Discovery of novel chemotypes to a G-protein-coupled receptor through ligand-steered homology modeling and structure-based virtual screening

Molecular determinants of antagonist interactions with chemokine receptors CCR2 and CCR5

By driving monocyte chemotaxis, the chemokine receptor CCR2 shapes inflammatory responses and the formation of tumor microenvironments. This makes it a promising target in inflammation and immuno-oncology; however, despite extensive efforts, there are no FDA-approved CCR2-targeting therapeutics. Cited challenges include the redundancy of the chemokine system, suboptimal properties of compound candidates, and species differences that confound the translation of results from animals to humans. Structure-based drug design can rationalize and accelerate the discovery and optimization of CCR2 antagonists to address these challenges. The prerequisites for such efforts include an atomic-level understanding of the molecular determinants of action of existing antagonists. In this study, using molecular docking and artificial-intelligence-powered compound library screening, we uncover the structural principles of small molecule antagonism and selectivity towards CCR2 and its sister receptor CCR5. CCR2 orthosteric inhibitors are shown to universally occupy an inactive-state-specific tunnel between receptor helices 1 and 7; we also discover an unexpected role for an extra-helical groove accessible through this tunnel, suggesting its potential as a new targetable interface for CCR2 and CCR5 modulation. By contrast, only shape complementarity and limited helix 8 hydrogen bonding govern the binding of various chemotypes of allosteric antagonists. CCR2 residues S1012.63 and V2446.36 are implicated as determinants of CCR2/CCR5 and human/mouse orthosteric and allosteric antagonist selectivity, respectively, and the role of S1012.63 is corroborated through experimental gain-of-function mutagenesis. We establish a critical role of induced fit in antagonist recognition, reveal strong chemotype selectivity of existing structures, and demonstrate the high predictive potential of a new deep-learning-based compound scoring function. Finally, this study expands the available CCR2 structural landscape with computationally generated chemotype-specific models well-suited for structure-based antagonist design.

Hic Sunt Dracones: Molecular Docking in Uncharted Territories with Structures from AlphaFold2 and RoseTTAfold

AlphaFold2 and RoseTTAfold impress with their high accuracy in protein structure prediction. However, for structure-based virtual screenings, not only the overall structure but especially the binding sites need to be accurately predicted. In this work, the docking performance for 66 targets with known ligands but without experimental structures available in the protein data bank was elucidated. The results suggest that using an experimental surrogate-ligand complex is often superior over homology models, and only at low sequence identity to the closest homologue AlphaFold2 structures show an equal performance. The generally high fluctuation of receiver operating characteristic area under the curve values obtained for different homology models suggests that multiple combinations of docking programs and homology models should be tested prior to prospective virtual screenings, and in some cases post-processing of crude models might be necessary.

Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development

The use of computer-aided drug design (CADD) for the identification of lead compounds in radiotracer development is steadily increasing. Traditional CADD methods, such as structure-based and ligand-based virtual screening and optimization, have been successfully utilized in many drug discovery programs and are highlighted throughout this review. First, we discuss the use of virtual screening for hit identification at the beginning of drug discovery programs. This is followed by an analysis of how the hits derived from virtual screening can be filtered and culled to highly probable candidates to test in in vitro assays. We then illustrate how CADD can be used to optimize the potency of experimentally validated hit compounds from virtual screening for use in positron emission tomography (PET). Finally, we conclude with a survey of the newest techniques in CADD employing machine learning (ML).

How good are AlphaFold models for docking-based virtual screening?

A crucial component in structure-based drug discovery is the availability of high-quality three-dimensional structures of the protein target. Whenever experimental structures were not available, homology modeling has been, so far, the method of choice. Recently, AlphaFold (AF), an artificial-intelligence-based protein structure prediction method, has shown impressive results in terms of model accuracy. This outstanding success prompted us to evaluate how accurate AF models are from the perspective of docking-based drug discovery. We compared the high-throughput docking (HTD) performance of AF models with their corresponding experimental PDB structures using a benchmark set of 22 targets. The AF models showed consistently worse performance using four docking programs and two consensus techniques. Although AlphaFold shows a remarkable ability to predict protein architecture, this might not be enough to guarantee that AF models can be reliably used for HTD, and post-modeling refinement strategies might be key to increase the chances of success.

Structure-based virtual screening and molecular dynamics of potential inhibitors targeting sodium-bile acid co-transporter of carcinogenic liver fluke Clonorchis sinensis

Background

Clonorchis sinensis requires bile acid transporters as this fluke inhabits bile juice-filled biliary ducts, which provide an extreme environment. Clonorchis sinensis sodium-bile acid co-transporter (CsSBAT) is indispensable for the fluke’s survival in the final host, as it circulates taurocholate and prevents bile toxicity in the fluke; hence, it is recognized as a useful drug target.

Methodology and principal findings

In the present study, using structure-based virtual screening approach, we presented inhibitor candidates targeting a bile acid-binding pocket of CsSBAT. CsSBAT models were built using tertiary structure modeling based on a bile acid transporter template (PDB ID: 3zuy and 4n7x) and were applied into AutoDock Vina for competitive docking simulation. First, potential compounds were identified from PubChem (holding more than 100,000 compounds) by applying three criteria: i) interacting more favorably with CsSBAT than with a human homolog, ii) intimate interaction to the inward- and outward-facing conformational states, iii) binding with CsSBAT preferably to natural bile acids. Second, two compounds were identified following the Lipinski’s rule of five. Third, other two compounds of molecular weight higher than 500 Da (Mr > 500 Da) were presumed to efficiently block the transporter via a feasible rational screening strategy. Of these candidates, compound 9806452 exhibited the least hepatotoxicity that may enhance drug-likeness properties.

Conclusions

It is proposed that compound 9806452 act as a potential inhibitor toward CsSBAT and further studies are warranted for drug development process against clonorchiasis.

AlignMe: an update of the web server for alignment of membrane protein sequences

The AlignMe web server is dedicated to accurately aligning sequences of membrane proteins, a particularly challenging task due to the strong evolutionary divergence and the low compositional complexity of hydrophobic membrane-spanning proteins. AlignMe can create pairwise alignments of either two primary amino acid sequences or two hydropathy profiles. The web server for AlignMe has been continuously available for >10 years, supporting 1000s of users per year. Recent improvements include anchoring, multiple submissions, and structure visualization. Anchoring is the ability to constrain a position in an alignment, which allows expert information about related residues in proteins to be incorporated into an alignment without manual modification. The original web interface to the server limited the user to one alignment per submission, hindering larger scale studies. Now, batches of alignments can be initiated with a single submission. Finally, to provide structural context for the relationship between proteins, sequence similarity can now be mapped onto one or more structures (or structural models) of the proteins being aligned, by links to MutationExplorer, a web-based visualization tool. Together with a refreshed user interface, these features further enhance an important resource in the membrane protein community. The AlignMe web server is freely available at https://www.bioinfo.mpg.de/AlignMe/.

Pharmaceutical interest of in-silico approaches

The virtual environment within the computer using software performed on the computer is known as in-silico studies. These drugs designing software play a vital task in discovering new drugs in the field of pharmaceuticals. These designing programs and software are employed in gene sequencing, molecular modeling, and in assessing the three-dimensional structure of the molecule, which can further be used in drug designing and development. Drug development and discovery is not only a powerful, extensive, and an interdisciplinary system but also a very complex and time-consuming method. This book chapter mainly focused on different types of in-silico approaches along with their pharmaceutical applications in numerous diseases.

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.

Discovery and Validation of Lmj_04_BRCT Domain, a Novel Therapeutic Target: Identification of Candidate Drugs for Leishmaniasis

Since many of the currently available antileishmanial treatments exhibit toxicity, low effectiveness, and resistance, search and validation of new therapeutic targets allowing the development of innovative drugs have become a worldwide priority. This work presents a structure-based drug discovery strategy to validate the Lmj_04_BRCT domain as a novel therapeutic target in Leishmania spp. The structure of this domain was explored using homology modeling, virtual screening, and molecular dynamics studies. Candidate compounds were validated in vitro using promastigotes of Leishmania major, L. amazonensis, and L. infantum, as well as primary mouse macrophages infected with L. major. The novel inhibitor CPE2 emerged as the most active of a group of compounds against Leishmania, being able to significantly reduce the viability of promastigotes. CPE2 was also active against the intracellular forms of the parasites and significantly reduced parasite burden in murine macrophages without exhibiting toxicity in host cells. Furthermore, L. major promastigotes treated with CPE2 showed significant lower expression levels of several genes (α-tubulin, Cyclin CYCA, and Yip1) related to proliferation and treatment resistance. Our in silico and in vitro studies suggest that the Lmj_04_BRCT domain and its here disclosed inhibitors are new potential therapeutic options against leishmaniasis.

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.

Molecular modeling study of the testosterone metabolizing enzyme UDP-glucuronosyltransferase 2B17

The dominant sex hormone testosterone is mainly metabolized by liver enzymes belonging to the uridine-diphospho (UDP) glucuronosyltransferase (UGT) family. These enzymes are the main phase II enzymes, and they have an important role in the detoxification of endogenous and exogenous compounds in humans. The aim of the present study was to improve the understanding of the binding properties of UGT2B17. A homology modelling procedure was used to generate models of the UGT2B17 enzyme based on templates with known crystal structures. Molecular docking of inhibitors was performed to gain further insights in the interactions between ligand and binding site, and to determine which of the models had the best accuracy. ROC curves were made to evaluate the ability of the models to differentiate between binders (inhibitors) and non-binders (decoys). When comparing the four models, which were based on four different crystal structures, the model based on the 4AMG crystal structure was the most accurate in distinguishing between true binders and non-binders. Investigating pharmacological UGT2B17 inhibition may provide novel treatment for patients with low testosterone levels. Such treatment may elevate endogenous testosterone levels and provide a more predictable increase in serum concentrations rather than un-physiological elevation of serum levels through direct treatment with testosterone, and this could be favorable both for giving a predictable treatment regime with reduced chances of serious adverse effects. The present study may serve as a tool in the search for novel drugs aiming for increasing testosterone levels.

A unique ligand-steered strategy for CC chemokine receptor 2 homology modeling to facilitate structure-based virtual screening

CC chemokine receptor 2 (CCR2) antagonists that disrupt CCR2/MCP-1 interaction are expected to treat a variety of inflammatory and autoimmune diseases. The lack of CCR2 crystal structure limits the application of structure-based drug design (SBDD) to this target. Although a few three-dimensional theoretical models have been reported, their accuracy remains to be improved in terms of templates and modeling approaches. In this study, we developed a unique ligand-steered strategy for CCR2 homology modeling. It starts with an initial model based on the X-ray structure of the closest homolog so far, that is, CXCR4. Then, it uses Elastic Network Normal Mode Analysis (EN-NMA) and flexible docking (FD) by AutoDock Vina software to generate ligand-induced fit models. It selects optimal model(s) as well as scoring function(s) via extensive evaluation of model performance based on a unique benchmarking set constructed by our in-house tool, that is, MUBD-DecoyMaker. The model of 81_04 presents the optimal enrichment when combined with the scoring function of PMF04, and the proposed binding mode between CCR2 and Teijin lead by this model complies with the reported mutagenesis data. To highlight the advantage of our strategy, we compared it with the only reported ligand-steered strategy for CCR2 homology modeling, that is, Discovery Studio/Ligand Minimization. Lastly, we performed prospective virtual screening based on 81_04 and CCR2 antagonist bioassay. The identification of two hit compounds, that is, E859-1281 and MolPort-007-767-945, validated the efficacy of our model and the ligand-steered strategy.

Study of combining virtual screening and antiviral treatments of the Sars-CoV-2 (Covid-19)

The recent epidemic outbreak of a novel human coronavirus called SARS-CoV-2 and causing the respiratory tract disease COVID-19 has reached worldwide resonance and a global effort is being undertaken to characterize the molecular features and evolutionary origins of this virus. Therefore, rapid and accurate identification of pathogenic viruses plays a vital role in selecting appropriate treatments, saving people's lives and preventing epidemics. Additionally, general treatments, coronavirus-specific treatments, and antiviral treatments useful in fighting COVID-19 are addressed. This review sets out to shed light on the SARS-CoV-2 and host receptor recognition, a crucial factor for successful virus infection and taking immune-informatics approaches to identify B- and T-cell epitopes for surface glycoprotein of SARS-CoV-2. A variety of improved or new approaches also have been developed. It is anticipated that this will assist researchers and clinicians in developing better techniques for timely and effective detection of coronavirus infection. Moreover, the genomic sequence of the virus responsible for COVID-19, as well as the experimentally determined three-dimensional structure of the Main protease (Mpro) is available. The reported structure of the target Mpro was described in this review to identify potential drugs for COVID-19 using virtual high throughput screening.

In silico Drug Repurposing for COVID-19: Targeting SARS-CoV-2 Proteins through Docking and Consensus Ranking

In December 2019, an infectious disease caused by the coronavirus SARS-CoV-2 appeared in Wuhan, China. This disease (COVID-19) spread rapidly worldwide, and on March 2020 was declared a pandemic by the World Health Organization (WHO). Today, over 21 million people have been infected, with more than 750.000 casualties. Today, no vaccine or antiviral drug is available. While the development of a vaccine might take at least a year, and for a novel drug, even longer; finding a new use to an old drug (drug repurposing) could be the most effective strategy. We present a docking-based screening using a quantum mechanical scoring of a library built from approved drugs and compounds undergoing clinical trials, against three SARS-CoV-2 target proteins: the spike or S-protein, and two proteases, the main protease and the papain-like protease. The S-protein binds directly to the Angiotensin Converting Enzyme 2 receptor of the human host cell surface, while the two proteases process viral polyproteins. Following the analysis of our structure-based compound screening, we propose several structurally diverse compounds (either FDA-approved or in clinical trials) that could display antiviral activity against SARS-CoV-2. Clearly, these compounds should be further evaluated in experimental assays and clinical trials to confirm their actual activity against the disease. We hope that these findings may contribute to the rational drug design against COVID-19.

Identification and experimental confirmation of novel cGMP efflux inhibitors by virtual ligand screening of vardenafil-analogues

BACKGROUND

Clinical studies have reported overexpression of PDE5 and elevation of intracellular cyclic GMP in various types of cancer cells. ABCC5 transports cGMP out of the cells with high affinity. PDE5 inhibitors prevent both cellular metabolism and cGMP efflux by inhibiting ABCC5 as well as PDE5. Increasing intracellular cGMP is hypothesized to promote apoptosis and growth restriction in tumor cells and also has potential for clinical use in treatment of cardiovascular disease and erectile dysfunction. Vardenafil is a potent inhibitor of both PDE5 and ABCC5-mediated cGMP cellular efflux. Nineteen novel vardenafil analogs that have been predicted as potent inhibitors by VLS were chosen for tests of their ability to inhibit ATP- dependent transport of cGMP by measuring the accumulation of cyclic GMP in inside-out vesicles.

AIM

In this study, we investigated the ability of nineteen new compounds to inhibit ABCC5- mediated cGMP transport. We also determined the Ki values of the six most potent compounds.

METHODS

Preparation of human erythrocyte inside out vesicles and transport assay.

RESULTS

Ki values for six of nineteen compounds that showed more than 50 % inhibition of cGMP transport in the screening test were determined and ranged from 1.1 to 23.1 μM. One compound was significantly more potent than the positive control, sildenafil.

CONCLUSION

Our findings show that computational screening correctly identified vardenafil-analogues that potently inhibit cGMP efflux-pumps from cytosol and could have substantial clinical potential in treatment of patients with diverse disorders.

Structural Genomics of SARS-CoV-2 Indicates Evolutionary Conserved Functional Regions of Viral Proteins

During its first two and a half months, the recently emerged 2019 novel coronavirus, SARS-CoV-2, has already infected over one-hundred thousand people worldwide and has taken more than four thousand lives. However, the swiftly spreading virus also caused an unprecedentedly rapid response from the research community facing the unknown health challenge of potentially enormous proportions. Unfortunately, the experimental research to understand the molecular mechanisms behind the viral infection and to design a vaccine or antivirals is costly and takes months to develop. To expedite the advancement of our knowledge, we leveraged data about the related coronaviruses that is readily available in public databases and integrated these data into a single computational pipeline. As a result, we provide comprehensive structural genomics and interactomics roadmaps of SARS-CoV-2 and use this information to infer the possible functional differences and similarities with the related SARS coronavirus. All data are made publicly available to the research community.

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.

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.

Salvianolic acids from antithrombotic Traditional Chinese Medicine Danshen are antagonists of human P2Y1 and P2Y12 receptors

Many hemorheologic Traditional Chinese Medicines (TCMs) that are widely-used clinically lack molecular mechanisms of action. We hypothesized that some of the active components of hemorheologic TCMs may function through targeting prothrombotic P2Y1 and/or P2Y12 receptors. The interactions between 253 antithrombotic compounds from TCM and these two G protein-coupled P2Y receptors were evaluated using virtual screening. Eleven highly ranked hits were further tested in radioligand binding and functional assays. Among these compounds, salvianolic acid A and C antagonized the activity of both P2Y1 and P2Y12 receptors in the low µM range, while salvianolic acid B antagonized the P2Y12 receptor. These three salvianolic acids are the major active components of the broadly-used hemorheologic TCM Danshen (Salvia militorrhiza), the antithrombotic molecular mechanisms of which were largely unknown. Thus, the combination of virtual screening and experimental validation identified potential mechanisms of action of multicomponent drugs that are already employed clinically.

A fluorescence nanoscopy marker for corticotropin-releasing hormone type 1 receptor: computer design, synthesis, signaling effects, super-resolved fluorescence imaging, and in situ affinity constant in cells

A new fluorescent marker for CRHR1 shows an antagonist effect and suitability for super resolution fluorescence microscopy.

Methods for Virtual Screening of GPCR Targets: Approaches and Challenges

Virtual screening (VS) has become an integral part of the drug discovery process and is a valuable tool for finding novel chemical starting points for GPCR targets. Ligand-based VS makes use of biochemical data for known, active compounds and has been applied successfully to many diverse GPCRs. Recent progress in GPCR X-ray crystallography has made it possible to incorporate detailed structural information into the VS process. This chapter outlines the latest VS techniques along with examples that highlight successful applications of these methods. Best practices for increasing the likelihood of VS success, as well as ongoing challenges, are also discussed.

The BigLEN-GPR171 Peptide Receptor System Within the Basolateral Amygdala Regulates Anxiety-Like Behavior and Contextual Fear Conditioning

Studies show that neuropeptide-receptor systems in the basolateral amygdala (BLA) play an important role in the pathology of anxiety and other mood disorders. Since GPR171, a recently deorphanized receptor for the abundant neuropeptide BigLEN, is expressed in the BLA, we investigated its role in fear and anxiety-like behaviors. To carry out these studies we identified small molecule ligands using a homology model of GPR171 to virtually screen a library of compounds. One of the hits, MS0021570_1, was identified as a GPR171 antagonist based on its ability to block (i) BigLEN-mediated activation of GPR171 in heterologous cells, (ii) BigLEN-mediated hyperpolarization of BLA pyramidal neurons, and (iii) feeding induced by DREADD-mediated activation of BigLEN containing AgRP neurons in the arcuate nucleus. The role of GPR171 in anxiety-like behavior or fear conditioning was evaluated following systemic or intra-BLA administration of MS0021570_1, as well as following lentiviral-mediated knockdown of GPR171 in the BLA. We find that systemic administration of MS0021570_1 attenuates anxiety-like behavior while intra-BLA administration or knockdown of GPR171 in the BLA reduces anxiety-like behavior and fear conditioning. These results indicate that the BigLEN-GPR171 system plays an important role in these behaviors and could be a novel target to develop therapeutics to treat psychiatric disorders.

Identification of a Novel Class of BRD4 Inhibitors by Computational Screening and Binding Simulations

Computational screening is a method to prioritize small-molecule compounds based on the structural and biochemical attributes built from ligand and target information. Previously, we have developed a scalable virtual screening workflow to identify novel multitarget kinase/bromodomain inhibitors. In the current study, we identified several novel N-[3-(2-oxo-pyrrolidinyl)phenyl]-benzenesulfonamide derivatives that scored highly in our ensemble docking protocol. We quantified the binding affinity of these compounds for BRD4(BD1) biochemically and generated cocrystal structures, which were deposited in the Protein Data Bank. As the docking poses obtained in the virtual screening pipeline did not align with the experimental cocrystal structures, we evaluated the predictions of their precise binding modes by performing molecular dynamics (MD) simulations. The MD simulations closely reproduced the experimentally observed protein-ligand cocrystal binding conformations and interactions for all compounds. These results suggest a computational workflow to generate experimental-quality protein-ligand binding models, overcoming limitations of docking results due to receptor flexibility and incomplete sampling, as a useful starting point for the structure-based lead optimization of novel BRD4(BD1) inhibitors.

The GPR139 reference agonists 1a and 7c, and tryptophan and phenylalanine share a common binding site

GPR139 is an orphan G protein-coupled receptor expressed in the brain, in particular in the habenula, hypothalamus and striatum. It has therefore been suggested that GPR139 is a possible target for metabolic disorders and Parkinson's disease. Several surrogate agonist series have been published for GPR139. Two series published by Shi et al. and Dvorak et al. included agonists 1a and 7c respectively, with potencies in the ten-nanomolar range. Furthermore, Isberg et al. and Liu et al. have previously shown that tryptophan (Trp) and phenylalanine (Phe) can activate GPR139 in the hundred-micromolar range. In this study, we produced a mutagenesis-guided model of the GPR139 binding site to form a foundation for future structure-based ligand optimization. Receptor mutants studied in a Ca2+ assay demonstrated that residues F1093×33, H1875×43, W2416×48 and N2717×38, but not E1083×32, are highly important for the activation of GPR139 as predicted by the receptor model. The initial ligand-receptor complex was optimized through free energy perturbation simulations, generating a refined GPR139 model in agreement with experimental data. In summary, the GPR139 reference surrogate agonists 1a and 7c, and the endogenous amino acids L-Trp and L-Phe share a common binding site, as demonstrated by mutagenesis, ligand docking and free energy calculations.

Ligand-guided homology modelling of the GABAB2 subunit of the GABAB receptor

γ-aminobutyric acid (GABA) is the main inhibitory neurotransmitter in the central nervous system, and disturbances in the GABAergic system have been implicated in numerous neurological and neuropsychiatric diseases. The GABA_B receptor is a heterodimeric class C G protein-coupled receptor (GPCR) consisting of GABA_B1a/b and GABA_B2 subunits. Two GABA_B receptor ligand binding sites have been described, namely the orthosteric GABA binding site located in the extracellular GABA_B1 Venus fly trap domain and the allosteric binding site found in the GABA_B2 transmembrane domain. To date, the only experimentally solved three-dimensional structures of the GABA_B receptor are of the Venus fly trap domain. GABA_B receptor allosteric modulators, however, show great therapeutic potential, and elucidating the structure of the GABA_B2 transmembrane domain may lead to development of novel drugs and increased understanding of the allosteric mechanism of action. Despite the lack of x-ray crystal structures of the GABA_B2 transmembrane domain, multiple crystal structures belonging to other classes of GPCRs than class A have been released within the last years. More closely related template structures are now available for homology modelling of the GABA_B receptor. Here, multiple homology models of the GABA_B2 subunit of the GABA_B receptor have been constructed using templates from class A, B and C GPCRs, and docking of five clusters of positive allosteric modulators and decoys has been undertaken to select models that enrich the active compounds. Using this ligand-guided approach, eight GABA_B2 homology models have been chosen as possible structural representatives of the transmembrane domain of the GABA_B2 subunit. To the best of our knowledge, the present study is the first to describe homology modelling of the transmembrane domain of the GABA_B2 subunit and the docking of positive allosteric modulators in the receptor.

Discovery of Mycobacterium tuberculosis InhA Inhibitors by Binding Sites Comparison and Ligands Prediction

Drug discovery is usually focused on a single protein target; in this process, existing compounds that bind to related proteins are often ignored. We describe ProBiS plugin, extension of our earlier ProBiS-ligands approach, which for a given protein structure allows prediction of its binding sites and, for each binding site, the ligands from similar binding sites in the Protein Data Bank. We developed a new database of precalculated binding site comparisons of about 290000 proteins to allow fast prediction of binding sites in existing proteins. The plugin enables advanced viewing of predicted binding sites, ligands' poses, and their interactions in three-dimensional graphics. Using the InhA query protein, an enoyl reductase enzyme in the Mycobacterium tuberculosis fatty acid biosynthesis pathway, we predicted its possible ligands and assessed their inhibitory activity experimentally. This resulted in three previously unrecognized inhibitors with novel scaffolds, demonstrating the plugin's utility in the early drug discovery process.

Identification of Novel Smoothened Ligands Using Structure-Based Docking

The seven transmembrane protein Smoothened is required for Hedgehog signaling during embryonic development and adult tissue homeostasis. Inappropriate activation of the Hedgehog signalling pathway leads to cancers such as basal cell carcinoma and medulloblastoma, and Smoothened inhibitors are now available clinically to treat these diseases. However, resistance to these inhibitors rapidly develops thereby limiting their efficacy. The determination of Smoothened crystal structures enables structure-based discovery of new ligands with new chemotypes that will be critical to combat resistance. In this study, we docked 3.2 million available, lead-like molecules against Smoothened, looking for those with high physical complementarity to its structure; this represents the first such campaign against the class Frizzled G-protein coupled receptor family. Twenty-one high-ranking compounds were selected for experimental testing, and four, representing three different chemotypes, were identified to antagonize Smoothened with IC₅₀ values better than 50 μM. A screen for analogs revealed another six molecules, with IC₅₀ values in the low micromolar range. Importantly, one of the most active of the new antagonists continued to be efficacious at the D473H mutant of Smoothened, which confers clinical resistance to the antagonist vismodegib in cancer treatment.

Galaxy7TM: flexible GPCR-ligand docking by structure refinement

G-protein-coupled receptors (GPCRs) play important physiological roles related to signal transduction and form a major group of drug targets. Prediction of GPCR-ligand complex structures has therefore important implications to drug discovery. With previously available servers, it was only possible to first predict GPCR structures by homology modeling and then perform ligand docking on the model structures. However, model structures generated without explicit consideration of specific ligands of interest can be inaccurate because GPCR structures can be affected by ligand binding. The Galaxy7TM server, freely accessible at http://galaxy.seoklab.org/7TM, improves an input GPCR structure by simultaneous ligand docking and flexible structure refinement using GALAXY methods. The server shows better performance in both ligand docking and GPCR structure refinement than commonly used programs AutoDock Vina and Rosetta MPrelax, respectively.

Allosteric ligands for the pharmacologically dark receptors GPR68 and GPR65

At least 120 non-olfactory G-protein-coupled receptors in the human genome are 'orphans' for which endogenous ligands are unknown, and many have no selective ligands, hindering the determination of their biological functions and clinical relevance. Among these is GPR68, a proton receptor that lacks small molecule modulators for probing its biology. Using yeast-based screens against GPR68, here we identify the benzodiazepine drug lorazepam as a non-selective GPR68 positive allosteric modulator. More than 3,000 GPR68 homology models were refined to recognize lorazepam in a putative allosteric site. Docking 3.1 million molecules predicted new GPR68 modulators, many of which were confirmed in functional assays. One potent GPR68 modulator, ogerin, suppressed recall in fear conditioning in wild-type but not in GPR68-knockout mice. The same approach led to the discovery of allosteric agonists and negative allosteric modulators for GPR65. Combining physical and structure-based screening may be broadly useful for ligand discovery for understudied and orphan GPCRs.

Identification of Substituted Naphthotriazolediones as Novel Tryptophan 2,3-Dioxygenase (TDO) Inhibitors through Structure-Based Virtual Screening

A structure-based virtual screening strategy, comprising homology modeling, ligand-support binding site optimization, virtual screening, and structure clustering analysis, was developed and used to identify novel tryptophan 2,3-dioxygenase (TDO) inhibitors. Compound 1 (IC50 = 711 nM), selected by virtual screening, showed inhibitory activity toward TDO and was subjected to structural modifications and molecular docking studies. This resulted in the identification of a potent TDO selective inhibitor (11e, IC50 = 30 nM), making it a potential compound for further investigation as a cancer therapeutic and other TDO-related targeted therapy.

Integrating Pharmacophore into Membrane Molecular Dynamics Simulations to Improve Homology Modeling of G Protein-coupled Receptors with Ligand Selectivity: A2A Adenosine Receptor as an Example

Homology modeling has been applied to fill in the gap in experimental G protein-coupled receptors structure determination. However, achievement of G protein-coupled receptors homology models with ligand selectivity remains challenging due to structural diversity of G protein-coupled receptors. In this work, we propose a novel strategy by integrating pharmacophore and membrane molecular dynamics (MD) simulations to improve homology modeling of G protein-coupled receptors with ligand selectivity. To validate this integrated strategy, the A2A adenosine receptor (A2A AR), whose structures in both active and inactive states have been established, has been chosen as an example. We performed blind predictions of the active-state A2A AR structure based on the inactive-state structure and compared the performance of different refinement strategies. The blind prediction model combined with the integrated strategy identified ligand-receptor interactions and conformational changes of key structural elements related to the activation of A2 A AR, including (i) the movements of intracellular ends of TM3 and TM5/TM6; (ii) the opening of ionic lock; (iii) the movements of binding site residues. The integrated strategy of pharmacophore with molecular dynamics simulations can aid in the optimization in the identification of side chain conformations in receptor models. This strategy can be further investigated in homology modeling and expand its applicability to other G protein-coupled receptor modeling, which should aid in the discovery of more effective and selective G protein-coupled receptor ligands.

Advancements in therapeutically targeting orphan GPCRs

G-protein coupled receptors (GPCRs) are popular biological targets for drug discovery and development. To date there are more than 140 orphan GPCRs, i.e., receptors whose endogenous ligands are unknown. Traditionally orphan GPCRs have been difficult to study and the development of therapeutic compounds targeting these receptors has been extremely slow although these GPCRs are considered important targets based on their distribution and behavioral phenotype as revealed by animals lacking the receptor. Recent advances in several methods used to study orphan receptors, including protein crystallography and homology modeling are likely to be useful in the identification of therapeutics targeting these receptors. In the past 13 years, over a dozen different Class A GPCRs have been crystallized; this trend is exciting, since homology modeling of GPCRs has previously been limited by the availability of solved structures. As the number of solved GPCR structures continues to grow so does the number of templates that can be used to generate increasingly accurate models of phylogenetically related orphan GPCRs. The availability of solved structures along with the advances in using multiple templates to build models (in combination with molecular dynamics simulations that reveal structural information not provided by crystallographic data and methods for modeling hard-to-predict flexible loop regions) have improved the quality of GPCR homology models. This, in turn, has improved the success rates of virtual ligand screens that use homology models to identify potential receptor binding compounds. Experimental testing of the predicted hits and validation using traditional GPCR pharmacological approaches can be used to drive ligand-based efforts to probe orphan receptor biology as well as to define the chemotypes and chemical scaffolds important for binding. As a result of these advances, orphan GPCRs are emerging from relative obscurity as a new class of drug targets.

Identifying modulators of CXC receptors 3 and 4 with tailored selectivity using multi-target docking

The G protein-coupled receptors of the C-X-C subfamily form a group among the chemokine receptors whose endogenous ligands are peptides with a common Cys-X-Cys motif. The CXC chemokine receptors 3 and 4 (CXCR3, CXCR4), which are investigated in this study, are linked to severe diseases such as cancer, multiple sclerosis, and HIV infections. Of particular interest, this receptor pair potentially forms a target for a polypharmacological drug treatment. Considering known ligands from public databases, such dual binders have not been identified yet. We therefore applied large-scale docking to the structure of CXCR4 and a homology model of CXCR3 with the goal to predict such dual binders, as well as compounds selective for either one of the receptors. Using signaling and biochemical assays, we showed that more than 50% of these predictions were correct in each category, yielding ligands with excellent binding efficiencies. These results highlight that docking is a suitable tool for the identification of ligands with tailored binding profiles to GPCRs, even when using homology models. More importantly, we present novel CXCR3-CXCR4 dual modulators that might pave the road to understanding the mechanisms of polypharmacological inhibition of these receptors.

Expanding the horizons of G protein-coupled receptor structure-based ligand discovery and optimization using homology models

We show the key role of structural homology models in GPCR structure-based lead discovery and optimization, highlighting methodological aspects, recent progress and future directions.

Profiling the interaction mechanism of quinoline/quinazoline derivatives as MCHR1 antagonists: an in silico method

Melanin concentrating hormone receptor 1 (MCHR1), a crucial regulator of energy homeostasis involved in the control of feeding and energy metabolism, is a promising target for treatment of obesity. In the present work, the up-to-date largest set of 181 quinoline/quinazoline derivatives as MCHR1 antagonists was subjected to both ligand- and receptor-based three-dimensional quantitative structure–activity (3D-QSAR) analysis applying comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The optimal predictable CoMSIA model exhibited significant validity with the cross-validated correlation coefficient (Q²) = 0.509, non-cross-validated correlation coefficient (R²_ncv) = 0.841 and the predicted correlation coefficient (R²_pred) = 0.745. In addition, docking studies and molecular dynamics (MD) simulations were carried out for further elucidation of the binding modes of MCHR1 antagonists. MD simulations in both water and lipid bilayer systems were performed. We hope that the obtained models and information may help to provide an insight into the interaction mechanism of MCHR1 antagonists and facilitate the design and optimization of novel antagonists as anti-obesity agents.

Structure and putative signaling mechanism of Protease activated receptor 2 (PAR2) - a promising target for breast cancer

Experimental evidences have observed enhanced expression of protease activated receptor 2 (PAR2) in breast cancer consistently. However, it is not yet recognized as an important therapeutic target for breast cancer as the primary molecular mechanisms of its activation are not yet well-defined. Nevertheless, recent reports on the mechanism of GPCR activation and signaling have given new insights to GPCR functioning. In the light of these details, we attempted to understand PAR2 structure & function using molecular modeling techniques. In this work, we generated averaged representative stable models of PAR2, using protease activated receptor 1 (PAR1) as a template and selected conformation based on their binding affinity with PAR2 specific agonist, GB110. Further, the selected model was used for studying the binding affinity of putative ligands. The selected ligands were based on a recent publication on phylogenetic analysis of Class A rhodopsin family of GPCRs. This study reports putative ligands, their interacting residues, binding affinity and molecular dynamics simulation studies on PAR2-ligand complexes. The results reported from this study would be useful for researchers and academicians to investigate PAR2 function as its physiological role is still hypothetical. Further, this information may provide a novel therapeutic scheme to manage breast cancer.

Strategies for improved modeling of GPCR-drug complexes: blind predictions of serotonin receptors bound to ergotamine

The recent increase in the number of atomic-resolution structures of G protein-coupled receptors (GPCRs) has contributed to a deeper understanding of ligand binding to several important drug targets. However, reliable modeling of GPCR-ligand complexes for the vast majority of receptors with unknown structure remains to be one of the most challenging goals for computer-aided drug design. The GPCR Dock 2013 assessment, in which researchers were challenged to predict the crystallographic structures of serotonin 5-HT(1B) and 5-HT(2B) receptors bound to ergotamine, provided an excellent opportunity to benchmark the current state of this field. Our contributions to GPCR Dock 2013 accurately predicted the binding mode of ergotamine with RMSDs below 1.8 Å for both receptors, which included the best submissions for the 5-HT(1B) complex. Our models also had the most accurate description of the binding sites and receptor-ligand contacts. These results were obtained using a ligand-guided homology modeling approach, which combines extensive molecular docking screening with incorporation of information from multiple crystal structures and experimentally derived restraints. In this work, we retrospectively analyzed thousands of structures that were generated during the assessment to evaluate our modeling strategies. Major contributors to accuracy were found to be improved modeling of extracellular loop two in combination with the use of molecular docking to optimize the binding site for ligand recognition. Our results suggest that modeling of GPCR-drug complexes has reached a level of accuracy at which structure-based drug design could be applied to a large number of pharmaceutically relevant targets.

Structure-based discovery of selective serotonin 5-HT(1B) receptor ligands

The development of safe and effective drugs relies on the discovery of selective ligands. Serotonin (5-hydroxytryptamine [5-HT]) G protein-coupled receptors are therapeutic targets for CNS disorders but are also associated with adverse drug effects. The determination of crystal structures for the 5-HT1B and 5-HT2B receptors provided an opportunity to identify subtype selective ligands using structure-based methods. From docking screens of 1.3 million compounds, 22 molecules were predicted to be selective for the 5-HT1B receptor over the 5-HT2B subtype, a requirement for safe serotonergic drugs. Nine compounds were experimentally verified as 5-HT1B-selective ligands, with up to 300-fold higher affinities for this subtype. Three of the ligands were agonists of the G protein pathway. Analysis of state-of-the-art homology models of the two 5-HT receptors revealed that the crystal structures were critical for predicting selective ligands. Our results demonstrate that structure-based screening can guide the discovery of ligands with specific selectivity profiles.

Modelling three-dimensional protein structures for applications in drug design

A structural perspective of drug target and anti-target proteins, and their molecular interactions with biologically active molecules, largely advances many areas of drug discovery, including target validation, hit and lead finding and lead optimisation. In the absence of experimental 3D structures, protein structure prediction often offers a suitable alternative to facilitate structure-based studies. This review outlines recent methodical advances in homology modelling, with a focus on those techniques that necessitate consideration of ligand binding. In this context, model quality estimation deserves special attention because the accuracy and reliability of different structure prediction techniques vary considerably, and the quality of a model ultimately determines its usefulness for structure-based drug discovery. Examples of G-protein-coupled receptors (GPCRs) and ADMET-related proteins were selected to illustrate recent progress and current limitations of protein structure prediction. Basic guidelines for good modelling practice are also provided.

Molecular modeling and ligand docking for solute carrier (SLC) transporters

Solute Carrier (SLC) transporters are membrane proteins that transport solutes, such as ions, metabolites, peptides, and drugs, across biological membranes, using diverse energy coupling mechanisms. In human, there are 386 SLC transporters, many of which contribute to the absorption, distribution, metabolism, and excretion of drugs and/or can be targeted directly by therapeutics. Recent atomic structures of SLC transporters determined by X-ray crystallography and NMR spectroscopy have significantly expanded the applicability of structure-based prediction of SLC transporter ligands, by enabling both comparative modeling of additional SLC transporters and virtual screening of small molecules libraries against experimental structures as well as comparative models. In this review, we begin by describing computational tools, including sequence analysis, comparative modeling, and virtual screening, that are used to predict the structures and functions of membrane proteins such as SLC transporters. We then illustrate the applications of these tools to predicting ligand specificities of select SLC transporters, followed by experimental validation using uptake kinetic measurements and other assays. We conclude by discussing future directions in the discovery of the SLC transporter ligands.