-based featurization and mapping of human protein pockets to identify closely related protein targets and to project novel drugs into a hybrid protein-ligand feature space to identify their likely protein interactions. Using structure-based template matches from PDB, protein pockets are featured by the ligands that bind to their best co-complex template matches. The simplicity and interpretability of this approach provide a granular characterization of the human proteome at the protein-pocket level instead of the traditional protein-level characterization by family, function, or pathway. We demonstrate the power of this featurization method by clustering a subset of the human proteome and evaluating the predicted cluster associations of over 7000 compounds.

Rabdosichuanin C inhibits productions of pro-inflammatory mediators regulated by NF-κB signaling in LPS-stimulated RAW264.7 cells

Chronic pharyngitis (CP) is an inflammatory disease of the pharyngeal mucosa and its lymphatic tissues that is difficult to treat clinically. However, research on the exact therapeutic agents and molecular mechanisms of CP is still unclear. In this study, we investigated Rabdosichuanin C (RC) to attenuate lipopolysaccharide (LPS)-induced inflammatory damage in RAW264.7 cells by a combination of targeted virtual screening and in vitro activity assay and further clarified its molecular mechanism of action centering on the IκB/nuclear factor kappa B (NF-κB) pathway. Molecular docking and pharmacophore simulation methods were used to screen compounds with IκB inhibitory effects. Expression of genes and proteins related to the IκB/NF-κB signaling pathway by RC in LPS-induced inflammatory injury model of RAW264.7 cells was detected by PCR, enzyme-linked immunosorbent assay, and Western blot. The docking of RC with IκB protein showed good binding energy, and pharmacophore simulations further confirmed the active effect of RC in inhibiting IκB protein. RC intervention in LPS-induced RAW264.7 cells significantly reduced the expression levels of inflammatory factors tumor necrosis factor-α, interleukins-6, iNOS, and CD-86 at the messenger RNA and protein levels, downregulated IκB, p65 protein phosphorylation levels, and significantly inhibited IκB/NF-κB signaling pathway activation. Virtual screening provided us with an effective method to rapidly identify compounds RC that target inhibit the action of IκB, and the activity results showed that RC inhibits NF-κB signaling pathway activation. It is suggested that RC may play a role in the treatment of CP by inhibiting the IκB/NF-κB signaling pathway.

Structure-based pharmacophore modeling 2. Developing a novel framework for structure-based pharmacophore model generation and selection

Pharmacophore models are three-dimensional arrangements of molecular features required for biological activity that are used in ligand identification efforts for many biological targets, including G protein-coupled receptors (GPCR). Though GPCR are integral membrane proteins of considerable interest as targets for drug development, many of these receptors lack known ligands or experimentally determined structures necessary for ligand- or structure-based pharmacophore model generation, respectively. Thus, we here present a structure-based pharmacophore modeling approach that uses fragments placed with Multiple Copy Simultaneous Search (MCSS) to generate high-performing pharmacophore models in the context of experimentally determined, as well as modeled GPCR structures. Moreover, we have addressed the oft-neglected topic of pharmacophore model selection via development of a cluster-then-predict machine learning workflow. Herein score-based pharmacophore models were generated in experimentally determined and modeled structures of 13 class A GPCR and resulted in pharmacophore models exhibiting high enrichment factors when used to search a database containing 569 class A GPCR ligands. In addition, classification of pharmacophore models with the best performing cluster-then-predict logistic regression classifier resulted in positive predictive values (PPV) of 0.88 and 0.76 for selecting high enrichment pharmacophore models from among those generated in experimentally determined and modeled structures, respectively.

Application of computational methods for class A GPCR Ligand discovery

G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for drug development due to their role in transmitting cellular signals in a multitude of biological processes. Of the six classes categorizing GPCR (A, B, C, D, E, and F), class A contains the largest number of therapeutically relevant GPCR. Despite their importance as drug targets, many challenges exist for the discovery of novel class A GPCR ligands serving as drug precursors. Though knowledge of the structural and functional characteristics of GPCR has grown significantly over the past 20 years, a large portion of GPCR lack reported, experimentally determined structures. Furthermore, many GPCR have no known endogenous and/or synthetic ligands, limiting further exploration of their biochemical, cellular, and physiological roles. While many successes in GPCR ligand discovery have resulted from experimental high-throughput screening, computational methods have played an increasingly important role in GPCR ligand identification in the past decade. Here we discuss computational techniques applied to GPCR ligand discovery. This review summarizes class A GPCR structure/function and provides an overview of many obstacles currently faced in GPCR ligand discovery. Furthermore, we discuss applications and recent successes of computational techniques used to predict GPCR structure as well as present a summary of ligand- and structure-based methods used to identify potential GPCR ligands. Finally, we discuss computational hit list generation and refinement and provide comprehensive workflows for GPCR ligand identification.

Structure-based pharmacophore modeling 1. Automated random pharmacophore model generation

Pharmacophores are three-dimensional arrangements of molecular features required for biological activity that are often used in virtual screening efforts to prioritize ligands for experimental testing. G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for ligand discovery and drug development. Ligand-based pharmacophore models can be constructed to identify structural commonalities between known bioactive ligands for targets including GPCR. However, structure-based pharmacophores (which only require an experimentally determined or modeled structure for a protein target) have gained more attention to aid in virtual screening efforts as the number of publicly available experimentally determined GPCR structures have increased (140 unique GPCR represented as of October 24, 2022). Thus, the goal of this study was to develop a method of structure-based pharmacophore model generation applicable to ligand discovery for GPCR that have few known ligands. Pharmacophore models were generated within the active sites of 8 class A GPCR crystal structures via automated annotation of 5 randomly selected functional group fragments to sample diverse combinations of pharmacophore features. Each of the 5000 generated pharmacophores was then used to search a database containing active and decoy/inactive compounds for 30 class A GPCR and scored using enrichment factor and goodness-of-hit metrics to assess performance. Application of this method to the set of 8 class A GPCR produced pharmacophore models possessing the theoretical maximum enrichment factor value in both resolved structures (8 of 8 cases) and homology models (7 of 8 cases), indicating that generated pharmacophore models can prove useful in the context of virtual screening.

Mind the Gap-Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence

G protein-coupled receptors (GPCRs) are amongst the most pharmaceutically relevant and well-studied protein targets, yet unanswered questions in the field leave significant gaps in our understanding of their nuanced structure and function. Three-dimensional pharmacophore models are powerful computational tools in in silico drug discovery, presenting myriad opportunities for the integration of GPCR structural biology and cheminformatics. This review highlights success stories in the application of 3D pharmacophore modeling to de novo drug design, the discovery of biased and allosteric ligands, scaffold hopping, QSAR analysis, hit-to-lead optimization, GPCR de-orphanization, mechanistic understanding of GPCR pharmacology and the elucidation of ligand-receptor interactions. Furthermore, advances in the incorporation of dynamics and machine learning are highlighted. The review will analyze challenges in the field of GPCR drug discovery, detailing how 3D pharmacophore modeling can be used to address them. Finally, we will present opportunities afforded by 3D pharmacophore modeling in the advancement of our understanding and targeting of GPCRs.

OPFRs in e-waste sites: Integrating in silico approaches, selective bioremediation, and health risk management of residents surrounding

A multilevel index system of organophosphate flame retardant bioremediation effect in an e-waste handling area was established under three bioremediation scenarios (scenario I, plant absorption; scenario II, plant-microbial combined remediation; scenario III, microbial degradation). Directional modification of OPFR substitutes with high selective bioremediation was performed. The virtual amino acid mutation approach was utilised to generate high-efficiency selective absorption/degradation mutant proteins (MPs) in a plant-microbial system under varying conditions. In scenario III, the MP's microbial degrading ability to replace molecules was increased to the greatest degree (165.82%). Appropriate foods such as corn, pig liver, and yam should be consumed, whereas the simultaneous consumption of high protein foods such as pig liver and walnut should be avoided; sweet potato and yam are believed to be prevent OPFRs and substitute molecules from entering the human body through multiple pathways for reduced genotoxicity of OPFRs in the populations of e-waste handling areas (the reduction degree can reach 85.12%). The study provides a theoretical basis for the development of ecologically acceptable OPFR substitutes and innovative high-efficiency bioremediation MPs, as well as for the reduction of the joint toxicity risk of multiple ingestion route exposure/gene damage of OPFRs in high OPFR exposure sites.

Diels-Alder Adducts of Morphinan-6,8-Dienes and Their Transformations

6,14-ethenomorphinans are semisynthetic opiate derivatives containing an ethylene bridge between positions 6 and 14 in ring-C of the morphine skeleton that imparts a rigid molecular structure. These compounds represent an important family of opioid receptor ligands in which the 6,14-etheno bridged structural motif originates from a [4 + 2] cycloaddition of morphinan-6,8-dienes with dienophiles. Certain 6,14-ethenomorphinans having extremely high affinity for opioid receptors are often non-selective for opioid receptor subtypes, but this view is now undergoing some revision. The agonist 20R-etorphine and 20R-dihydroetorphine are several thousand times more potent analgesics than morphine, whereas diprenorphine is a high-affinity non-selective antagonist. The partial agonist buprenorphine is used as an analgesic in the management of post-operative pain or in substitution therapy for opiate addiction, sometimes in combination with the non-selective antagonist naloxone. In the context of the current opioid crisis, we communicated a summary of several decades of work toward generating opioid analgesics with lesser side effects or abuse potential. Our summary placed a focus on Diels-Alder reactions of morphinan-6,8-dienes and subsequent transformations of the cycloadducts. We also summarized the pharmacological aspects of radiolabeled 6,14-ethenomorphinans used in molecular imaging of opioid receptors.