Structure-based ligand discovery for the protein-protein interface of chemokine receptor CXCR4

C-X-C motif chemokine ligand 12-C-X-C chemokine receptor type 4 signaling axis in cancer and the development of chemotherapeutic molecules

Chemokines are small, secreted cytokines crucial in the regulation of a variety of cell functions. The binding of chemokine C-X-C motif chemokine ligand 12 (CXCL12) (stromal cell-derived factor 1) to a G-protein-coupled receptor C-X-C chemokine receptor type 4 (CXCR4) triggers downstream signaling pathways with effects on cell survival, proliferation, chemotaxis, migration, and gene expression. Intensive and extensive investigations have provided evidence suggesting that the CXCL12-CXCR4 axis plays a pivotal role in tumor development, survival, angiogenesis, metastasis, as well as in creating tumor microenvironment, thus implying that this axis is a potential target for the development of cancer therapies. The structures of CXCL12 and CXCR4 have been resolved with experimental methods such as X-ray crystallography, NMR, or cryo-EM. Therefore, it is possible to apply structure-based computational approaches to discover, design, and modify therapeutic molecules for cancer treatments. Here, we summarize the current understanding of the roles played by the CXCL12-CXCR4 signaling axis in cellular functions linking to cancer progression and metastasis. This review also provides an introduction to protein structures of CXCL12 and CXCR4 and the application of computer simulation and analysis in understanding CXCR4 activation and antagonist binding. Furthermore, examples of strategies and current progress in CXCL12-CXCR4 axis-targeted development of therapeutic anticancer inhibitors are discussed.

Large library docking for cannabinoid-1 receptor agonists with reduced side effects

Large library docking can reveal unexpected chemotypes that complement the structures of biological targets. Seeking new agonists for the cannabinoid-1 receptor (CB1R), we docked 74 million tangible molecules, prioritizing 46 high ranking ones for de novo synthesis and testing. Nine were active by radioligand competition, a 20% hit-rate. Structure-based optimization of one of the most potent of these (Ki = 0.7 uM) led to '4042, a 1.9 nM ligand and a full CB1R agonist. A cryo-EM structure of the purified enantiomer of '4042 ('1350) in complex with CB1R-Gi1 confirmed its docked pose. The new agonist was strongly analgesic, with generally a 5-10-fold therapeutic window over sedation and catalepsy and no observable conditioned place preference. These findings suggest that new cannabinoid chemotypes may disentangle characteristic cannabinoid side-effects from their analgesia, supporting the further development of cannabinoids as pain therapeutics.

The pathological role of C-X-C chemokine receptor type 4 (CXCR4) in colorectal cancer (CRC) progression; special focus on molecular mechanisms and possible therapeutics

Colorectal cancer (CRC) is comprised of transformed cells and non-malignant cells including cancer-associated fibroblasts (CAF), endothelial vasculature cells, and tumor-infiltrating cells. These nonmalignant cells, as well as soluble factors (e.g., cytokines), and the extracellular matrix (ECM), form the tumor microenvironment (TME). In general, the cancer cells and their surrounding TME can crosstalk by direct cell-to-cell contact and via soluble factors, such as cytokines (e.g., chemokines). TME not only promotes cancer progression through growth-promoting cytokines but also provides resistance to chemotherapy. Understanding the mechanisms of tumor growth and progression and the roles of chemokines in CRC will likely suggest new therapeutic targets. In this line, a plethora of reports has evidenced the critical role of chemokine receptor type 4 (CXCR4)/C-X-C motif chemokine ligand 12 (CXCL12 or SDF-1) axis in CRC pathogenesis. In the current review, we take a glimpse into the role of the CXCR4/CXCL12 axis in CRC growth, metastasis, angiogenesis, drug resistance, and immune escape. Also, a summary of recent reports concerning targeting CXCR4/CXCL12 axis for CRC management and therapy has been delivered.

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.

Helix encoder: a compound-protein interaction prediction model specifically designed for class A GPCRs

Class A G protein-coupled receptors (GPCRs) represent the largest class of GPCRs. They are essential targets of drug discovery and thus various computational approaches have been applied to predict their ligands. However, there are a large number of orphan receptors in class A GPCRs and it is difficult to use a general protein-specific supervised prediction scheme. Therefore, the compound-protein interaction (CPI) prediction approach has been considered one of the most suitable for class A GPCRs. However, the accuracy of CPI prediction is still insufficient. The current CPI prediction model generally employs the whole protein sequence as the input because it is difficult to identify the important regions in general proteins. In contrast, it is well-known that only a few transmembrane helices of class A GPCRs play a critical role in ligand binding. Therefore, using such domain knowledge, the CPI prediction performance could be improved by developing an encoding method that is specifically designed for this family. In this study, we developed a protein sequence encoder called the Helix encoder, which takes only a protein sequence of transmembrane regions of class A GPCRs as input. The performance evaluation showed that the proposed model achieved a higher prediction accuracy compared to a prediction model using the entire protein sequence. Additionally, our analysis indicated that several extracellular loops are also important for the prediction as mentioned in several biological researches.

Structure-based discovery of conformationally selective inhibitors of the serotonin transporter

The serotonin transporter (SERT) removes synaptic serotonin and is the target of anti-depressant drugs. SERT adopts three conformations: outward-open, occluded, and inward-open. All known inhibitors target the outward-open state except ibogaine, which has unusual anti-depressant and substance-withdrawal effects, and stabilizes the inward-open conformation. Unfortunately, ibogaine's promiscuity and cardiotoxicity limit the understanding of inward-open state ligands. We docked over 200 million small molecules against the inward-open state of the SERT. Thirty-six top-ranking compounds were synthesized, and thirteen inhibited; further structure-based optimization led to the selection of two potent (low nanomolar) inhibitors. These stabilized an outward-closed state of the SERT with little activity against common off-targets. A cryo-EM structure of one of these bound to the SERT confirmed the predicted geometry. In mouse behavioral assays, both compounds had anxiolytic- and anti-depressant-like activity, with potencies up to 200-fold better than fluoxetine (Prozac), and one substantially reversed morphine withdrawal effects.

Systematic Investigation of Docking Failures in Large-Scale Structure-Based Virtual Screening

In recent years, large-scale structure-based virtual screening has attracted increasing levels of interest for identification of novel compounds corresponding to potential drug targets. It is critical to understand the strengths and weaknesses of docking algorithms to increase the success rate in practical applications. Here, we systematically investigated the docking successes and failures of two representative docking programs: UCSF DOCK 3.7 and AutoDock Vina. DOCK 3.7 performed better in early enrichment on the Directory of Useful Decoys: Enhanced (DUD-E) data set, although both docking methods were roughly comparable in overall enrichment performance. DOCK 3.7 also showed superior computational efficiency. Intriguingly, the Vina scoring function showed a bias toward compounds with higher molecular weights. Both the tested docking approaches yielded incorrectly predicted ligand binding poses caused by the limitations of torsion sampling. Based on a careful analysis of docking results from six representative cases, we propose the reasons underlying docking failures; furthermore, we provide a few solutions, representing practical guidance for large-scale virtual screening campaigns and future docking algorithm development.

Structure-based discovery of nonopioid analgesics acting through the α2A-adrenergic receptor

Because nonopioid analgesics are much sought after, we computationally docked more than 301 million virtual molecules against a validated pain target, the α2A-adrenergic receptor (α2AAR), seeking new α2AAR agonists chemotypes that lack the sedation conferred by known α2AAR drugs, such as dexmedetomidine. We identified 17 ligands with potencies as low as 12 nanomolar, many with partial agonism and preferential Gi and Go signaling. Experimental structures of α2AAR complexed with two of these agonists confirmed the docking predictions and templated further optimization. Several compounds, including the initial docking hit '9087 [mean effective concentration (EC50) of 52 nanomolar] and two analogs, '7075 and PS75 (EC50 4.1 and 4.8 nanomolar), exerted on-target analgesic activity in multiple in vivo pain models without sedation. These newly discovered agonists are interesting as therapeutic leads that lack the liabilities of opioids and the sedation of dexmedetomidine.

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.

Cell-Based Therapies for Trabecular Meshwork Regeneration to Treat Glaucoma

Glaucoma is clinically characterized by elevated intraocular pressure (IOP) that leads to retinal ganglion cell (RGC) and optic nerve damage, and eventually blindness if left untreated. Even in normal pressure glaucoma patients, a reduction of IOP is currently the only effective way to prevent blindness, by either increasing aqueous humor outflow or decreasing aqueous humor production. The trabecular meshwork (TM) and the adjacent Schlemm’s canal inner wall play a key role in regulating IOP by providing resistance when aqueous humor drains through the tissue. TM dysfunction seen in glaucoma, through reduced cellularity, abnormal extracellular matrix accumulation, and increased stiffness, contributes to elevated IOP, but current therapies do not target the TM tissue. Stem cell transplantation for regeneration and re-functionalization of damaged TM has shown promise in providing a more direct and effective therapy for glaucoma. In this review, we describe the use of different types of stem cells for TM regeneration in glaucoma models, the mechanisms of regeneration, and the potential for glaucoma treatment using autologous stem cell transplantation.

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.

Fragment binding to the Nsp3 macrodomain of SARS-CoV-2 identified through crystallographic screening and computational docking

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) macrodomain within the nonstructural protein 3 counteracts host-mediated antiviral adenosine diphosphate-ribosylation signaling. This enzyme is a promising antiviral target because catalytic mutations render viruses nonpathogenic. Here, we report a massive crystallographic screening and computational docking effort, identifying new chemical matter primarily targeting the active site of the macrodomain. Crystallographic screening of 2533 diverse fragments resulted in 214 unique macrodomain-binders. An additional 60 molecules were selected from docking more than 20 million fragments, of which 20 were crystallographically confirmed. X-ray data collection to ultra-high resolution and at physiological temperature enabled assessment of the conformational heterogeneity around the active site. Several fragment hits were confirmed by solution binding using three biophysical techniques (differential scanning fluorimetry, homogeneous time-resolved fluorescence, and isothermal titration calorimetry). The 234 fragment structures explore a wide range of chemotypes and provide starting points for development of potent SARS-CoV-2 macrodomain inhibitors.

C@PA: Computer-Aided Pattern Analysis to Predict Multitarget ABC Transporter Inhibitors

Based on literature reports of the last two decades, a computer-aided pattern analysis (C@PA) was implemented for the discovery of novel multitarget ABCB1 (P-gp), ABCC1 (MRP1), and ABCG2 (BCRP) inhibitors. C@PA included basic scaffold identification, substructure search and statistical distribution, as well as novel scaffold extraction to screen a large virtual compound library. Over 45,000 putative and novel broad-spectrum ABC transporter inhibitors were identified, from which 23 were purchased for biological evaluation. Our investigations revealed five novel lead molecules as triple ABCB1, ABCC1, and ABCG2 inhibitors. C@PA is the very first successful computational approach for the discovery of promiscuous ABC transporter inhibitors.

Fragment Binding to the Nsp3 Macrodomain of SARS-CoV-2 Identified Through Crystallographic Screening and Computational Docking

The SARS-CoV-2 macrodomain (Mac1) within the non-structural protein 3 (Nsp3) counteracts host-mediated antiviral ADP-ribosylation signalling. This enzyme is a promising antiviral target because catalytic mutations render viruses non-pathogenic. Here, we report a massive crystallographic screening and computational docking effort, identifying new chemical matter primarily targeting the active site of the macrodomain. Crystallographic screening of diverse fragment libraries resulted in 214 unique macrodomain-binding fragments, out of 2,683 screened. An additional 60 molecules were selected from docking over 20 million fragments, of which 20 were crystallographically confirmed. X-ray data collection to ultra-high resolution and at physiological temperature enabled assessment of the conformational heterogeneity around the active site. Several crystallographic and docking fragment hits were validated for solution binding using three biophysical techniques (DSF, HTRF, ITC). Overall, the 234 fragment structures presented explore a wide range of chemotypes and provide starting points for development of potent SARS-CoV-2 macrodomain inhibitors.

Discovery of novel aminopiperidinyl amide CXCR4 modulators through virtual screening and rational drug design

The C-X-C chemokine receptor type 4 (CXCR4) is a potential therapeutic target for HIV infection, metastatic cancer, and inflammatory autoimmune diseases. In this study, we screened the ZINC chemical database for novel CXCR4 modulators through a series of in silico guided processes. After evaluating the screened compounds for their binding affinities to CXCR4 and inhibitory activities against the chemoattractant CXCL12, we identified a hit compound (ZINC 72372983) showing 100 nM affinity and 69% chemotaxis inhibition at the same concentration (100 nM). To increase the potency of our hit compound, we explored the protein-ligand interactions at an atomic level using molecular dynamics simulation which enabled us to design and synthesize a novel compound (Z7R) with nanomolar affinity (IC₅₀ = 1.25 nM) and improved chemotaxis inhibition (78.5%). Z7R displays promising anti-inflammatory activity (50%) in a mouse edema model by blocking CXCR4-expressed leukocytes, being supported by our immunohistochemistry study.

Adipose-derived stem cells integrate into trabecular meshwork with glaucoma treatment potential

The trabecular meshwork (TM) is an ocular tissue that maintains intraocular pressure (IOP) within a physiologic range. Glaucoma patients have reduced TM cellularity and, frequently, elevated IOP. To establish a stem cell-based approach to restoring TM function and normalizing IOP, human adipose-derived stem cells (ADSCs) were induced to differentiate to TM cells in vitro. These ADSC-TM cells displayed a TM cell-like genotypic profile, became phagocytic, and responded to dexamethasone stimulation, characteristic of TM cells. After transplantation into naive mouse eyes, ADSCs and ADSC-TM cells integrated into the TM tissue, expressed TM cell markers, and maintained normal IOP, outflow facility, and extracellular matrix. Cell migration and affinity results indicated that the chemokine pair CXCR4/SDF1 may play an important role in ADSC-TM cell homing. Our study demonstrates the possibility of applying autologous or allogeneic ADSCs and ADSC-TM cells as a potential treatment to restore TM structure and function in glaucoma.

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.

Identification of Synthetic Activators of Cancer Cell Migration by Hybrid Deep Learning

Deep convolutional neural networks (CNNs) are a method of choice for image recognition. Herein a hybrid CNN approach is presented for molecular pattern recognition in drug discovery. Using self-organizing map images of molecular pharmacophores as input, CNN models were trained to identify chemokine receptor CXCR4 modulators with high accuracy. This machine learning classifier identified first-in-class synthetic CXCR4 full agonists. The receptor-activating effects were confirmed by intracellular cAMP response and in a phenotypic spheroid invasion assay of medulloblastoma cell invasion. Additional macromolecular targets of the small molecules were predicted in silico and tested in vitro, revealing modulatory effects on dopamine receptors and CCR1. These results positively advocate the applicability of molecular image recognition by CNNs to ligand-based virtual compound screening, and demonstrate the complementarity of machine intelligence and human expert knowledge.

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.

Synthetic Activators of Cell Migration Designed by Constructive Machine Learning

Constructive machine learning aims to create examples from its learned domain which are likely to exhibit similar properties. Here, a recurrent neural network was trained with the chemical structures of known cell-migration modulators. This machine learning model was used to generate new molecules that mimic the training compounds. Two top-scoring designs were synthesized, and tested for functional activity in a phenotypic spheroid cell migration assay. These computationally generated small molecules significantly increased the migration of medulloblastoma cells. The results further corroborate the applicability of constructive machine learning to the de novo design of druglike molecules with desired properties.

Chemokine Receptor Crystal Structures: What Can Be Learned from Them?

Chemokine receptors belong to the class A of G protein-coupled receptors (GPCRs) and are implicated in a wide variety of physiologic functions, mostly related to the homeostasis of the immune system. Chemokine receptors are also involved in multiple pathologic processes, including immune and autoimmune diseases, as well as cancer. Hence, several members of this GPCR subfamily are considered to be very relevant therapeutic targets. Since drug discovery efforts can be significantly reinforced by the availability of crystal structures, substantial efforts in the area of chemokine receptor structural biology could dramatically increase the outcome of drug discovery campaigns. This short review summarizes the available data on chemokine receptor crystal structures, discusses the numerous applications from chemokine receptor structures that can enhance the daily work of molecular pharmacologists, and describes the challenges and pitfalls to consider when relying on crystal structures for further research applications. SIGNIFICANCE STATEMENT: This short review summarizes the available data on chemokine receptor crystal structures, discusses the numerous applications from chemokine receptor structures that can enhance the daily work of molecular pharmacologists, and describes the challenges and pitfalls to consider when relying on crystal structures for further research applications.

Machine Learning Analysis of τRAMD Trajectories to Decipher Molecular Determinants of Drug-Target Residence Times

Drug-target residence times can impact drug efficacy and safety, and are therefore increasingly being considered during lead optimization. For this purpose, computational methods to predict residence times, τ, for drug-like compounds and to derive structure-kinetic relationships are desirable. A challenge for approaches based on molecular dynamics (MD) simulation is the fact that drug residence times are typically orders of magnitude longer than computationally feasible simulation times. Therefore, enhanced sampling methods are required. We recently reported one such approach: the τRAMD procedure for estimating relative residence times by performing a large number of random acceleration MD (RAMD) simulations in which ligand dissociation occurs in times of about a nanosecond due to the application of an additional randomly oriented force to the ligand. The length of the RAMD simulations is used to deduce τ. The RAMD simulations also provide information on ligand egress pathways and dissociation mechanisms. Here, we describe a machine learning approach to systematically analyze protein-ligand binding contacts in the RAMD trajectories in order to derive regression models for estimating τ and to decipher the molecular features leading to longer τ values. We demonstrate that the regression models built on the protein-ligand interaction fingerprints of the dissociation trajectories result in robust estimates of τ for a set of 94 drug-like inhibitors of heat shock protein 90 (HSP90), even for the compounds for which the length of the RAMD trajectories does not provide a good estimation of τ. Thus, we find that machine learning helps to overcome inaccuracies in the modeling of protein-ligand complexes due to incomplete sampling or force field deficiencies. Moreover, the approach facilitates the identification of features important for residence time. In particular, we observed that interactions of the ligand with the sidechain of F138, which is located on the border between the ATP binding pocket and a hydrophobic transient sub-pocket, play a key role in slowing compound dissociation. We expect that the combination of the τRAMD simulation procedure with machine learning analysis will be generally applicable as an aid to target-based lead optimization.

Computational advances in combating colloidal aggregation in drug discovery

Small molecule effectors are essential for drug discovery. Specific molecular recognition, reversible binding and dose-dependency are usually key requirements to ensure utility of a novel chemical entity. However, artefactual frequent-hitter and assay interference compounds may divert lead optimization and screening programmes towards attrition-prone chemical matter. Colloidal aggregates are the prime source of false positive readouts, either through protein sequestration or protein-scaffold mimicry. Nevertheless, assessment of colloidal aggregation remains somewhat overlooked and under-appreciated. In this Review, we discuss the impact of aggregation in drug discovery by analysing select examples from the literature and publicly-available datasets. We also examine and comment on technologies used to experimentally identify these potentially problematic entities. We focus on evidence-based computational filters and machine learning algorithms that may be swiftly deployed to flag chemical matter and mitigate the impact of aggregates in discovery programmes. We highlight the tools that can be used to scrutinize libraries, and identify and eliminate these problematic compounds.

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.

Human stem cells home to and repair laser-damaged trabecular meshwork in a mouse model

Glaucoma is the leading cause of irreversible vision loss, and reducing elevated intraocular pressure is currently the only effective clinical treatment. The trabecular meshwork is the main resistance site for aqueous outflow that maintains intraocular pressure. In this study, we transplanted human trabecular meshwork stem cells (TMSCs) intracamerally into mice that received laser photocoagulation over a 180° arc of the trabecular meshwork. TMSCs preferentially homed and integrated to the laser-damaged trabecular meshwork region and expressed differentiated cell markers at 2 and 4 weeks. Laser-induced inflammatory and fibrotic responses were prevented by TMSC transplantation with simultaneous ultrastructure and function restoration. Cell affinity and migration assays and elevated expression of CXCR4 and SDF1 in laser-treated mouse trabecular meshwork suggest that the CXCR4/SDF1 chemokine axis plays an important role in TMSC homing. Our results suggest that TMSCs may be a viable candidate for trabecular meshwork refunctionalization as a novel treatment for glaucoma.

Hongmin Yun et al. show that implanted human stem cells can accurately home to and repair damaged trabecular meshwork tissue in the mouse eye via a chemokine axis defined by CXCR4 and SDF1. The study suggests that stem cells from the trabecular meshwork could be used to refunctionalize the outflow pathway as a treatment for glaucoma.

Structure-based exploration and pharmacological evaluation of N-substituted piperidin-4-yl-methanamine CXCR4 chemokine receptor antagonists

Using the available structural information of the chemokine receptor CXCR4, we present hit finding and hit exploration studies that make use of virtual fragment screening, design, synthesis and structure-activity relationship (SAR) studies. Fragment 2 was identified as virtual screening hit and used as a starting point for the exploration of 31 N-substituted piperidin-4-yl-methanamine derivatives to investigate and improve the interactions with the CXCR4 binding site. Additionally, subtle structural ligand changes lead to distinct interactions with CXCR4 resulting in a full to partial displacement of CXCL12 binding and competitive and/or non-competitive antagonism. Three-dimensional quantitative structure-activity relationship (3D-QSAR) and binding model studies were used to identify important hydrophobic interactions that determine binding affinity and indicate key ligand-receptor interactions.

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.

Celastrol binds to its target protein via specific noncovalent interactions and reversible covalent bonds

Celastrol binding to its target protein Nur77 requires specific noncovalent interactions that position celastrol close to a specific cysteine and furthermore confer its binding specificity.

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.

Testing inhomogeneous solvation theory in structure-based ligand discovery

Binding-site water is often displaced upon ligand recognition, but is commonly neglected in structure-based ligand discovery. Inhomogeneous solvation theory (IST) has become popular for treating this effect, but it has not been tested in controlled experiments at atomic resolution. To do so, we turned to a grid-based version of this method, GIST, readily implemented in molecular docking. Whereas the term only improves docking modestly in retrospective ligand enrichment, it could be added without disrupting performance. We thus turned to prospective docking of large libraries to investigate GIST's impact on ligand discovery, geometry, and water structure in a model cavity site well-suited to exploring these terms. Although top-ranked docked molecules with and without the GIST term often overlapped, many ligands were meaningfully prioritized or deprioritized; some of these were selected for testing. Experimentally, 13/14 molecules prioritized by GIST did bind, whereas none of the molecules that it deprioritized were observed to bind. Nine crystal complexes were determined. In six, the ligand geometry corresponded to that predicted by GIST, for one of these the pose without the GIST term was wrong, and three crystallographic poses differed from both predictions. Notably, in one structure, an ordered water molecule with a high GIST displacement penalty was observed to stay in place. Inclusion of this water-displacement term can substantially improve the hit rates and ligand geometries from docking screens, although the magnitude of its effects can be small and its impact in drug binding sites merits further controlled studies.

Structure and Function of Peptide-Binding G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) are the largest family of cell surface receptors and are important human drug targets. Of the 826 human GPCRs, 118 of them recognize endogenous peptide or protein ligands, and 30 of the 118 are targeted by approved drug molecules, including the very high-profile class B glucagon-like peptide 1 receptor. In this review, we analyze the 21 experimentally determined three-dimensional structures of the known peptide-binding GPCRs in relation to the endogenous peptides and drug molecules that modulate their cell signaling processes. Our integrated analyses reveal that half of the marketed drugs and most of the drugs in clinical trials that interact with peptide GPCRs are small molecules with a wide range of binding modes distinct from those of large peptide ligands. As we continue to collect additional data on these receptors from orthogonal approaches, including nuclear magnetic resonance and electron microscopy, we are beginning to understand how these receptors interact with their ligands at the molecular level and how improving the pharmacology of GPCR signal transduction requires us to study these receptors using multiple biophysical techniques.

What Do Structures Tell Us About Chemokine Receptor Function and Antagonism?

Chemokines and their cell surface G protein-coupled receptors are critical for cell migration, not only in many fundamental biological processes but also in inflammatory diseases and cancer. Recent X-ray structures of two chemokines complexed with full-length receptors provided unprecedented insight into the atomic details of chemokine recognition and receptor activation, and computational modeling informed by new experiments leverages these insights to gain understanding of many more receptor:chemokine pairs. In parallel, chemokine receptor structures with small molecules reveal the complicated and diverse structural foundations of small molecule antagonism and allostery, highlight the inherent physicochemical challenges of receptor:chemokine interfaces, and suggest novel epitopes that can be exploited to overcome these challenges. The structures and models promote unique understanding of chemokine receptor biology, including the interpretation of two decades of experimental studies, and will undoubtedly assist future drug discovery endeavors.

Structure-Based Discovery of New Antagonist and Biased Agonist Chemotypes for the Kappa Opioid Receptor

The ongoing epidemics of opioid overdose raises an urgent need for effective antiaddiction therapies and addiction-free painkillers. The κ-opioid receptor (KOR) has emerged as a promising target for both indications, raising demand for new chemotypes of KOR antagonists as well as G-protein-biased agonists. We employed the crystal structure of the KOR-JDTic complex and ligand-optimized structural templates to perform virtual screening of available compound libraries for new KOR ligands. The prospective virtual screening campaign yielded a high 32% hit rate, identifying novel fragment-like and lead-like chemotypes of KOR ligands. A round of optimization resulted in 11 new submicromolar KOR binders (best K_i = 90 nM). Functional assessment confirmed at least two compounds as potent KOR antagonists, while compound 81 was identified as a potent G_i biased agonist for KOR with minimal β-arrestin recruitment. These results support virtual screening as an effective tool for discovery of new lead chemotypes with therapeutically relevant functional profiles.

Structural basis for chemokine recognition by a G protein-coupled receptor and implications for receptor activation

Chemokines orchestrate cell migration for development, immune surveillance, and disease by binding to cell surface heterotrimeric guanine nucleotide-binding protein (G protein)-coupled receptors (GPCRs). The array of interactions between the nearly 50 chemokines and their 20 GPCR targets generates an extensive signaling network to which promiscuity and biased agonism add further complexity. The receptor CXCR4 recognizes both monomeric and dimeric forms of the chemokine CXCL12, which is a distinct example of ligand bias in the chemokine family. We demonstrated that a constitutively monomeric CXCL12 variant reproduced the G protein-dependent and β-arrestin-dependent responses that are associated with normal CXCR4 signaling and lead to cell migration. In addition, monomeric CXCL12 made specific contacts with CXCR4 that are not present in the structure of the receptor in complex with a dimeric form of CXCL12, a biased agonist that stimulates only G protein-dependent signaling. We produced an experimentally validated model of an agonist-bound chemokine receptor that merged a nuclear magnetic resonance-based structure of monomeric CXCL12 bound to the amino terminus of CXCR4 with a crystal structure of the transmembrane domains of CXCR4. The large CXCL12:CXCR4 protein-protein interface revealed by this structure identified previously uncharacterized functional interactions that fall outside of the classical "two-site model" for chemokine-receptor recognition. Our model suggests a mechanistic hypothesis for how interactions on the extracellular face of the receptor may stimulate the conformational changes required for chemokine receptor-mediated signal transduction.

Ligand Discovery for a Peptide-Binding GPCR by Structure-Based Screening of Fragment- and Lead-Like Chemical Libraries

Peptide-recognizing G protein-coupled receptors (GPCRs) are promising therapeutic targets but often resist drug discovery efforts. Determination of crystal structures for peptide-binding GPCRs has provided opportunities to explore structure-based methods in lead development. Molecular docking screens of two chemical libraries, containing either fragment- or lead-like compounds, against a neurotensin receptor 1 crystal structure allowed for a comparison between different drug development strategies for peptide-binding GPCRs. A total of 2.3 million molecules were screened computationally, and 25 fragments and 27 leads that were top-ranked in each library were selected for experimental evaluation. Of these, eight fragments and five leads were confirmed as ligands by surface plasmon resonance. The hit rate for the fragment screen (32%) was thus higher than for the lead-like library (19%), but the affinities of the fragments were ∼100-fold lower. Both screens returned unique scaffolds and demonstrated that a crystal structure of a stabilized peptide-binding GPCR can guide the discovery of small-molecule agonists. The complementary advantages of exploring fragment- and lead-like chemical space suggest that these strategies should be applied synergistically in structure-based screens against challenging GPCR targets.

In silico design of novel probes for the atypical opioid receptor MRGPRX2

The primate-exclusive MRGPRX2 G protein-coupled receptor (GPCR) has been suggested to modulate pain and itch. Despite putative peptide and small molecule MRGPRX2 agonists, selective nanomolar potency probes have not yet been reported. To identify a MRGPRX2 probe, we first screened 5,695 small molecules and found many opioid compounds activated MRGPRX2, including (−)- and (+)-morphine, hydrocodone, sinomenine, dextromethorphan and the prodynorphin-derived peptides, dynorphin A, dynorphin B, and α- and β-neoendorphin. We used these to select for mutagenesis-validated homology models and docked almost 4 million small molecules. From this docking, we predicted ZINC-3573, which represents a potent MRGPRX2-selective agonist, showing little activity against 315 other GPCRs and 97 representative kinases, and an essentially inactive enantiomer. ZINC-3573 activates endogenous MRGPRX2 in a human mast cell line inducing degranulation and calcium release. MRGPRX2 is a unique atypical opioid-like receptor important for modulating mast cell degranulation, which can now be specifically modulated with ZINC-3573.

Structural Analysis of Chemokine Receptor-Ligand Interactions

This review focuses on the construction and application of structural chemokine receptor models for the elucidation of molecular determinants of chemokine receptor modulation and the structure-based discovery and design of chemokine receptor ligands. A comparative analysis of ligand binding pockets in chemokine receptors is presented, including a detailed description of the CXCR4, CCR2, CCR5, CCR9, and US28 X-ray structures, and their implication for modeling molecular interactions of chemokine receptors with small-molecule ligands, peptide ligands, and large antibodies and chemokines. These studies demonstrate how the integration of new structural information on chemokine receptors with extensive structure–activity relationship and site-directed mutagenesis data facilitates the prediction of the structure of chemokine receptor–ligand complexes that have not been crystallized. Finally, a review of structure-based ligand discovery and design studies based on chemokine receptor crystal structures and homology models illustrates the possibilities and challenges to find novel ligands for chemokine receptors.

Structural studies of G protein-coupled receptors

G protein-coupled receptors (GPCRs) comprise the largest membrane protein family. These receptors sense a variety of signaling molecules, activate multiple intracellular signal pathways, and act as the targets of over 40% of marketed drugs. Recent progress on GPCR structural studies provides invaluable insights into the structure-function relationship of the GPCR superfamily, deepening our understanding about the molecular mechanisms of GPCR signal transduction. Here, we review recent breakthroughs on GPCR structure determination and the structural features of GPCRs, and take the structures of chemokine receptor CCR5 and purinergic receptors P2Y₁ R and P2Y₁₂ R as examples to discuss the importance of GPCR structures on functional studies and drug discovery. In addition, we discuss the prospect of GPCR structure-based drug discovery. © 2016 IUBMB Life, 68(11):894-903, 2016.

Identifying ligands at orphan GPCRs: current status using structure-based approaches

GPCRs are the most successful pharmaceutical targets in history. Nevertheless, the pharmacology of many GPCRs remains inaccessible as their endogenous or exogenous modulators have not been discovered. Tools that explore the physiological functions and pharmacological potential of these 'orphan' GPCRs, whether they are endogenous and/or surrogate ligands, are therefore of paramount importance. Rates of receptor deorphanization determined by traditional reverse pharmacology methods have slowed, indicating a need for the development of more sophisticated and efficient ligand screening approaches. Here, we discuss the use of structure-based ligand discovery approaches to identify small molecule modulators for exploring the function of orphan GPCRs. These studies have been buoyed by the growing number of GPCR crystal structures solved in the past decade, providing a broad range of template structures for homology modelling of orphans. This review discusses the methods used to establish the appropriate signalling assays to test orphan receptor activity and provides current examples of structure-based methods used to identify ligands of orphan GPCRs. Linked Articles This article is part of a themed section on Molecular Pharmacology of G Protein-Coupled Receptors. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v173.20/issuetoc.

Structure-based discovery of opioid analgesics with reduced side effects

Morphine is an alkaloid from the opium poppy used to treat pain. The potentially lethal side effects of morphine and related opioids-which include fatal respiratory depression-are thought to be mediated by μ-opioid-receptor (μOR) signalling through the β-arrestin pathway or by actions at other receptors. Conversely, G-protein μOR signalling is thought to confer analgesia. Here we computationally dock over 3 million molecules against the μOR structure and identify new scaffolds unrelated to known opioids. Structure-based optimization yields PZM21-a potent Gi activator with exceptional selectivity for μOR and minimal β-arrestin-2 recruitment. Unlike morphine, PZM21 is more efficacious for the affective component of analgesia versus the reflexive component and is devoid of both respiratory depression and morphine-like reinforcing activity in mice at equi-analgesic doses. PZM21 thus serves as both a probe to disentangle μOR signalling and a therapeutic lead that is devoid of many of the side effects of current opioids.

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.

Discovery and characterization of novel small-molecule CXCR4 receptor agonists and antagonists

The chemokine CXCL12 (SDF-1) and its cognate receptor CXCR4 are involved in a large number of physiological processes including HIV-1 infectivity, inflammation, tumorigenesis, stem cell migration, and autoimmune diseases. While previous efforts have identified a number of CXCR4 antagonists, there have been no small molecule agonists reported. Herein, we describe the identification of a novel series of CXCR4 modulators, including the first small molecules to display agonist behavior against this receptor, using a combination of structure- and ligand-based virtual screening. These agonists produce robust calcium mobilization in human melanoma cell lines which can be blocked by the CXCR4-selective antagonist AMD3100. We also demonstrate the ability of these new agonists to induce receptor internalization, ERK activation, and chemotaxis, all hallmarks of CXCR4 activation. Our results describe a new series of biologically relevant small molecules that will enable further study of the CXCR4 receptor and may contribute to the development of new therapeutics.

Function-specific virtual screening for GPCR ligands using a combined scoring method

The ability of scoring functions to correctly select and rank docking poses of small molecules in protein binding sites is highly target dependent, which presents a challenge for structure-based drug discovery. Here we describe a virtual screening method that combines an energy-based docking scoring function with a molecular interaction fingerprint (IFP) to identify new ligands based on G protein-coupled receptor (GPCR) crystal structures. The consensus scoring method is prospectively evaluated by: 1) the discovery of chemically novel, fragment-like, high affinity histamine H₁ receptor (H₁R) antagonists/inverse agonists, 2) the selective structure-based identification of ß₂-adrenoceptor (ß₂R) agonists, and 3) the experimental validation and comparison of the combined and individual scoring approaches. Systematic retrospective virtual screening simulations allowed the definition of scoring cut-offs for the identification of H₁R and ß₂R ligands and the selection of an optimal ß-adrenoceptor crystal structure for the discrimination between ß₂R agonists and antagonists. The consensus approach resulted in the experimental validation of 53% of the ß₂R and 73% of the H₁R virtual screening hits with up to nanomolar affinities and potencies. The selective identification of ß₂R agonists shows the possibilities of structure-based prediction of GPCR ligand function by integrating protein-ligand binding mode information.

SLC Transporters: Structure, Function, and Drug Discovery

The human Solute Carrier (SLC) transporters are important targets for drug development. Structure-based drug discovery for SLC transporters requires the description of their structure, dynamics, and mechanism of interaction with small molecule ligands and ions. The recent determination of atomic structures of human SLC transporters and their homologs, combined with improved computational power and prediction methods have led to an increased applicability of structure-based drug design methods for human SLC members. In this review, we provide an overview of the SLC transporters' structures and transport mechanisms. We then describe computational techniques, such as homology modeling and virtual screening that are emerging as key tools to discover chemical probes for human SLC members. We illustrate the utility of these methods by presenting case studies in which rational integration of computation and experiment was used to characterize SLC members that transport key nutrients and metabolites, including the amino acid transporters LAT-1 and ASCT2, the SLC13 family of citric acid cycle intermediate transporters, and the glucose transporter GLUT1. We conclude with a brief discussion about future directions in structure-based drug discovery for the human SLC superfamily, one of the most structurally and functionally diverse protein families in human.

Multi-objective active machine learning rapidly improves structure-activity models and reveals new protein-protein interaction inhibitors

Active machine learning puts artificial intelligence in charge of a sequential, feedback-driven discovery process. We present the application of a multi-objective active learning scheme for identifying small molecules that inhibit the protein-protein interaction between the anti-cancer target CXC chemokine receptor 4 (CXCR4) and its endogenous ligand CXCL-12 (SDF-1). Experimental design by active learning was used to retrieve informative active compounds that continuously improved the adaptive structure-activity model. The balanced character of the compound selection function rapidly delivered new molecular structures with the desired inhibitory activity and at the same time allowed us to focus on informative compounds for model adjustment. The results of our study validate active learning for prospective ligand finding by adaptive, focused screening of large compound repositories and virtual compound libraries.