Ligand discovery from a dopamine D3 receptor homology model and crystal structure

GPCR-mediated natural products and compounds: Potential therapeutic targets for the treatment of neurological diseases

G protein-coupled receptors (GPCRs), widely expressed in the human central nervous system (CNS), perform numerous physiological functions and play a significant role in the pathogenesis of diseases. Consequently, identifying key therapeutic GPCRs targets for CNS-related diseases is garnering immense interest in research labs and pharmaceutical companies. However, using GPCRs drugs for treating neurodegenerative diseases has limitations, including side effects and uncertain effective time frame. Recognizing the rich history of herbal treatments for neurological disorders like stroke, Alzheimer's disease (AD), and Parkinson's disease (PD), modern pharmacological research is now focusing on the understanding of the efficacy of traditional Chinese medicinal herbs and compounds in modulating GPCRs and treatment of neurodegenerative conditions. This paper will offer a comprehensive, critical review of how certain natural products and compounds target GPCRs to treat neurological diseases. Conducting an in-depth study of herbal remedies and their efficacies against CNS-related disorders through GPCRs targeting will augment our strategies for treating neurological disorders. This will not only broaden our understanding of effective therapeutic methodologies but also identify the root causes of altered GPCRs signaling in the context of pathophysiological mechanisms in neurological diseases. Moreover, it would be informative for the creation of safer and more effective GPCR-mediated drugs, thereby establishing a foundation for future treatment of various neurological diseases.

AlphaFold accelerated discovery of psychotropic agonists targeting the trace amine-associated receptor 1

Artificial intelligence is revolutionizing protein structure prediction, providing unprecedented opportunities for drug design. To assess the potential impact on ligand discovery, we compared virtual screens using protein structures generated by the AlphaFold machine learning method and traditional homology modeling. More than 16 million compounds were docked to models of the trace amine-associated receptor 1 (TAAR1), a G protein-coupled receptor of unknown structure and target for treating neuropsychiatric disorders. Sets of 30 and 32 highly ranked compounds from the AlphaFold and homology model screens, respectively, were experimentally evaluated. Of these, 25 were TAAR1 agonists with potencies ranging from 12 to 0.03 μM. The AlphaFold screen yielded a more than twofold higher hit rate (60%) than the homology model and discovered the most potent agonists. A TAAR1 agonist with a promising selectivity profile and drug-like properties showed physiological and antipsychotic-like effects in wild-type but not in TAAR1 knockout mice. These results demonstrate that AlphaFold structures can accelerate drug discovery.

AlphaFold2 structures guide prospective ligand discovery

AlphaFold2 (AF2) models have had wide impact but mixed success in retrospective ligand recognition. We prospectively docked large libraries against unrefined AF2 models of the σ2 and serotonin 2A (5-HT2A) receptors, testing hundreds of new molecules and comparing results with those obtained from docking against the experimental structures. Hit rates were high and similar for the experimental and AF2 structures, as were affinities. Success in docking against the AF2 models was achieved despite differences between orthosteric residue conformations in the AF2 models and the experimental structures. Determination of the cryo-electron microscopy structure for one of the more potent 5-HT2A ligands from the AF2 docking revealed residue accommodations that resembled the AF2 prediction. AF2 models may sample conformations that differ from experimental structures but remain low energy and relevant for ligand discovery, extending the domain of structure-based drug design.

Structural insights into the potential binding sites of Cathepsin D using molecular modelling techniques

Alzheimer's disease (AD) is the most prevalent type of dementia caused by the accumulation of amyloid beta (Aβ) peptides. The extracellular deposition of Aβ peptides in human AD brain causes neuronal death. Therefore, it has been found that Aβ peptide degradation is a possible therapeutic target for AD. CathD has been known to breakdown amyloid beta peptides. However, the structural role of CathD is not yet clear. Hence, for the purpose of gaining a deeper comprehension of the structure of CathD, the present computational investigation was performed using virtual screening technique to predict CathD's active site residues and substrate binding mode. Ligand-based virtual screening was implemented on small molecules from ZINC database against crystal structure of CathD. Further, molecular docking was utilised to investigate the binding mechanism of CathD with substrates and virtually screened inhibitors. Localised compounds obtained through screening performed by PyRx and AutoDock 4.2 with CathD receptor and the compounds having highest binding affinities were picked as; ZINC00601317, ZINC04214975 and ZINCC12500925 as our top choices. The hydrophobic residues Viz. Gly35, Val31, Thr34, Gly128, Ile124 and Ala13 help stabilising the CathD-ligand complexes, which in turn emphasises substrate and inhibitor selectivity. Further, MM-GBSA approach has been used to calculate binding free energy between CathD and selected compounds. Therefore, it would be beneficial to understand the active site pocket of CathD with the assistance of these discoveries. Thus, the present study would be helpful to identify active site pocket of CathD, which could be beneficial to develop novel therapeutic strategies for the AD.

AlphaFold2 structures template ligand discovery

AlphaFold2 (AF2) and RosettaFold have greatly expanded the number of structures available for structure-based ligand discovery, even though retrospective studies have cast doubt on their direct usefulness for that goal. Here, we tested unrefined AF2 models prospectively, comparing experimental hit-rates and affinities from large library docking against AF2 models vs the same screens targeting experimental structures of the same receptors. In retrospective docking screens against the σ2 and the 5-HT2A receptors, the AF2 structures struggled to recapitulate ligands that we had previously found docking against the receptors' experimental structures, consistent with published results. Prospective large library docking against the AF2 models, however, yielded similar hit rates for both receptors versus docking against experimentally-derived structures; hundreds of molecules were prioritized and tested against each model and each structure of each receptor. The success of the AF2 models was achieved despite differences in orthosteric pocket residue conformations for both targets versus the experimental structures. Intriguingly, against the 5-HT2A receptor the most potent, subtype-selective agonists were discovered via docking against the AF2 model, not the experimental structure. To understand this from a molecular perspective, a cryoEM structure was determined for one of the more potent and selective ligands to emerge from docking against the AF2 model of the 5-HT2A receptor. Our findings suggest that AF2 models may sample conformations that are relevant for ligand discovery, much extending the domain of applicability of structure-based ligand discovery.

Review on allosteric modulators of dopamine receptors so far

Background Contemporary research is predominantly directed towards allosteric modulators, a class of compounds designed to interact with specific sites distinct from the orthosteric site on G protein-coupled receptors. These allosteric modulators play a pivotal role in influencing diverse pharmacological effects, such as agonism/inverse agonism, efficacy modulation, and affinity modulation. One particularly intriguing aspect is the demonstrated capacity of allosteric modulation to enhance drug selectivity for therapeutic purposes, potentially leading to a reduction in serious side effects associated with traditional approaches. Allosteric ligands, a majority of which fall into the categories of negative allosteric modulators or positive allosteric modulators, exhibit the unique ability to either diminish or enhance the effects of endogenous ligands. Negative allosteric modulators weaken the response, while positive allosteric modulators intensify it. Additionally, silent allosteric modulators represent a distinct class that neither activates nor blocks the effects of endogenous ligands, adding complexity to the spectrum of allosteric modulation. In the broader context of central nervous system disorders, allosteric modulation takes center stage, particularly in the realm of dopamine receptors specifically, D1, D2, and D3 receptors. These receptors hold immense therapeutic potential for a range of conditions spanning neurodegenerative disorders to neurobehavioral and psychiatric disorders. The intricate modulation of dopamine receptors through allosteric mechanisms offers a nuanced and versatile approach to drug development. As research endeavors continue to unfold, the exploration of allosteric modulation stands as a promising frontier, holding the potential to reshape the landscape of drug discovery and therapeutic interventions in the field of neurology and psychiatry.

Orphan G protein-coupled receptors: the ongoing search for a home

G protein-coupled receptors (GPCRs) make up the largest receptor superfamily, accounting for 4% of protein-coding genes. Despite the prevalence of such transmembrane receptors, a significant number remain orphans, lacking identified endogenous ligands. Since their conception, the reverse pharmacology approach has been used to characterize such receptors. However, the multifaceted and nuanced nature of GPCR signaling poses a great challenge to their pharmacological elucidation. Considering their therapeutic relevance, the search for native orphan GPCR ligands continues. Despite limited structural input in terms of 3D crystallized structures, with advances in machine-learning approaches, there has been great progress with respect to accurate ligand prediction. Though such an approach proves valuable given that ligand scarcity is the greatest hurdle to orphan GPCR deorphanization, the future pairings of the remaining orphan GPCRs may not necessarily take a one-size-fits-all approach but should be more comprehensive in accounting for numerous nuanced possibilities to cover the full spectrum of GPCR signaling.

Exploring Novel Antidepressants Targeting G Protein-Coupled Receptors and Key Membrane Receptors Based on Molecular Structures

Major Depressive Disorder (MDD) is a complex mental disorder that involves alterations in signal transmission across multiple scales and structural abnormalities. The development of effective antidepressants (ADs) has been hindered by the dominance of monoamine hypothesis, resulting in slow progress. Traditional ADs have undesirable traits like delayed onset of action, limited efficacy, and severe side effects. Recently, two categories of fast-acting antidepressant compounds have surfaced, dissociative anesthetics S-ketamine and its metabolites, as well as psychedelics such as lysergic acid diethylamide (LSD). This has led to structural research and drug development of the receptors that they target. This review provides breakthroughs and achievements in the structure of depression-related receptors and novel ADs based on these. Cryo-electron microscopy (cryo-EM) has enabled researchers to identify the structures of membrane receptors, including the N-methyl-D-aspartate receptor (NMDAR) and the 5-hydroxytryptamine 2A (5-HT2A) receptor. These high-resolution structures can be used for the development of novel ADs using virtual drug screening (VDS). Moreover, the unique antidepressant effects of 5-HT1A receptors in various brain regions, and the pivotal roles of the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) and tyrosine kinase receptor 2 (TrkB) in regulating synaptic plasticity, emphasize their potential as therapeutic targets. Using structural information, a series of highly selective ADs were designed based on the different role of receptors in MDD. These molecules have the favorable characteristics of rapid onset and low adverse drug reactions. This review offers researchers guidance and a methodological framework for the structure-based design of ADs.

Advances in structure-based drug design: The potential for precision therapeutics in psychiatric disorders

Over the years, the field of GPCR drug design has undergone a remarkable evolution, fueled by advancements in science and technology. This evolution has given rise to a diverse range of ideas and approaches in structure-based drug design, bolstering the versatility and strength of the GPCR drug design toolbox. This review encapsulates the iterative development process, navigating challenges and opportunities in structure-based drug design within GPCRs. With a focused emphasis on its impact on psychiatric disorders, the review accentuates recent advancements and delves into the potentials unlocked by emerging technologies. The review explores the intricate interplay between scientific progress and iterative refinement, offering profound insights into the potential pathways that lie ahead for GPCR drug design.

How accurately can one predict drug binding modes using AlphaFold models?

Computational prediction of protein structure has been pursued intensely for decades, motivated largely by the goal of using structural models for drug discovery. Recently developed machine-learning methods such as AlphaFold 2 (AF2) have dramatically improved protein structure prediction, with reported accuracy approaching that of experimentally determined structures. To what extent do these advances translate to an ability to predict more accurately how drugs and drug candidates bind to their target proteins? Here, we carefully examine the utility of AF2 protein structure models for predicting binding poses of drug-like molecules at the largest class of drug targets, the G-protein-coupled receptors. We find that AF2 models capture binding pocket structures much more accurately than traditional homology models, with errors nearly as small as differences between structures of the same protein determined experimentally with different ligands bound. Strikingly, however, the accuracy of ligand-binding poses predicted by computational docking to AF2 models is not significantly higher than when docking to traditional homology models and is much lower than when docking to structures determined experimentally without these ligands bound. These results have important implications for all those who might use predicted protein structures for drug discovery.

Quality Assessment of Selected Protein Structures Derived from Homology Modeling and AlphaFold

With technology advancing, many prediction algorithms have been developed to facilitate the modeling of inherently dynamic and flexible macromolecules such as proteins. Improvements in the prediction of protein structures have attracted a great deal of attention due to the advantages they offer, e.g., in drug design. While trusted experimental methods, such as X-ray crystallography, NMR spectroscopy, and electron microscopy, are preferred structure analysis techniques, in silico approaches are also being widely used. Two computational methods, which are on opposite ends of the spectrum with respect to their modus operandi, i.e., homology modeling and AlphaFold, have been established to provide high-quality structures. Here, a comparative study of the quality of structures either predicted by homology modeling or by AlphaFold is presented based on the characteristics determined by experimental studies using structure validation servers to fulfill the purpose. Although AlphaFold is able to predict high-quality structures, high-confidence parts are sometimes observed to be in disagreement with experimental data. On the other hand, while the structures obtained from homology modeling are successful in incorporating all aspects of the experimental structure used as a template, this method may struggle to accurately model a structure in the absence of a suitable template. In general, although both methods produce high-quality models, the criteria by which they are superior to each other are different and thus discussed in detail.

Substrate binding and inhibition of the anion exchanger 1 transporter

Anion exchanger 1 (AE1), a member of the solute carrier (SLC) family, is the primary bicarbonate transporter in erythrocytes, regulating pH levels and CO2 transport between lungs and tissues. Previous studies characterized its role in erythrocyte structure and provided insight into transport regulation. However, key questions remain regarding substrate binding and transport, mechanisms of drug inhibition and modulation by membrane components. Here we present seven cryo-EM structures in apo, bicarbonate-bound and inhibitor-bound states. These, combined with uptake and computational studies, reveal important molecular features of substrate recognition and transport, and illuminate sterol binding sites, to elucidate distinct inhibitory mechanisms of research chemicals and prescription drugs. We further probe the substrate binding site via structure-based ligand screening, identifying an AE1 inhibitor. Together, our findings provide insight into mechanisms of solute carrier transport and inhibition.

Targeting SLC transporters: small molecules as modulators and therapeutic opportunities

Solute carrier (SLCs) transporters mediate the transport of a broad range of solutes across biological membranes. Dysregulation of SLCs has been associated with various pathologies, including metabolic and neurological disorders, as well as cancer and rare diseases. SLCs are therefore emerging as key targets for therapeutic intervention with several recently approved drugs targeting these proteins. Unlocking this large and complex group of proteins is essential to identifying unknown SLC targets and developing next-generation SLC therapeutics. Recent progress in experimental and computational techniques has significantly advanced SLC research, including drug discovery. Here, we review emerging topics in therapeutic discovery of SLCs, focusing on state-of-the-art approaches in structural, chemical, and computational biology, and discuss current challenges in transporter drug discovery.

Large library docking for novel SARS-CoV-2 main protease non-covalent and covalent inhibitors

Antiviral therapeutics to treat SARS-CoV-2 are needed to diminish the morbidity of the ongoing COVID-19 pandemic. A well-precedented drug target is the main viral protease (MPro ), which is targeted by an approved drug and by several investigational drugs. Emerging viral resistance has made new inhibitor chemotypes more pressing. Adopting a structure-based approach, we docked 1.2 billion non-covalent lead-like molecules and a new library of 6.5 million electrophiles against the enzyme structure. From these, 29 non-covalent and 11 covalent inhibitors were identified in 37 series, the most potent having an IC50 of 29 and 20 μM, respectively. Several series were optimized, resulting in low micromolar inhibitors. Subsequent crystallography confirmed the docking predicted binding modes and may template further optimization. While the new chemotypes may aid further optimization of MPro inhibitors for SARS-CoV-2, the modest success rate also reveals weaknesses in our approach for challenging targets like MPro versus other targets where it has been more successful, and versus other structure-based techniques against MPro itself.

Computational approaches streamlining drug discovery

Computer-aided drug discovery has been around for decades, although the past few years have seen a tectonic shift towards embracing computational technologies in both academia and pharma. This shift is largely defined by the flood of data on ligand properties and binding to therapeutic targets and their 3D structures, abundant computing capacities and the advent of on-demand virtual libraries of drug-like small molecules in their billions. Taking full advantage of these resources requires fast computational methods for effective ligand screening. This includes structure-based virtual screening of gigascale chemical spaces, further facilitated by fast iterative screening approaches. Highly synergistic are developments in deep learning predictions of ligand properties and target activities in lieu of receptor structure. Here we review recent advances in ligand discovery technologies, their potential for reshaping the whole process of drug discovery and development, as well as the challenges they encounter. We also discuss how the rapid identification of highly diverse, potent, target-selective and drug-like ligands to protein targets can democratize the drug discovery process, presenting new opportunities for the cost-effective development of safer and more effective small-molecule treatments.

Comprehensive Survey of Consensus Docking for High-Throughput Virtual Screening

The rapid advances of 3D techniques for the structural determination of proteins and the development of numerous computational methods and strategies have led to identifying highly active compounds in computer drug design. Molecular docking is a method widely used in high-throughput virtual screening campaigns to filter potential ligands targeted to proteins. A great variety of docking programs are currently available, which differ in the algorithms and approaches used to predict the binding mode and the affinity of the ligand. All programs heavily rely on scoring functions to accurately predict ligand binding affinity, and despite differences in performance, none of these docking programs is preferable to the others. To overcome this problem, consensus scoring methods improve the outcome of virtual screening by averaging the rank or score of individual molecules obtained from different docking programs. The successful application of consensus docking in high-throughput virtual screening highlights the need to optimize the predictive power of molecular docking methods.

Describing inhibitor specificity for the amino acid transporter LAT1 from metainference simulations

The human L-type amino acid transporter 1 (LAT1; SLC7A5) is a membrane transporter of amino acids, thyroid hormones, and drugs such as the Parkinson's disease drug levodopa (L-Dopa). LAT1 is found in the blood-brain barrier, testis, bone marrow, and placenta, and its dysregulation has been associated with various neurological diseases, such as autism and epilepsy, as well as cancer. In this study, we combine metainference molecular dynamics simulations, molecular docking, and experimental testing, to characterize LAT1-inhibitor interactions. We first conducted a series of molecular docking experiments to identify the most relevant interactions between LAT1's substrate-binding site and ligands, including both inhibitors and substrates. We then performed metainference molecular dynamics simulations using cryoelectron microscopy structures in different conformations of LAT1 with the electron density map as a spatial restraint, to explore the inherent heterogeneity in the structures. We analyzed the LAT1 substrate-binding site to map important LAT1-ligand interactions as well as newly described druggable pockets. Finally, this analysis guided the discovery of previously unknown LAT1 ligands using virtual screening and cellular uptake experiments. Our results improve our understanding of LAT1-inhibitor recognition, providing a framework for rational design of future lead compounds targeting this key drug target.

Computational modeling and druggability assessment of Aggregatibacter actinomycetemcomitans leukotoxin

The leukotoxin (LtxA) of Aggregatibacter actinomycetemcomitans (A. actinomycetemcomitans) is a protein exotoxin belonging to the repeat-in-toxin family (RTX). Numerous studies have demonstrated that LtxA may play a critical role in the pathogenicity of A. actinomycetemcomitans since hyper-leukotoxic strains have been associated with severe disease. Accordingly, considerable effort has been made to elucidate the mechanisms by which LtxA interacts with host cells and induce their death. However, these attempts have been hampered by the unavailability of a tertiary structure of the toxin, which limits the understanding of its molecular properties and mechanisms. In this paper, we used homology and template free modeling algorithms to build the complete tertiary model of LtxA at atomic level in its calcium-bound Holo-state. The resulting model was refined by energy minimization, validated by Molprobity and ProSA tools, and subsequently subjected to a cumulative 600ns of all-atom classical molecular dynamics simulation to evaluate its structural aspects. The druggability of the proposed model was assessed using Fpocket and FTMap tools, resulting in the identification of four putative cavities and fifteen binding hotspots that could be targeted by rational drug design tools to find new ligands to inhibit LtxA activity.

Antifungal activity and molecular mechanisms of mulberrin derivatives against Colletotrichum gloeosporioides for mango storage

In this work, by using high throughput virtual screening and bioactivity assays, this work revealed that three natural compounds, mulberrin (Mul) exhibiting the highest anti-CYP51 activity, isoxanthohumol and (s)-isopsoralen markedly inhibited 14α-demethylase (a pivotal biosynthetic enzyme involved in the biosynthesis of ergosterol) in Colletotrichum gloeosporioides. Results of computational biology analysis demonstrated that, among the three inhibitors bound to the catalytic pocket of CYP51, Mul showed a closer distance with heme in CYP51 and a stronger binding free energy with CYP51. In vitro tests, Mul demonstrated excellent anti-Colletotrichum gloeosporioides activity by inhibiting CYP51 activity. Notably, Mul treatment decreased the bioactivity of CYP51, thereby increasing cell membrane permeability and cell death. Moreover, Mul treatment significantly prolonged the preservation period of fruits. These results suggest that Mul suppresses anthracnose in postharvest mango by inhibiting the growth of Colletotrichum gloeosporioides and can be used as a potential natural preserving agent.

Functional Characterization of Sodium Channel Inhibitors at the Delta-Opioid Receptor

Existing pharmacotherapies acting on the opioid receptor system have been extensively used to treat chronic pain and addictive disorders. Nevertheless, the adverse side effects associated with opioid therapy underscore the need for concerted measures to develop safer analgesics. A promising avenue of research stems from the characterization of a sodium-dependent allosteric regulation site housed within the delta-opioid receptor and several other G protein-coupled receptors (GPCRs), thereby revealing the presence of a cluster of sodium and water molecules lodged in a cavity thought to be present only in the inactive conformation of the receptor. Studies into the structure-function relationship of said pocket demonstrated its critical involvement in the functional control of GPCR signaling. While the sodium pocket has been proposed to be present in the majority of class A GPCRs, the shape of this allosteric cavity appears to have significant structural variation among crystallographically solved GPCRs, making this site optimal for the design of new allosteric modulators that will be selective for opioid receptors. The size of the sodium pocket supports the accommodation of small molecules, and it has been speculated that promiscuous amiloride and 5'-substituted amiloride-related derivatives could target this cavity within many GPCRs, including opioid receptors. Using pharmacological approaches, we have described the selectivities of 5'-substituted amiloride-related derivatives, as well as the hitherto undescribed activity of the NHE1 inhibitor zoniporide toward class A GPCRs. Our investigations into the structural features of the delta-opioid receptor and its ensuing signaling activities suggest a bitopic mode of overlapping interactions involving the orthosteric site and the juxtaposed Na⁺ pocket, but only at the active or partially active opioid receptor.

Structure-Based Virtual Screening for Ligands of G Protein-Coupled Receptors: What Can Molecular Docking Do for You?

G protein-coupled receptors (GPCRs) constitute the largest family of membrane proteins in the human genome and are important therapeutic targets. During the last decade, the number of atomic-resolution structures of GPCRs has increased rapidly, providing insights into drug binding at the molecular level. These breakthroughs have created excitement regarding the potential of using structural information in ligand design and initiated a new era of rational drug discovery for GPCRs. The molecular docking method is now widely applied to model the three-dimensional structures of GPCR-ligand complexes and screen for chemical probes in large compound libraries. In this review article, we first summarize the current structural coverage of the GPCR superfamily and the understanding of receptor-ligand interactions at atomic resolution. We then present the general workflow of structure-based virtual screening and strategies to discover GPCR ligands in chemical libraries. We assess the state of the art of this research field by summarizing prospective applications of virtual screening based on experimental structures. Strategies to identify compounds with specific efficacy and selectivity profiles are discussed, illustrating the opportunities and limitations of the molecular docking method. Our overview shows that structure-based virtual screening can discover novel leads and will be essential in pursuing the next generation of GPCR drugs. SIGNIFICANCE STATEMENT: Extraordinary advances in the structural biology of G protein-coupled receptors have revealed the molecular details of ligand recognition by this large family of therapeutic targets, providing novel avenues for rational drug design. Structure-based docking is an efficient computational approach to identify novel chemical probes from large compound libraries, which has the potential to accelerate the development of drug candidates.

Nonantimicrobial Actions of Macrolides: Overview and Perspectives for Future Development

Macrolides are among the most widely prescribed broad spectrum antibacterials, particularly for respiratory infections. It is now recognized that these drugs, in particular azithromycin, also exert time-dependent immunomodulatory actions that contribute to their therapeutic benefit in both infectious and other chronic inflammatory diseases. Their increased chronic use in airway inflammation and, more recently, of azithromycin in COVID-19, however, has led to a rise in bacterial resistance. An additional crucial aspect of chronic airway inflammation, such as chronic obstructive pulmonary disease, as well as other inflammatory disorders, is the loss of epithelial barrier protection against pathogens and pollutants. In recent years, azithromycin has been shown with time to enhance the barrier properties of airway epithelial cells, an action that makes an important contribution to its therapeutic efficacy. In this article, we review the background and evidence for various immunomodulatory and time-dependent actions of macrolides on inflammatory processes and on the epithelium and highlight novel nonantibacterial macrolides that are being studied for immunomodulatory and barrier-strengthening properties to circumvent the risk of bacterial resistance that occurs with macrolide antibacterials. We also briefly review the clinical effects of macrolides in respiratory and other inflammatory diseases associated with epithelial injury and propose that the beneficial epithelial effects of nonantibacterial azithromycin derivatives in chronic inflammation, even given prophylactically, are likely to gain increasing attention in the future. SIGNIFICANCE STATEMENT: Based on its immunomodulatory properties and ability to enhance the protective role of the lung epithelium against pathogens, azithromycin has proven superior to other macrolides in treating chronic respiratory inflammation. A nonantibiotic azithromycin derivative is likely to offer prophylactic benefits against inflammation and epithelial damage of differing causes while preserving the use of macrolides as antibiotics.

A practical guide to large-scale docking

Structure-based docking screens of large compound libraries have become common in early drug and probe discovery. As computer efficiency has improved and compound libraries have grown, the ability to screen hundreds of millions, and even billions, of compounds has become feasible for modest-sized computer clusters. This allows the rapid and cost-effective exploration and categorization of vast chemical space into a subset enriched with potential hits for a given target. To accomplish this goal at speed, approximations are used that result in undersampling of possible configurations and inaccurate predictions of absolute binding energies. Accordingly, it is important to establish controls, as are common in other fields, to enhance the likelihood of success in spite of these challenges. Here we outline best practices and control docking calculations that help evaluate docking parameters for a given target prior to undertaking a large-scale prospective screen, with exemplification in one particular target, the melatonin receptor, where following this procedure led to direct docking hits with activities in the subnanomolar range. Additional controls are suggested to ensure specific activity for experimentally validated hit compounds. These guidelines should be useful regardless of the docking software used. Docking software described in the outlined protocol (DOCK3.7) is made freely available for academic research to explore new hits for a range of targets.

Efficiency of Homology Modeling Assisted Molecular Docking in G-protein Coupled Receptors

BACKGROUND

Molecular docking is in regular practice to assess ligand affinity on a target protein crystal structure. In the absence of protein crystal structure, the homology modeling or comparative modeling is the best alternative to elucidate the relationship details between a ligand and protein at the molecular level. The development of accurate homology modeling (HM) and its integration with molecular docking (MD) is essential for successful, rational drug discovery.

OBJECTIVE

The G-protein coupled receptors (GPCRs) are attractive therapeutic targets due to their immense role in human pharmacology. The GPCRs are membrane-bound proteins with the complex constitution, and the understanding of their activation and inactivation mechanisms is quite challenging. Over the past decade, there has been a rapid expansion in the number of solved G-protein-coupled receptor (GPCR) crystal structures; however, the majority of the GPCR structures remain unsolved. In this context, HM guided MD has been widely used for structure-based drug design (SBDD) of GPCRs.

METHODS

The focus of this review is on the recent (i) developments on HM supported GPCR drug discovery in the absence of GPCR crystal structures and (ii) application of HM in understanding the ligand interactions at the binding site, virtual screening, determining receptor subtype selectivity and receptor behaviour in comparison with GPCR crystal structures.

RESULTS

The HM in GPCRs has been extremely challenging due to the scarcity in template structures. In such a scenario, it is difficult to get accurate HM that can facilitate understanding of the ligand-receptor interactions. This problem has been alleviated to some extent by developing refined HM based on incorporating active /inactive ligand information and inducing protein flexibility. In some cases, HM proteins were found to outscore crystal structures.

CONCLUSION

The developments in HM have been highly operative to gain insights about the ligand interaction at the binding site and receptor functioning at the molecular level. Thus, HM guided molecular docking may be useful for rational drug discovery for the GPCRs mediated diseases.

Data-Driven Ensemble Docking to Map Molecular Interactions of Steroid Analogs with Hepatic Organic Anion Transporting Polypeptides

Hepatic organic anion transporting polypeptides—OATP1B1, OATP1B3, and OATP2B1—are expressed at the basolateral membrane of hepatocytes, being responsible for the uptake of a wide range of natural substrates and structurally unrelated pharmaceuticals. Impaired function of hepatic OATPs has been linked to clinically relevant drug–drug interactions leading to altered pharmacokinetics of administered drugs. Therefore, understanding the commonalities and differences across the three transporters represents useful knowledge to guide the drug discovery process at an early stage. Unfortunately, such efforts remain challenging because of the lack of experimentally resolved protein structures for any member of the OATP family. In this study, we established a rigorous computational protocol to generate and validate structural models for hepatic OATPs. The multistep procedure is based on the systematic exploration of available protein structures with shared protein folding using normal-mode analysis, the calculation of multiple template backbones from elastic network models, the utilization of multiple template conformations to generate OATP structural models with various degrees of conformational flexibility, and the prioritization of models on the basis of enrichment docking. We employed the resulting OATP models of OATP1B1, OATP1B3, and OATP2B1 to elucidate binding modes of steroid analogs in the three transporters. Steroid conjugates have been recognized as endogenous substrates of these transporters. Thus, investigating this data set delivers insights into mechanisms of substrate recognition. In silico predictions were complemented with in vitro studies measuring the bioactivity of a compound set on OATP expressing cell lines. Important structural determinants conferring shared and distinct binding patterns of steroid analogs in the three transporters have been identified. Overall, this comparative study provides novel insights into hepatic OATP-ligand interactions and selectivity. Furthermore, the integrative computational workflow for structure-based modeling can be leveraged for other pharmaceutical targets of interest.

Broadband terahertz signatures and vibrations of dopamine

Dopamine (DA) is an essential neurotransmitter and hormone of the nervous system, its structural and conformational properties play critical roles in biological functions and signal transmission processes. Although this neuroactive molecule has been studied extensively, the low-frequency vibration features that are closely related to the conformation and molecular interactions in the terahertz (THz) band still remain unclear. In this study, a broadband THz time-domain spectroscopy (THz-TDS) system in the frequency band of 0.5-18 THz was used to characterize the unique THz fingerprint of DA. In addition, density functional theory (DFT) calculations were performed to analyze the vibrational properties of DA. The results suggest that each THz resonant absorption peak of DA corresponds to specific vibrational modes, and the collective vibration also exists in the broadband THz range. Moreover, the interactions between the DA ligand and the D2 and D3 receptors were investigated by docking, and the simulated THz spectra were obtained. The results indicate the dominant role of hydrogen bonding interactions and the specificity of molecular conformation. This work may help to understand the resonance coupling between THz electromagnetic waves and neurotransmitters.

Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?

The determination of G protein-coupled receptor (GPCR) structures at atomic resolution has improved understanding of cellular signaling and will accelerate the development of new drug candidates. However, experimental structures still remain unavailable for a majority of the GPCR family. GPCR structures and their interactions with ligands can also be modelled computationally, but such predictions have limited accuracy. In this work, we explored if molecular dynamics (MD) simulations could be used to refine the accuracy of in silico models of receptor-ligand complexes that were submitted to a community-wide assessment of GPCR structure prediction (GPCR Dock). Two simulation protocols were used to refine 30 models of the D3 dopamine receptor (D3R) in complex with an antagonist. Close to 60 μs of simulation time was generated and the resulting MD refined models were compared to a D3R crystal structure. In the MD simulations, the receptor models generally drifted further away from the crystal structure conformation. However, MD refinement was able to improve the accuracy of the ligand binding mode. The best refinement protocol improved agreement with the experimentally observed ligand binding mode for a majority of the models. Receptor structures with improved virtual screening performance, which was assessed by molecular docking of ligands and decoys, could also be identified among the MD refined models. Application of weak restraints to the transmembrane helixes in the MD simulations further improved predictions of the ligand binding mode and second extracellular loop. These results provide guidelines for application of MD refinement in prediction of GPCR-ligand complexes and directions for further method development.

BIO-GATS: A Tool for Automated GPCR Template Selection Through a Biophysical Approach for Homology Modeling

G protein-coupled receptors (GPCRs) are the largest family of membrane proteins with more than 800 members. GPCRs are involved in numerous physiological functions within the human body and are the target of more than 30% of the United States Food and Drug Administration (FDA) approved drugs. At present, over 400 experimental GPCR structures are available in the Protein Data Bank (PDB) representing 76 unique receptors. The absence of an experimental structure for the majority of GPCRs demand homology models for structure-based drug discovery workflows. The generation of good homology models requires appropriate templates. The commonly used methods for template selection are based on sequence identity. However, there exists low sequence identity among the GPCRs. Sequences with similar patterns of hydrophobic residues are often structural homologs, even with low sequence identity. Extending this, we propose a biophysical approach for template selection based principally on hydrophobicity correspondence between the target and the template. Our approach takes into consideration other relevant parameters, including resolution, similarity within the orthosteric binding pocket of GPCRs, and structure completeness, for template selection. The proposed method was implemented in the form of a free tool called Bio-GATS, to provide the user with easy selection of the appropriate template for a query GPCR sequence. Bio-GATS was successfully validated with recent published benchmarking datasets. An application to an olfactory receptor to select an appropriate template has also been provided as a case study.

Biased ligands at opioid receptors: Current status and future directions

The opioid crisis represents a major worldwide public health crisis that has accelerated the search for safer and more effective opioids. Over the past few years, the identification of biased opioid ligands capable of eliciting selective functional responses has provided an alternative avenue to develop novel therapeutics without the side effects of current opioid medications. However, whether biased agonism or other pharmacological properties, such as partial agonism (or low efficacy), account for the therapeutic benefits remains questionable. Here, we provide a summary of the current status of biased opioid ligands that target the μ- and κ-opioid receptors and highlight advances in preclinical and clinical trials of some of these ligands. We also discuss an example of structure-based biased ligand discovery at the μ-opioid receptor, an approach that could revolutionize drug discovery at opioid and other receptors. Last, we briefly discuss caveats and future directions for this important area of research.

Methods for Molecular Modelling of Protein Complexes

Biological processes are often mediated by complexes formed between proteins and various biomolecules. The 3D structures of such protein-biomolecule complexes provide insights into the molecular mechanism of their action. The structure of these complexes can be predicted by various computational methods. Choosing an appropriate method for modelling depends on the category of biomolecule that a protein interacts with and the availability of structural information about the protein and its interacting partner. We intend for the contents of this chapter to serve as a guide as to what software would be the most appropriate for the type of data at hand and the kind of 3D complex structure required. Particularly, we have dealt with protein-small molecule ligand, protein-peptide, protein-protein, and protein-nucleic acid interactions.Most, if not all, model building protocols perform some sampling and scoring. Typically, several alternate conformations and configurations of the interactors are sampled. Each such sample is then scored for optimization. To boost the confidence in these predicted models, their assessment using other independent scoring schemes besides the inbuilt/default ones would prove to be helpful. This chapter also lists such software and serves as a guide to gauge the fidelity of modelled structures of biomolecular complexes.

Structure-Based Discovery of Novel Ligands for the Orexin 2 Receptor

The orexin receptors are peptide-sensing G protein-coupled receptors that are intimately linked with regulation of the sleep/wake cycle. We used a recently solved X-ray structure of the orexin receptor subtype 2 in computational docking calculations with the aim to identify additional ligands with unprecedented chemotypes. We found validated ligands with a high hit rate of 29% out of those tested, none of them showing selectivity with respect to the orexin receptor subtype 1. Furthermore, of the higher-affinity compounds examined, none showed any agonist activity. While novel chemical structures can thus be found, selectivity is a challenge owing to the largely identical binding pockets.

The Se…S/N interactions as a possible mechanism of δ-aminolevulinic acid dehydratase enzyme inhibition by organoselenium compounds: A computational study

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DPDS and PSA interacts with cysteine residues from AlaD active site.

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The Se…S interactions could be involved in the δ-AlaD inhibition.

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δ-AlaD from Cucumis sativus does not present cysteine residues in the active site.

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Se…N interactions could be involved in the organoselenium action.

Organoselenium compounds present many pharmacological properties and are promising drugs. However, toxicological effects associated with inhibition of thiol-containing enzymes, such as the δ-aminolevulinic acid dehydratase (δ-AlaD), have been described. The molecular mechanism(s) by which they inhibit thiol-containing enzymes at the atomic level, is still not well known. The use of computational methods to understand the physical–chemical properties and biological activity of chemicals is essential to the rational design of new drugs. In this work, we propose an in silico study to understand the δ-AlaD inhibition mechanism by diphenyl diselenide (DPDS) and its putative metabolite, phenylseleninic acid (PSA), using δ-AlaD enzymes from Homo sapiens (Hsδ-AlaD), Drosophila melanogaster (Dmδ-AlaD) and Cucumis sativus (Csδ-AlaD). Protein modeling homology, molecular docking, and DFT calculations are combined in this study. According to the molecular docking, DPDS and PSA might bind in the Hsδ-AlaD and Dmδ-AlaD active sites interacting with the cysteine residues by Se^…S interactions. On the other hand, the DPDS does not access the active site of the Csδ-AlaD (a non-thiol protein), while the PSA interacts with the amino acids residues from the active site, such as the Lys291. These interactions might lead to the formation of a covalent bond, and consequently, to the enzyme inhibition. In fact, DFT calculations (mPW1PW91/def2TZVP) demonstrated that the selenylamide bond formation is energetically favored. The in silico data showed here are in accordance with previous experimental studies, and help us to understand the reactivity and biological activity of organoselenium compounds.

Discovery, Optimization, and Characterization of ML417: A Novel and Highly Selective D3 Dopamine Receptor Agonist

To identify novel D₃ dopamine receptor (D3R) agonists, we conducted a high-throughput screen using a β-arrestin recruitment assay. Counterscreening of the hit compounds provided an assessment of their selectivity, efficacy, and potency. The most promising scaffold was optimized through medicinal chemistry resulting in enhanced potency and selectivity. The optimized compound, ML417 (20), potently promotes D3R-mediated β-arrestin translocation, G protein activation, and ERK1/2 phosphorylation (pERK) while lacking activity at other dopamine receptors. Screening of ML417 against multiple G protein-coupled receptors revealed exceptional global selectivity. Molecular modeling suggests that ML417 interacts with the D3R in a unique manner, possibly explaining its remarkable selectivity. ML417 was also found to protect against neurodegeneration of dopaminergic neurons derived from iPSCs. Together with promising pharmacokinetics and toxicology profiles, these results suggest that ML417 is a novel and uniquely selective D3R agonist that may serve as both a research tool and a therapeutic lead for the treatment of neuropsychiatric disorders.

Performance of virtual screening against GPCR homology models: Impact of template selection and treatment of binding site plasticity

Rational drug design for G protein-coupled receptors (GPCRs) is limited by the small number of available atomic resolution structures. We assessed the use of homology modeling to predict the structures of two therapeutically relevant GPCRs and strategies to improve the performance of virtual screening against modeled binding sites. Homology models of the D2 dopamine (D2R) and serotonin 5-HT2A receptors (5-HT2AR) were generated based on crystal structures of 16 different GPCRs. Comparison of the homology models to D2R and 5-HT2AR crystal structures showed that accurate predictions could be obtained, but not necessarily using the most closely related template. Assessment of virtual screening performance was based on molecular docking of ligands and decoys. The results demonstrated that several templates and multiple models based on each of these must be evaluated to identify the optimal binding site structure. Models based on aminergic GPCRs showed substantial ligand enrichment and there was a trend toward improved virtual screening performance with increasing binding site accuracy. The best models even yielded ligand enrichment comparable to or better than that of the D2R and 5-HT2AR crystal structures. Methods to consider binding site plasticity were explored to further improve predictions. Molecular docking to ensembles of structures did not outperform the best individual binding site models, but could increase the diversity of hits from virtual screens and be advantageous for GPCR targets with few known ligands. Molecular dynamics refinement resulted in moderate improvements of structural accuracy and the virtual screening performance of snapshots was either comparable to or worse than that of the raw homology models. These results provide guidelines for successful application of structure-based ligand discovery using GPCR homology models.

G Protein-Coupled Receptors in Asthma Therapy: Pharmacology and Drug Action

Asthma is a heterogeneous inflammatory disease of the airways that is associated with airway hyperresponsiveness and airflow limitation. Although asthma was once simply categorized as atopic or nonatopic, emerging analyses over the last few decades have revealed a variety of asthma endotypes that are attributed to numerous pathophysiological mechanisms. The classification of asthma by endotype is primarily routed in different profiles of airway inflammation that contribute to bronchoconstriction. Many asthma therapeutics target G protein-coupled receptors (GPCRs), which either enhance bronchodilation or prevent bronchoconstriction. Short-acting and long-acting β 2-agonists are widely used bronchodilators that signal through the activation of the β 2-adrenergic receptor. Short-acting and long-acting antagonists of muscarinic acetylcholine receptors are used to reduce bronchoconstriction by blocking the action of acetylcholine. Leukotriene antagonists that block the signaling of cysteinyl leukotriene receptor 1 are used as an add-on therapy to reduce bronchoconstriction and inflammation induced by cysteinyl leukotrienes. A number of GPCR-targeting asthma drug candidates are also in different stages of development. Among them, antagonists of prostaglandin D2 receptor 2 have advanced into phase III clinical trials. Others, including antagonists of the adenosine A2B receptor and the histamine H4 receptor, are in early stages of clinical investigation. In the past decade, significant research advancements in pharmacology, cell biology, structural biology, and molecular physiology have greatly deepened our understanding of the therapeutic roles of GPCRs in asthma and drug action on these GPCRs. This review summarizes our current understanding of GPCR signaling and pharmacology in the context of asthma treatment. SIGNIFICANCE STATEMENT: Although current treatment methods for asthma are effective for a majority of asthma patients, there are still a large number of patients with poorly controlled asthma who may experience asthma exacerbations. This review summarizes current asthma treatment methods and our understanding of signaling and pharmacology of G protein-coupled receptors (GPCRs) in asthma therapy, and discusses controversies regarding the use of GPCR drugs and new opportunities in developing GPCR-targeting therapeutics for the treatment of asthma.

Comparative Docking to Distinct G Protein-Coupled Receptor Conformations Exclusively Yields Ligands with Agonist Efficacy

G protein-coupled receptors exist in a whole spectrum of conformations that are stabilized by the binding of ligands with different efficacy or intracellular effector proteins. Here, we investigate whether three-dimensional structures of receptor conformations in different states of activation can be used to enrich ligands with agonist behavior in prospective docking calculations. We focused on the β ₂-adrenergic receptor, as it is currently the receptor with the highest number of active-state crystal structures. Comparative docking calculations to distinct conformations of the receptor were used for the in silico prediction of ligands with agonist efficacy. The pharmacology of molecules selected based on these predictions was characterized experimentally, resulting in a hit rate of 37% ligands, all of which were agonists. The ligands furthermore contain a pyrazole moiety that has previously not been described for β ₂-adrenergic receptor ligands, and one of them shows an intrinsic efficacy comparable to salbutamol. SIGNIFICANCE STATEMENT: Structure-based ligand design for G protein-coupled receptors crucially depends on receptor conformation and, hence, their activation state. We explored the influence of using multiple active-conformation X-ray structures on the hit rate of docking calculations to find novel agonists, and how to predict the most fruitful strategy to apply. The results suggest that aggregating the ranks of molecules across docking calculations to more than one active-state structure exclusively yields agonists.

Influence of the Structural Accuracy of Homology Models on Their Applicability to Docking-Based Virtual Screening: The β2 Adrenergic Receptor as a Case Study

How accurate do structures of the β₂ adrenergic receptor (β₂AR) need to be to effectively serve as platforms for docking-based virtual screening campaigns? To answer this research question, here, we targeted through controlled virtual screening experiments 23 homology models of the β₂AR endowed with different levels of structural accuracy. Subsequently, we studied the correlation between virtual screening performance and structural accuracy of the targeted models. Moreover, we studied the correlation between virtual screening performance and template/target receptor sequence identity. Our study demonstrates that docking-based virtual screening campaigns targeting homology models of the β₂AR, in the majority of the cases, yielded results that exceeded random expectations in terms of area under the receiver operating characteristic curve (ROC AUC). Moreover, with the most effective scoring method, over one-third and one-quarter of the models yielded results that exceeded random expectation also in terms of enrichment factors (EF1, EF5, and EF10) and BEDROC (α = 160.9), respectively. Not surprisingly, we found a detectable linear correlation between virtual screening performance and structural accuracy of the ligand-binding cavity. We also found a detectable linear correlation between virtual screening performance and structural accuracy of the second extracellular loop (EL2). Finally, our data indicate that, although there is no detectable linear correlation between virtual screening performance and template/β₂AR sequence identity, models built on the basis of templates that show high sequence identity with the β₂AR, especially within the ligand-biding cavity, performed consistently well. Conversely, models with lower sequence identity displayed performance levels that ranged from very good to random, with no apparent correlation with the sequence identity itself.

Integrated computational and Drosophila cancer model platform captures previously unappreciated chemicals perturbing a kinase network

Drosophila provides an inexpensive and quantitative platform for measuring whole animal drug response. A complementary approach is virtual screening, where chemical libraries can be efficiently screened against protein target(s). Here, we present a unique discovery platform integrating structure-based modeling with Drosophila biology and organic synthesis. We demonstrate this platform by developing chemicals targeting a Drosophila model of Medullary Thyroid Cancer (MTC) characterized by a transformation network activated by oncogenic dRet^M955T. Structural models for kinases relevant to MTC were generated for virtual screening to identify unique preliminary hits that suppressed dRet^M955T-induced transformation. We then combined features from our hits with those of known inhibitors to create a ‘hybrid’ molecule with improved suppression of dRet^M955T transformation. Our platform provides a framework to efficiently explore novel kinase inhibitors outside of explored inhibitor chemical space that are effective in inhibiting cancer networks while minimizing whole body toxicity.

Effective and safe treatment of multigenic diseases often involves drugs that address multiple points along disease networks, i.e., polypharmacology. Polypharmacology is increasingly appreciated as a potentially desirable property of kinase drugs. However, most known drugs that interact with multiple targets have been identified as such by chance and most polypharmacological compounds are not chemically unique, resembling structures of known kinase inhibitors. The fruit fly Drosophila provides an inexpensive, rapid, quantitative, whole animal screening platform that has the potential to complement computational approaches. We present a chemical genetics approach that efficiently combines Drosophila with structural prediction and virtual screening, creating a unique discovery platform. We demonstrate the utility of our approach by developing useful small molecules targeting a kinase network in a Drosophila model of Medullary Thyroid Cancer (MTC) driven by oncogenic dRet^M955T.

Current Advances in Studying Clinically Relevant Transporters of the Solute Carrier (SLC) Family by Connecting Computational Modeling and Data Science

Organic anion and cation transporting proteins (OATs, OATPs, and OCTs), as well as the Multidrug and Toxin Extrusion (MATE) transporters of the Solute Carrier (SLC) family are playing a pivotal role in the discovery and development of new drugs due to their involvement in drug disposition, drug-drug interactions, adverse drug effects and related toxicity. Computational methods to understand and predict clinically relevant transporter interactions can provide useful guidance at early stages in drug discovery and design, especially if they include contemporary data science approaches. In this review, we summarize the current state-of-the-art of computational approaches for exploring ligand interactions and selectivity for these drug (uptake) transporters. The computational methods discussed here by highlighting interesting examples from the current literature are ranging from semiautomatic data mining and integration, to ligand-based methods (such as quantitative structure-activity relationships, and combinatorial pharmacophore modeling), and finally structure-based methods (such as comparative modeling, molecular docking, and molecular dynamics simulations). We are focusing on promising computational techniques such as fold-recognition methods, proteochemometric modeling or techniques for enhanced sampling of protein conformations used in the context of these ADMET-relevant SLC transporters with a special focus on methods useful for studying ligand selectivity.

Ultra-large library docking for discovering new chemotypes

Despite intense interest in expanding chemical space, libraries containing hundreds-of-millions to billions of diverse molecules have remained inaccessible. Here we investigate structure-based docking of 170 million make-on-demand compounds from 130 well-characterized reactions. The resulting library is diverse, representing over 10.7 million scaffolds that are otherwise unavailable. For each compound in the library, docking against AmpC β-lactamase (AmpC) and the D4 dopamine receptor were simulated. From the top-ranking molecules, 44 and 549 compounds were synthesized and tested for interactions with AmpC and the D4 dopamine receptor, respectively. We found a phenolate inhibitor of AmpC, which revealed a group of inhibitors without known precedent. This molecule was optimized to 77 nM, which places it among the most potent non-covalent AmpC inhibitors known. Crystal structures of this and other AmpC inhibitors confirmed the docking predictions. Against the D4 dopamine receptor, hit rates fell almost monotonically with docking score, and a hit-rate versus score curve predicted that the library contained 453,000 ligands for the D4 dopamine receptor. Of 81 new chemotypes discovered, 30 showed submicromolar activity, including a 180-pM subtype-selective agonist of the D4 dopamine receptor.

Reliability of Docking-Based Virtual Screening for GPCR Ligands with Homology Modeled Structures: A Case Study of the Angiotensin II Type I Receptor

The number of solved G-protein-coupled receptor (GPCR) crystal structures has expanded rapidly, but most GPCR structures remain unsolved. Therefore, computational techniques, such as homology modeling, have been widely used to produce the theoretical structures of various GPCRs for structure-based drug design (SBDD). Due to the low sequence similarity shared by the transmembrane domains of GPCRs, accurate prediction of GPCR structures by homology modeling is quite challenging. In this study, angiotensin II type I receptor (AT1R) was taken as a typical case to assess the reliability of class A GPCR homology models for SBDD. Four homology models of angiotensin II type I receptor (AT1R) at the inactive state were built based on the crystal structures of CXCR4 chemokine receptor, CCR5 chemokine receptor, and δ-opioid receptor, and refined through molecular dynamics (MD) simulations and induced-fit docking, to allow for backbone and side-chain flexibility. Then, the quality of the homology models was assessed relative to the crystal structures in terms of two criteria commonly used in SBDD: prediction accuracy of ligand-binding poses and screening power of docking-based virtual screening. It was found that the crystal structures outperformed the homology models prior to any refinement in both assessments. MD simulations could generally improve the docking results for both the crystal structures and homology models. Moreover, the optimized homology model refined by MD simulations and induced-fit docking even shows a similar performance of the docking assessment to the crystal structures. Our results indicate that it is possible to establish a reliable class A GPCR homology model for SBDD through the refinement by integrating multiple molecular modeling techniques.

Molecular Modeling of Drug-Transporter Interactions-An International Transporter Consortium Perspective

Membrane transporters play diverse roles in the pharmacokinetics and pharmacodynamics of small-molecule drugs. Understanding the mechanisms of drug-transporter interactions at the molecular level is, therefore, essential for the design of drugs with optimal therapeutic effects. This white paper examines recent progress, applications, and challenges of molecular modeling of membrane transporters, including modeling techniques that are centered on the structures of transporter ligands, and those focusing on the structures of the transporters. The goals of this article are to illustrate current best practices and future opportunities in using molecular modeling techniques to understand and predict transporter-mediated effects on drug disposition and efficacy.Membrane transporters from the solute carrier (SLC) and ATP-binding cassette (ABC) superfamilies regulate the cellular uptake, efflux, and homeostasis of many essential nutrients and significantly impact the pharmacokinetics of drugs; further, they may provide targets for novel therapeutics as well as facilitate prodrug approaches. Because of their often broad substrate selectivity they are also implicated in many undesirable and sometimes life-threatening drug-drug interactions (DDIs).^5,6.

Selectivity Challenges in Docking Screens for GPCR Targets and Antitargets

To investigate large library docking's ability to find molecules with joint activity against on-targets and selectivity versus antitargets, the dopamine D2 and serotonin 5-HT2A receptors were targeted, seeking selectivity against the histamine H1 receptor. In a second campaign, κ-opioid receptor ligands were sought with selectivity versus the μ-opioid receptor. While hit rates ranged from 40% to 63% against the on-targets, they were just as good against the antitargets, even though the molecules were selected for their putative lack of binding to the off-targets. Affinities, too, were often as good or better for the off-targets. Even though it was occasionally possible to find selective molecules, such as a mid-nanomolar D2/5-HT2A ligand with 21-fold selectivity versus the H1 receptor, this was the exception. Whereas false-negatives are tolerable in docking screens against on-targets, they are intolerable against antitargets; addressing this problem may demand new strategies in the field.

Recent Trends and Applications of Molecular Modeling in GPCR⁻Ligand Recognition and Structure-Based Drug Design

G protein-coupled receptors represent the largest family of human membrane proteins and are modulated by a variety of drugs and endogenous ligands. Molecular modeling techniques, especially enhanced sampling methods, have provided significant insight into the mechanism of GPCR–ligand recognition. Notably, the crucial role of the membrane in the ligand-receptor association process has earned much attention. Additionally, docking, together with more accurate free energy calculation methods, is playing an important role in the design of novel compounds targeting GPCRs. Here, we summarize the recent progress in the computational studies focusing on the above issues. In the future, with continuous improvement in both computational hardware and algorithms, molecular modeling would serve as an indispensable tool in a wider scope of the research concerning GPCR–ligand recognition as well as drug design targeting GPCRs.