Ligand supported homology modeling and docking evaluation of CCR2: docked pose selection by consensus scoring

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.

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.

Maximal Unbiased Benchmarking Data Sets for Human Chemokine Receptors and Comparative Analysis

Chemokine receptors (CRs) have long been druggable targets for the treatment of inflammatory diseases and HIV-1 infection. As a powerful technique, virtual screening (VS) has been widely applied to identifying small molecule leads for modern drug targets including CRs. For rational selection of a wide variety of VS approaches, ligand enrichment assessment based on a benchmarking data set has become an indispensable practice. However, the lack of versatile benchmarking sets for the whole CRs family that are able to unbiasedly evaluate every single approach including both structure- and ligand-based VS somewhat hinders modern drug discovery efforts. To address this issue, we constructed Maximal Unbiased Benchmarking Data sets for human Chemokine Receptors (MUBD-hCRs) using our recently developed tools of MUBD-DecoyMaker. The MUBD-hCRs encompasses 13 subtypes out of 20 chemokine receptors, composed of 404 ligands and 15756 decoys so far and is readily expandable in the future. It had been thoroughly validated that MUBD-hCRs ligands are chemically diverse while its decoys are maximal unbiased in terms of "artificial enrichment", "analogue bias". In addition, we studied the performance of MUBD-hCRs, in particular CXCR4 and CCR5 data sets, in ligand enrichment assessments of both structure- and ligand-based VS approaches in comparison with other benchmarking data sets available in the public domain and demonstrated that MUBD-hCRs is very capable of designating the optimal VS approach. MUBD-hCRs is a unique and maximal unbiased benchmarking set that covers major CRs subtypes so far.

A Natural CCR2 Antagonist Relieves Tumor-associated Macrophage-mediated Immunosuppression to Produce a Therapeutic Effect for Liver Cancer

Hepatocellular carcinoma (HCC) is a common malignant tumor in the digestive tract with limited therapeutic choices. Although sorafenib, an orally administered multikinase inhibitor, has produced survival benefits for patients with advanced HCC, favorable clinical outcomes are limited due to individual differences and resistance. The application of immunotherapy, a promising approach for HCC is urgently needed. Macrophage infiltration, mediated by the CCL2/CCR2 axis, is a potential immunotherapeutic target. Here, we report that a natural product from Abies georgei, named 747 and related in structure to kaempferol, exhibits sensitivity and selectivity as a CCR2 antagonist. The specificity of 747 on CCR2 was demonstrated via calcium flux, the binding domain of CCR2 was identified in an extracellular loop by chimera binding assay, and in vivo antagonistic activity of 747 was confirmed through a thioglycollate-induced peritonitis model. In animals, 747 elevated the number of CD8 + T cells in tumors via blocking tumor-infiltrating macrophage-mediated immunosuppression and inhibited orthotopic and subcutaneous tumor growth in a CD8 + T cell-dependent manner. Further, 747 enhanced the therapeutic efficacy of low-dose sorafenib without obvious toxicity, through elevating the numbers of intra-tumoral CD8 + T cells and increasing death of tumor cells. Thus, we have discovered a natural CCR2 antagonist and have provided a new perspective on development of this antagonist for treatment of HCC. In mouse models of HCC, 747 enhanced the tumor immunosuppressive microenvironment and potentiated the therapeutic effect of sorafenib, indicating that the combination of an immunomodulator with a chemotherapeutic drug could be a new approach for treating HCC.

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We identified a natural product, 747, as an antagonist of CCR2.

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747 exhibited anticancer properties and potentiated the efficacy of sorafenib in mouse models of HCC.

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A combination of immunotherapy and chemotherapy could be a new approach for treating HCC.

Hepatocellular carcinoma (HCC) is a common malignant tumor that arises in people with a chronic liver disorder and inflammation. Macrophage infiltration, controlled by the CCL2/CCR2 axis, is evident in various liver diseases, including hepatitis, cirrhosis, and tumor progression, making it a therapeutic immune target for liver cancer. In the present investigation, we identified a CCR2 antagonist, 747, from Abies georgei. In a mouse model of HCC, 747 alone exhibited anticancer properties and potentiated the antitumor efficacy of a low dose of sorafenib. Our findings indicate that a combination of immunotherapy and chemotherapy could be a new approach for treating HCC.

Potential Broad Spectrum Inhibitors of the Coronavirus 3CLpro: A Virtual Screening and Structure-Based Drug Design Study

Human coronaviruses represent a significant disease burden; however, there is currently no antiviral strategy to combat infection. The outbreak of severe acute respiratory syndrome (SARS) in 2003 and Middle East respiratory syndrome (MERS) less than 10 years later demonstrates the potential of coronaviruses to cross species boundaries and further highlights the importance of identifying novel lead compounds with broad spectrum activity. The coronavirus 3CL(pro) provides a highly validated drug target and as there is a high degree of sequence homology and conservation in main chain architecture the design of broad spectrum inhibitors is viable. The ZINC drugs-now library was screened in a consensus high-throughput pharmacophore modeling and molecular docking approach by Vina, Glide, GOLD and MM-GBSA. Molecular dynamics further confirmed results obtained from structure-based techniques. A highly defined hit-list of 19 compounds was identified by the structure-based drug design methodologies. As these compounds were extensively validated by a consensus approach and by molecular dynamics, the likelihood that at least one of these compounds is bioactive is excellent. Additionally, the compounds segregate into 15 significantly dissimilar (p < 0.05) clusters based on shape and features, which represent valuable scaffolds that can be used as a basis for future anti-coronaviral inhibitor discovery experiments. Importantly though, the enriched subset of 19 compounds identified from the larger library has to be validated experimentally.

Structure prediction and molecular dynamics simulations of a G-protein coupled receptor: human CCR2 receptor

CC chemokine receptor type-2 (CCR2) is a member of G-protein coupled receptors superfamily, expressed on the cell surface of monocytes and macrophages. It binds to the monocyte chemoattractant protein-1, a CC chemokine, produced at the sites of inflammation and infection. A homology model of human CCR2 receptor based on the recently available C-X-C chemokine recepor-4 crystal structure has been reported. Ligand information was used as an essential element in the homology modeling process. Six known CCR2 antagonists were docked into the model using simple and induced fit docking procedure. Docked complexes were then subjected to visual inspection to check their suitability to explain the experimental data obtained from site directed mutagenesis and structure-activity relationship studies. The homology model was refined, validated, and assessed for its performance in docking-based virtual screening on a set of CCR2 antagonists and decoys. The docked complexes of CCR2 with the known antagonists, TAK779, a dual CCR2/CCR5 antagonist, and Teijin-comp1, a CCR2 specific antagonist were subjected to molecular dynamics (MD) simulations, which further validated the binding modes of these antagonists. B-factor analysis of 20 ns MD simulations demonstrated that Cys190 is helpful in providing structural rigidity to the extracellular loop (EL2). Residues important for CCR2 antagonism were recognized using free energy decomposition studies. The acidic residue Glu291 from TM7, a conserved residue in chemokine receptors, is favorable for the binding of Teijin-comp1 with CCR2 by ΔG of -11.4 kcal/mol. Its contribution arises more from the side chains than the backbone atoms. In addition, Tyr193 from EL2 contributes -0.9 kcal/mol towards the binding of the CCR2 specific antagonist with the receptor. Here, the homology modeling and subsequent molecular modeling studies proved successful in probing the structure of human CCR2 chemokine receptor for the structure-based virtual screening and predicting the binding modes of CCR2 antagonists.

Pharmacological modulation of chemokine receptor function

G protein-coupled chemokine receptors and their peptidergic ligands are interesting therapeutic targets due to their involvement in various immune-related diseases, including rheumatoid arthritis, multiple sclerosis, inflammatory bowel disease, chronic obstructive pulmonary disease, HIV-1 infection and cancer. To tackle these diseases, a lot of effort has been focused on discovery and development of small-molecule chemokine receptor antagonists. This has been rewarded by the market approval of two novel chemokine receptor inhibitors, AMD3100 (CXCR4) and Maraviroc (CCR5) for stem cell mobilization and treatment of HIV-1 infection respectively. The recent GPCR crystal structures together with mutagenesis and pharmacological studies have aided in understanding how small-molecule ligands interact with chemokine receptors. Many of these ligands display behaviour deviating from simple competition and do not interact with the chemokine binding site, providing evidence for an allosteric mode of action. This review aims to give an overview of the evidence supporting modulation of this intriguing receptor family by a range of ligands, including small molecules, peptides and antibodies. Moreover, the computer-assisted modelling of chemokine receptor-ligand interactions is discussed in view of GPCR crystal structures. Finally, the implications of concepts such as functional selectivity and chemokine receptor dimerization are considered.

Structural insights from binding poses of CCR2 and CCR5 with clinically important antagonists: a combined in silico study

Chemokine receptors are G protein-coupled receptors that contain seven transmembrane domains. In particular, CCR2 and CCR5 and their ligands have been implicated in the pathophysiology of a number of diseases, including rheumatoid arthritis and multiple sclerosis. Based on their roles in disease, they have been attractive targets for the pharmaceutical industry, and furthermore, targeting both CCR2 and CCR5 can be a useful strategy. Owing to the importance of these receptors, information regarding the binding site is of prime importance. Structural studies have been hampered due to the lack of X-ray crystal structures, and templates with close homologs for comparative modeling. Most of the previous models were based on the bovine rhodopsin and β2-adrenergic receptor. In this study, based on a closer homolog with higher resolution (CXCR4, PDB code: 3ODU 2.5 Å), we constructed three-dimensional models. The main aim of this study was to provide relevant information on binding sites of these receptors. Molecular dynamics simulation was done to refine the homology models and PROCHECK results indicated that the models were reasonable. Here, binding poses were checked with some established inhibitors of high pharmaceutical importance against the modeled receptors. Analysis of interaction modes gave an integrated interpretation with detailed structural information. The binding poses confirmed that the acidic residues Glu291 (CCR2) and Glu283 (CCR5) are important, and we also found some additional residues. Comparisons of binding sites of CCR2/CCR5 were done sequentially and also by docking a potent dual antagonist. Our results can be a starting point for further structure-based drug design.

Prediction of ligand binding using an approach designed to accommodate diversity in protein-ligand interactions

Computational determination of protein-ligand interaction potential is important for many biological applications including virtual screening for therapeutic drugs. The novel internal consensus scoring strategy is an empirical approach with an extended set of 9 binding terms combined with a neural network capable of analysis of diverse complexes. Like conventional consensus methods, internal consensus is capable of maintaining multiple distinct representations of protein-ligand interactions. In a typical use the method was trained using ligand classification data (binding/no binding) for a single receptor. The internal consensus analyses successfully distinguished protein-ligand complexes from decoys (r², 0.895 for a series of typical proteins). Results are superior to other tested empirical methods. In virtual screening experiments, internal consensus analyses provide consistent enrichment as determined by ROC-AUC and pROC metrics.