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

Bivalent ligands targeting chemokine receptor dimerization: molecular design and functional studies

Increasing evidence has shown that chemokine receptors may form functional dimers with unique pharmacological profiles. A common practice to characterize such G protein-coupled receptor dimerization processes is to apply bivalent ligands as chemical probes which can interact with both receptors simultaneously. Currently, two chemokine receptor dimers have been studied by applying bivalent compounds: the CXCR4-CXCR4 homodimer and the CCR5-MOR heterodimer. These bivalent compounds have revealed how dimerization influences receptor function and may lead to novel therapeutics. Future design of bivalent ligands for chemokine receptor dimers may be aided with the recently available CXCR4 homodimer, and CCR5 monomer crystal structures by more accurately simulating chemokine receptors and their dimers.

GPCR-Bench: A Benchmarking Set and Practitioners' Guide for G Protein-Coupled Receptor Docking

Virtual screening is routinely used to discover new ligands and in particular new ligand chemotypes for G protein-coupled receptors (GPCRs). To prepare for a virtual screen, we often tailor a docking protocol that will enable us to select the best candidates for further screening. To aid this, we created GPCR-Bench, a publically available docking benchmarking set in the spirit of the DUD and DUD-E reference data sets for validation studies, containing 25 nonredundant high-resolution GPCR costructures with an accompanying set of diverse ligands and computational decoy molecules for each target. Benchmarking sets are often used to compare docking protocols; however, it is important to evaluate docking methods not by "retrospective" hit rates but by the actual likelihood that they will produce novel prospective hits. Therefore, docking protocols must not only rank active molecules highly but also produce good poses that a chemist will select for purchase and screening. Currently, no simple objective machine-scriptable function exists that can do this; instead, docking hit lists must be subjectively examined in a consistent way to compare between docking methods. We present here a case study highlighting considerations we feel are of importance when evaluating a method, intended to be useful as a practitioners' guide.

Docking and Linking of Fragments To Discover Jumonji Histone Demethylase Inhibitors

Development of tool molecules that inhibit Jumonji demethylases allows for the investigation of cancer-associated transcription. While scaffolds such as 2,4-pyridinedicarboxylic acid (2,4-PDCA) are potent inhibitors, they exhibit limited selectivity. To discover new inhibitors for the KDM4 demethylases, enzymes overexpressed in several cancers, we docked a library of 600,000 fragments into the high-resolution structure of KDM4A. Among the most interesting chemotypes were the 5-aminosalicylates, which docked in two distinct but overlapping orientations. Docking poses informed the design of covalently linked fragment compounds, which were further derivatized. This combined approach improved affinity by ∼ 3 log-orders to yield compound 35 (Ki = 43 nM). Several hybrid inhibitors were selective for KDM4C over the related enzymes FIH, KDM2A, and KDM6B while lacking selectivity against the KDM3 and KDM5 subfamilies. Cocrystal structures corroborated the docking predictions. This study extends the use of structure-based docking from fragment discovery to fragment linking optimization, yielding novel KDM4 inhibitors.

Structure-Based Discovery of Novel and Selective 5-Hydroxytryptamine 2B Receptor Antagonists for the Treatment of Irritable Bowel Syndrome

Here we employed structure-based ligand discovery techniques to explore a recently determined crystal structure of the 5-hydroxytryptamine 2B (5-HT2B) receptor. Ten compounds containing a novel chemical scaffold were identified; among them, seven molecules were active in cellular function assays with the most potent one exhibiting an IC50 value of 27.3 nM. We then systematically probed the binding characteristics of this scaffold by designing, synthesizing, and testing a series of structural modifications. The structure-activity relationship studies strongly support our predicted binding model. The binding profiling across a panel of 11 5-HT receptors indicated that these compounds are highly selective for the 5-HT2B receptor. Oral administration of compound 15 (30 mg/kg) produced significant attenuation of visceral hypersensitivity in a rat model of irritable bowel syndrome (IBS). We expect this novel scaffold will serve as the foundation for the development of 5-HT2B antagonists for the treatment of IBS.

Fragment-Based Discovery of Subtype-Selective Adenosine Receptor Ligands from Homology Models

Fragment-based lead discovery (FBLD) holds great promise for drug discovery, but applications to G protein-coupled receptors (GPCRs) have been limited by a lack of sensitive screening techniques and scarce structural information. If virtual screening against homology models of GPCRs could be used to identify fragment ligands, FBLD could be extended to numerous important drug targets and contribute to efficient lead generation. Access to models of multiple receptors may further enable the discovery of fragments that bind specifically to the desired target. To investigate these questions, we used molecular docking to screen >500 000 fragments against homology models of the A3 and A1 adenosine receptors (ARs) with the goal to discover A3AR-selective ligands. Twenty-one fragments with predicted A3AR-specific binding were evaluated in live-cell fluorescence-based assays; of eight verified ligands, six displayed A3/A1 selectivity, and three of these had high affinities ranging from 0.1 to 1.3 μM. Subsequently, structure-guided fragment-to-lead optimization led to the identification of a >100-fold-selective antagonist with nanomolar affinity from commercial libraries. These results highlight that molecular docking screening can guide fragment-based discovery of selective ligands even if the structures of both the target and antitarget receptors are unknown. The same approach can be readily extended to a large number of pharmaceutically important targets.

Structural basis of receptor sulfotyrosine recognition by a CC chemokine: the N-terminal region of CCR3 bound to CCL11/eotaxin-1

Trafficking of leukocytes in immune surveillance and inflammatory responses is activated by chemokines engaging their receptors. Sulfation of tyrosine residues in peptides derived from the eosinophil chemokine receptor CCR3 dramatically enhances binding to cognate chemokines. We report the structural basis of this recognition and affinity enhancement. We describe the structure of a CC chemokine (CCL11/eotaxin-1) bound to a fragment of a chemokine receptor: residues 8–23 of CCR3, including two sulfotyrosine residues. We also show that intact CCR3 is sulfated and sulfation enhances receptor activity. The CCR3 sulfotyrosine residues form hydrophobic, salt bridge and cation-p interactions with residues that are highly conserved in CC chemokines. However, the orientation of the chemokine relative to the receptor N terminus differs substantially from those observed for two CXC chemokines, suggesting that initial binding of the receptor sulfotyrosine residues guides subsequent steps in receptor activation, thereby influencing the receptor conformational changes and signaling.

Small-Molecule Allosteric Modulators of the Protein Kinase PDK1 from Structure-Based Docking

Finding small molecules that target allosteric sites remains a grand challenge for ligand discovery. In the protein kinase field, only a handful of highly selective allosteric modulators have been found. Thus, more general methods are needed to discover allosteric modulators for additional kinases. Here, we use virtual screening against an ensemble of both crystal structures and comparative models to identify ligands for an allosteric peptide-binding site on the protein kinase PDK1 (the PIF pocket). We optimized these ligands through an analog-by-catalog search that yielded compound 4, which binds to PDK1 with 8 μM affinity. We confirmed the docking poses by determining a crystal structure of PDK1 in complex with 4. Because the PIF pocket appears to be a recurring structural feature of the kinase fold, known generally as the helix αC patch, this approach may enable the discovery of allosteric modulators for other kinases.

Progress toward rationally designed small-molecule peptide and peptidomimetic CXCR4 antagonists

Over the last 5 years, X-ray structures of CXCR4 in complex with three different ligands (the small-molecule antagonist IT1t, the polypeptide antagonist CVX15 and the viral chemokine antagonist vMIP-II) have been released. In addition to the inherent scientific value of these specific X-ray structures, they provide a reliable structural foundation for studies of the molecular interactions between CXCR4 and its key peptide ligands (CXCL12 and HIV-1 gp120), and serve as valuable templates for further development of small-molecule CXCR4 antagonists with therapeutic potential. We here review recent computational studies of the molecular interactions between CXCR4 and its peptide ligands – based on the X-ray structures of CXCR4 – and the current status of small-molecule peptide and peptidomimetic CXCR4 antagonists.

Three Homology Models of PAR2 Derived from Different Templates: Application to Antagonist Discovery

Protease activated receptor 2 (PAR2) is an unusual G-protein coupled receptor (GPCR) involved in inflammation and metabolism. It is activated through cleavage of its N-terminus by proteases. The new N-terminus functions as a tethered ligand that folds back and intramolecularly activates PAR2, initiating multiple downstream signaling pathways. The only compounds reported to date to inhibit PAR2 activation are of moderate potency. Three structural models for PAR2 have been constructed based on sequence homology with known crystal structures for bovine rhodopsin, human ORL-1 (also called nociceptin/orphanin FQ receptor), and human PAR1. The three PAR2 model structures were compared and used to predict potential interactions with ligands. Virtual screening for ligands using the Chembridge database, and either ORL-1 or PAR1 derived PAR2 models led to identification of eight new small molecule PAR2 antagonists (IC50 10-100 μM). Notably, the most potent compound 1 (IC50 11 μM) was derived from the less homologous template protein, human ORL-1. The results suggest that virtual screening against multiple homology models of the same GPCR can produce structurally diverse antagonists and that this may be desirable even when some models have less sequence homology with the target protein.

Discovery of Selective Small-Molecule Inhibitors for the β-Catenin/T-Cell Factor Protein-Protein Interaction through the Optimization of the Acyl Hydrazone Moiety

Acyl hydrazone is an important functional group for the discovery of bioactive small molecules. This functional group is also recognized as a pan assay interference structure. In this study, a new small-molecule inhibitor for the β-catenin/Tcf protein-protein interaction (PPI), ZINC02092166, was identified through AlphaScreen and FP assays. This compound contains an acyl hydrazone group and exhibits higher inhibitory activities in cell-based assays than biochemical assays. Inhibitor optimization resulted in chemically stable derivatives that disrupt the β-catenin/Tcf PPI. The binding mode of new inhibitors was characterized by site-directed mutagenesis and structure-activity relationship studies. This series of inhibitors with a new scaffold exhibits dual selectivity for β-catenin/Tcf over β-catenin/cadherin and β-catenin/APC PPIs. One derivative of this series suppresses canonical Wnt signaling, downregulates the expression of Wnt target genes, and inhibits the growth of cancer cells. This compound represents a solid starting point for the development of potent and selective β-catenin/Tcf inhibitors.

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.

Docking and Virtual Screening Strategies for GPCR Drug Discovery

Progress in structure determination of G protein-coupled receptors (GPCRs) has made it possible to apply structure-based drug design (SBDD) methods to this pharmaceutically important target class. The quality of GPCR structures available for SBDD projects fall on a spectrum ranging from high resolution crystal structures (<2 Å), where all water molecules in the binding pocket are resolved, to lower resolution (>3 Å) where some protein residues are not resolved, and finally to homology models that are built using distantly related templates. Each GPCR project involves a distinct set of opportunities and challenges, and requires different approaches to model the interaction between the receptor and the ligands. In this review we will discuss docking and virtual screening to GPCRs, and highlight several refinement and post-processing steps that can be used to improve the accuracy of these calculations. Several examples are discussed that illustrate specific steps that can be taken to improve upon the docking and virtual screening accuracy. While GPCRs are a unique target class, many of the methods and strategies outlined in this review are general and therefore applicable to other protein families.

Hierarchical virtual screening approaches in small molecule drug discovery

Virtual screening has played a significant role in the discovery of small molecule inhibitors of therapeutic targets in last two decades. Various ligand and structure-based virtual screening approaches are employed to identify small molecule ligands for proteins of interest. These approaches are often combined in either hierarchical or parallel manner to take advantage of the strength and avoid the limitations associated with individual methods. Hierarchical combination of ligand and structure-based virtual screening approaches has received noteworthy success in numerous drug discovery campaigns. In hierarchical virtual screening, several filters using ligand and structure-based approaches are sequentially applied to reduce a large screening library to a number small enough for experimental testing. In this review, we focus on different hierarchical virtual screening strategies and their application in the discovery of small molecule modulators of important drug targets. Several virtual screening studies are discussed to demonstrate the successful application of hierarchical virtual screening in small molecule drug discovery.

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.

Structure-based virtual screening for fragment-like ligands of the G protein-coupled histamine H4 receptor

Structure-based virtual screening using H₁R- and β₂R-based histamine H₄R homology models identified 9 fragments with an affinity ranging from 0.14 to 6.3 μm for H₄R.

Docking and free energy perturbation studies of ligand binding in the kappa opioid receptor

The kappa opioid receptor (KOR) is an important target for pain and depression therapeutics that lack harmful and addictive qualities of existing medications. We present a model for the binding of morphinan ligands and JDTic to the JDTic/KOR crystal structure based on an atomic level description of the water structure within its active site. The model contains two key interaction motifs that are supported by experimental evidence. The first is the formation of a salt bridge between the ligand and Asp 138(3.32) in transmembrane domain (TM) 3. The second is the stabilization by the ligand of two high energy, isolated, and ice-like waters near TM5 and TM6. This model is incorporated via energetic terms into a new empirical scoring function, WScore, designed to assess interactions between ligands and localized water in a binding site. Pairing WScore with the docking program Glide discriminates known active KOR ligands from large sets of decoy molecules much better than Glide's older generation scoring functions, SP and XP. We also use rigorous free energy perturbation calculations to provide evidence for the proposed mechanism of interaction between ligands and KOR. The molecular description of ligand binding in KOR should provide a good starting point for future drug discovery efforts for this receptor.

Computational studies to predict or explain G protein coupled receptor polypharmacology

Since G protein-coupled receptors (GPCRs) belong to a very large superfamily of evolutionarily related receptors (>800 members in humans), and due to the rapid progress on their structural biology, they are ideal candidates for polypharmacology studies. Broad screening and bioinformatics/chemoinformatics have been applied to understanding off-target effects of GPCR ligands. It is now feasible to approach the question of GPCR polypharmacology using molecular modeling and the available X-ray GPCR structures. As an example, large and sterically constrained adenosine derivatives (potent adenosine receptor ligands with low conformational freedom and multiple extended substituents) were screened for binding at diverse receptors. Unanticipated off-target interactions, including at biogenic amine receptors, were then modeled using a structure-based approach to provide a consistent understanding of recognition. A conserved Asp in TM3 changed its role from counterion for biogenic amines to characteristic H-bonding to adenosine. The same systematic approach could potentially be applied to many GPCRs or other receptors using other sets of congeneric ligands.

The receptor concept in 3D: from hypothesis and metaphor to GPCR-ligand structures

The first mentioning of the word "receptor" for the structure with which a bioactive compound should react for obtaining its specific influence on a physiological system goes back to the years around 1900. The receptor concept was adapted from the lock and key theory for the enzyme substrate and blockers interactions. Through the years the concept, in the beginning rather being a metaphor, not a model, was refined and became reality in recent years. Not only the structures of receptors were elucidated, also the receptor machineries were unraveled. Following a brief historical review we will describe how the recent breakthroughs in the experimental determination of G protein-coupled receptor (GPCR) crystal structures can be complemented by computational modeling, medicinal chemistry, biochemical, and molecular pharmacological studies to obtain new insights into the molecular determinants of GPCR-ligand binding and activation. We will furthermore discuss how this information can be used for structure-based discovery of novel GPCR ligands that bind specific (allosteric) binding sites with desired effects on GPCR functional activity.

Advances in GPCR modeling evaluated by the GPCR Dock 2013 assessment: meeting new challenges

Despite tremendous successes of GPCR crystallography, the receptors with available structures represent only a small fraction of human GPCRs. An important role of the modeling community is to maximize structural insights for the remaining receptors and complexes. The community-wide GPCR Dock assessment was established to stimulate and monitor the progress in molecular modeling and ligand docking for GPCRs. The four targets in the present third assessment round presented new and diverse challenges for modelers, including prediction of allosteric ligand interaction and activation states in 5-hydroxytryptamine receptors 1B and 2B, and modeling by extremely distant homology for smoothened receptor. Forty-four modeling groups participated in the assessment. State-of-the-art modeling approaches achieved close-to-experimental accuracy for small rigid orthosteric ligands and models built by close homology, and they correctly predicted protein fold for distant homology targets. Predictions of long loops and GPCR activation states remain unsolved problems.

Anti-HIV small-molecule binding in the peptide subpocket of the CXCR4:CVX15 crystal structure

The CXC chemokine receptor 4 (CXCR4) is involved in chemotaxis and serves as a coreceptor for T-tropic HIV-1 viral entry, thus making this receptor an attractive drug target. Recently, crystal structures of CXCR4 were reported as complexes with the small molecule IT1t and the CVX15 peptide. Follow-up efforts to model different antagonists into the small molecule CXCR4:IT1t crystal structure did not generate poses consistent with either the X-ray crystal structure or site-directed mutagenesis (SDM). Here, we compare the binding pockets of the two CXCR4 crystal structures, revealing differences in helices IV, V, VI, and VII, with major differences for the His203 residue buried in the binding pocket. The small molecule antagonist AMD11070 was docked into both CXCR4 crystal structures. An AMD11070 pose identified from the CXCR4:CVX15 model presented interactions with Asp171, Glu288, Trp94, and Asp97, consistent with published SDM data, thus suggesting it is the bioactive pose. A CXCR4 receptor model was optimized around this pose of AMD11070, and the resulting model correlated HIV-1 inhibition with MM-GBSA docking scores for a congeneric AMD11070-like series. Subsequent NAMFIS NMR results successfully linked the proposed binding pose to an independent experimental structure. These results strongly suggest that not all small molecules will bind to CXCR4 in a similar manner as IT1t. Instead, the CXCR4:CVX15 crystal structure may provide a binding locus for small organic molecules that is more suitable than the secondary IT1t site. This work is expected to provide modeling insights useful for future CXCR4 antagonist and X4-tropic HIV-1 based drug design efforts.

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.

Incorporation of protein flexibility and conformational energy penalties in docking screens to improve ligand discovery

Proteins fluctuate between alternative conformations, which presents a challenge for ligand discovery because such flexibility is difficult to treat computationally owing to problems with conformational sampling and energy weighting. Here we describe a flexible docking method that samples and weights protein conformations using experimentally derived conformations as a guide. The crystallographically refined occupancies of these conformations, which are observable in an apo receptor structure, define energy penalties for docking. In a large prospective library screen, we identified new ligands that target specific receptor conformations of a cavity in cytochrome c peroxidase, and we confirm both ligand pose and associated receptor conformation predictions by crystallography. The inclusion of receptor flexibility led to ligands with new chemotypes and physical properties. By exploiting experimental measures of loop and side-chain flexibility, this method can be extended to the discovery of new ligands for hundreds of targets in the Protein Data Bank for which similar experimental information is available.

Selecting an optimal number of binding site waters to improve virtual screening enrichments against the adenosine A2A receptor

A major challenge in structure-based virtual screening (VS) involves the treatment of explicit water molecules during docking in order to improve the enrichment of active compounds over decoys. Here we have investigated this in the context of the adenosine A2A receptor, where water molecules have previously been shown to be important for achieving high enrichment rates with docking, and where the positions of some binding site waters are known from a high-resolution crystal structure. The effect of these waters (both their presence and orientations) on VS enrichment was assessed using a carefully curated set of 299 high affinity A2A antagonists and 17,337 decoys. We show that including certain crystal waters greatly improves VS enrichment and that optimization of water hydrogen positions is needed in order to achieve the best results. We also show that waters derived from a molecular dynamics simulation - without any knowledge of crystallographic waters - can improve enrichments to a similar degree as the crystallographic waters, which makes this strategy applicable to structures without experimental knowledge of water positions. Finally, we used decision trees to select an ensemble of structures with different water molecule positions and orientations that outperforms any single structure with water molecules. The approach presented here is validated against independent test sets of A2A receptor antagonists and decoys from the literature. In general, this water optimization strategy could be applied to any target with waters-mediated protein-ligand interactions.

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.

Compound activity prediction using models of binding pockets or ligand properties in 3D

Transient interactions of endogenous and exogenous small molecules with flexible binding sites in proteins or macromolecular assemblies play a critical role in all biological processes. Current advances in high-resolution protein structure determination, database development, and docking methodology make it possible to design three-dimensional models for prediction of such interactions with increasing accuracy and specificity. Using the data collected in the Pocketome encyclopedia, we here provide an overview of two types of the three-dimensional ligand activity models, pocketbased and ligand property-based, for two important classes of proteins, nuclear and G-protein coupled receptors. For half the targets, the pocket models discriminate actives from property matched decoys with acceptable accuracy (the area under ROC curve, AUC, exceeding 84%) and for about one fifth of the targets with high accuracy (AUC > 95%). The 3D ligand property field models performed better than 95% in half of the cases. The high performance models can already become a basis of activity predictions for new chemicals. Family-wide benchmarking of the models highlights strengths of both approaches and helps identify their inherent bottlenecks and challenges.

A novel inhibitor of Mammalian triosephosphate isomerase found by an in silico approach

Triosephosphate isomerase (TIM) is an essential, highly conserved component of glycolysis. Tumors are often dependent on glycolysis for energy and metabolite production (the Warburg effect). Glycolysis inhibitors thus show promise as cancer treatments. TIM inhibition, unlike inhibition of other glycolysis enzymes, also produces toxic methylglyoxal targeted to regions of high glycolysis, an effect that might also be therapeutically useful. Thus TIM is an attractive drug target. A total of 338,562 lead-like molecules were analyzed computationally to find TIM inhibitors by an efficient “double screen” approach. The first fragment-sized compounds were studied using structure-based virtual screening to identify binding motifs for mammalian TIM. Subsequently, larger compounds, filtered to meet the binding criteria developed in the first analysis, were ranked using a second round of structure-based virtual screening. A compound was found that inhibited mammalian TIM in vitro in the micromolar range. Docking and molecular dynamics (MD) suggested that the inhibitor made hydrogen bond contacts with TIM catalytic residues. In addition, hydrophobic contacts were made throughout the binding site. All predicted inhibitor-TIM interactions involved TIM residues that were highly conserved. The discovered compound may provide a scaffold for elaboration of other inhibitors.

The GPCR crystallography boom: providing an invaluable source of structural information and expanding the scope of homology modeling

G protein-coupled receptors (GPCRs) are integral membrane proteins of high pharmaceutical interest. Until relatively recently, their structures have been particularly elusive, and rhodopsin has been for many years the only member of the superfamily with experimentally elucidated structures. However, a number of recent technical and scientific advancements made the determination of GPCR structures more feasible, thus leading to the solution of the structures of several receptors. Besides providing direct structural information, these experimental GPCR structures also provide templates for the construction of GPCR models. In depth studies have been performed to probe the accuracy of these models, in particular with respect to the interactions with their ligands, and to assess their applicability the rational discovery of GPCR modulators. Given the current state of the art and the pace of the field, the future of GPCR structural studies is likely to be characterized by a landscape populated by an increasingly higher number of experimental and theoretical structures.

Chemokine receptor modeling: an interdisciplinary approach to drug design

Chemokines and their receptors are integral components of the immune response, regulating lymphocyte development, homing and trafficking, and playing a key role in the pathophysiology of many diseases. Chemokine receptors have, therefore, become the target for both small-molecule, peptide and antibody therapeutics. Chemokine receptors belong to the family of seven transmembrane receptor class A G protein-coupled receptors. The publication of the crystal structure of the archetypal class A seven transmembrane receptor protein rhodopsin, and other G protein-coupled receptors, including C-X-C chemokine receptor 4 and C-C chemokine receptor 5, provided the opportunity to create homology models of chemokine receptors. In this review, we describe an interdisciplinary approach to chemokine receptor modeling and the utility of this approach for structure-based drug design of chemokine receptor inhibitors.

Homology modeling of human muscarinic acetylcholine receptors

We have developed homology models of the acetylcholine muscarinic receptors M₁R-M₅R, based on the β₂-adrenergic receptor crystal as the template. This is the first report of homology modeling of all five subtypes of acetylcholine muscarinic receptors with binding sites optimized for ligand binding. The models were evaluated for their ability to discriminate between muscarinic antagonists and decoy compounds using virtual screening using enrichment factors, area under the ROC curve (AUC), and an early enrichment measure, LogAUC. The models produce rational binding modes of docked ligands as well as good enrichment capacity when tested against property-matched decoy libraries, which demonstrates their unbiased predictive ability. To test the relative effects of homology model template selection and the binding site optimization procedure, we generated and evaluated a naïve M₂R model, using the M₃R crystal structure as a template. Our results confirm previous findings that binding site optimization using ligand(s) active at a particular receptor, i.e. including functional knowledge into the model building process, has a more pronounced effect on model quality than target-template sequence similarity. The optimized M₁R-M₅R homology models are made available as part of the Supporting Information to allow researchers to use these structures, compare them to their own results, and thus advance the development of better modeling approaches.

Ligand pose and orientational sampling in molecular docking

Molecular docking remains an important tool for structure-based screening to find new ligands and chemical probes. As docking ambitions grow to include new scoring function terms, and to address ever more targets, the reliability and extendability of the orientation sampling, and the throughput of the method, become pressing. Here we explore sampling techniques that eliminate stochastic behavior in DOCK3.6, allowing us to optimize the method for regularly variable sampling of orientations. This also enabled a focused effort to optimize the code for efficiency, with a three-fold increase in the speed of the program. This, in turn, facilitated extensive testing of the method on the 102 targets, 22,805 ligands and 1,411,214 decoys of the Directory of Useful Decoys - Enhanced (DUD-E) benchmarking set, at multiple levels of sampling. Encouragingly, we observe that as sampling increases from 50 to 500 to 2000 to 5000 to 20000 molecular orientations in the binding site (and so from about 1×10¹⁰ to 4×10¹⁰ to 1×10¹¹ to 2×10¹¹ to 5×10¹¹ mean atoms scored per target, since multiple conformations are sampled per orientation), the enrichment of ligands over decoys monotonically increases for most DUD-E targets. Meanwhile, including internal electrostatics in the evaluation ligand conformational energies, and restricting aromatic hydroxyls to low energy rotamers, further improved enrichment values. Several of the strategies used here to improve the efficiency of the code are broadly applicable in the field.

Complementarity between in silico and biophysical screening approaches in fragment-based lead discovery against the A(2A) adenosine receptor

Fragment-based lead discovery (FBLD) is becoming an increasingly important method in drug development. We have explored the potential to complement NMR-based biophysical screening of chemical libraries with molecular docking in FBLD against the A(2A) adenosine receptor (A(2A)AR), a drug target for inflammation and Parkinson's disease. Prior to an NMR-based screen of a fragment library against the A(2A)AR, molecular docking against a crystal structure was used to rank the same set of molecules by their predicted affinities. Molecular docking was able to predict four out of the five orthosteric ligands discovered by NMR among the top 5% of the ranked library, suggesting that structure-based methods could be used to prioritize among primary hits from biophysical screens. In addition, three fragments that were top-ranked by molecular docking, but had not been picked up by the NMR-based method, were demonstrated to be A(2A)AR ligands. While biophysical approaches for fragment screening are typically limited to a few thousand compounds, the docking screen was extended to include 328,000 commercially available fragments. Twenty-two top-ranked compounds were tested in radioligand binding assays, and 14 of these were A(2A)AR ligands with K(i) values ranging from 2 to 240 μM. Optimization of fragments was guided by molecular dynamics simulations and free energy calculations. The results illuminate strengths and weaknesses of molecular docking and demonstrate that this method can serve as a valuable complementary tool to biophysical screening in FBLD.

Structure-based ligand discovery targeting orthosteric and allosteric pockets of dopamine receptors

Small molecules targeting allosteric pockets of G protein-coupled receptors (GPCRs) have a great therapeutic potential for the treatment of neurologic and other chronic disorders. Here we performed virtual screening for orthosteric and putative allosteric ligands of the human dopamine D3 receptor (D3R) using two optimized crystal-structure-based models: the receptor with an empty binding pocket (D3R(APO)), and the receptor complex with dopamine (D3R(Dopa)). Subsequent biochemical and functional characterization revealed 14 novel ligands with a binding affinity of better than 10 μM in the D3R(APO) candidate list (56% hit rate), and 8 novel ligands in the D3R(Dopa) list (32% hit rate). Most ligands in the D3R(APO) model span both orthosteric and extended pockets and behave as antagonists at D3R, with compound 7 showing the highest potency of dopamine inhibition (IC₅₀ = 7 nM). In contrast, compounds identified by the D3R(Dopa) model are predicted to occupy an allosteric site at the extracellular extension of the pocket, and they all lack the anchoring amino group. Compounds targeting the allosteric site display a variety of functional activity profiles, where behavior of at least two compounds (23 and 26) is consistent with noncompetitive allosteric modulation of dopamine signaling in the extracellular signal-regulated kinase 1 and 2 phosphorylation and β-arrestin recruitment assays. The high affinity and ligand efficiency of the chemically diverse hits identified in this study suggest utility of structure-based screening targeting allosteric sites of GPCRs.

Fragment-based optimization of small molecule CXCL12 inhibitors for antagonizing the CXCL12/CXCR4 interaction

The chemokine CXCL12 and its G protein-coupled receptor (GPCR) CXCR4 are high-priority clinical targets because of their involvement in metastatic cancers (also implicated in autoimmune disease and cardiovascular disease). Because chemokines interact with two distinct sites to bind and activate their receptors, both the GPCRs and chemokines are potential targets for small molecule inhibition. A number of chemokines have been validated as targets for drug development, but virtually all drug discovery efforts focus on the GPCRs. However, all CXCR4 receptor antagonists with the exception of MSX-122 have failed in clinical trials due to unmanageable toxicities, emphasizing the need for alternative strategies to interfere with CXCL12/CXCR4-guided metastatic homing. Although targeting the relatively featureless surface of CXCL12 was presumed to be challenging, focusing efforts at the sulfotyrosine (sY) binding pockets proved successful for procuring initial hits. Using a hybrid structure-based in silico/NMR screening strategy, we recently identified a ligand that occludes the receptor recognition site. From this initial hit, we designed a small fragment library containing only nine tetrazole derivatives using a fragment-based and bioisostere approach to target the sY binding sites of CXCL12. Compound binding modes and affinities were studied by 2D NMR spectroscopy, X-ray crystallography, molecular docking and cell-based functional assays. Our results demonstrate that the sY binding sites are conducive to the development of high affinity inhibitors with better ligand efficiency (LE) than typical protein-protein interaction inhibitors (LE ≤ 0.24). Our novel tetrazole-based fragment 18 was identified to bind the sY21 site with a K(d) of 24 μM (LE = 0.30). Optimization of 18 yielded compound 25 which specifically inhibits CXCL12-induced migration with an improvement in potency over the initial hit 9. The fragment from this library that exhibited the highest affinity and ligand efficiency (11: K(d) = 13 μM, LE = 0.33) may serve as a starting point for development of inhibitors targeting the sY12 site.

Muscarinic receptors as model targets and antitargets for structure-based ligand discovery

G protein-coupled receptors (GPCRs) regulate virtually all aspects of human physiology and represent an important class of therapeutic drug targets. Many GPCR-targeted drugs resemble endogenous agonists, often resulting in poor selectivity among receptor subtypes and restricted pharmacologic profiles. The muscarinic acetylcholine receptor family exemplifies these problems; thousands of ligands are known, but few are receptor subtype-selective and nearly all are cationic in nature. Using structure-based docking against the M2 and M3 muscarinic receptors, we screened 3.1 million molecules for ligands with new physical properties, chemotypes, and receptor subtype selectivities. Of 19 docking-prioritized molecules tested against the M2 subtype, 11 had substantial activity and 8 represented new chemotypes. Intriguingly, two were uncharged ligands with low micromolar to high nanomolar Ki values, an observation with few precedents among aminergic GPCRs. To exploit a single amino-acid substitution among the binding pockets between the M2 and M3 receptors, we selected molecules predicted by docking to bind to the M3 and but not the M2 receptor. Of 16 molecules tested, 8 bound to the M3 receptor. Whereas selectivity remained modest for most of these, one was a partial agonist at the M3 receptor without measurable M2 agonism. Consistent with this activity, this compound stimulated insulin release from a mouse β-cell line. These results support the ability of structure-based discovery to identify new ligands with unexplored chemotypes and physical properties, leading to new biologic functions, even in an area as heavily explored as muscarinic pharmacology.

Roles for ordered and bulk solvent in ligand recognition and docking in two related cavities

A key challenge in structure-based discovery is accounting for modulation of protein-ligand interactions by ordered and bulk solvent. To investigate this, we compared ligand binding to a buried cavity in Cytochrome c Peroxidase (CcP), where affinity is dominated by a single ionic interaction, versus a cavity variant partly opened to solvent by loop deletion. This opening had unexpected effects on ligand orientation, affinity, and ordered water structure. Some ligands lost over ten-fold in affinity and reoriented in the cavity, while others retained their geometries, formed new interactions with water networks, and improved affinity. To test our ability to discover new ligands against this opened site prospectively, a 534,000 fragment library was docked against the open cavity using two models of ligand solvation. Using an older solvation model that prioritized many neutral molecules, three such uncharged docking hits were tested, none of which was observed to bind; these molecules were not highly ranked by the new, context-dependent solvation score. Using this new method, another 15 highly-ranked molecules were tested for binding. In contrast to the previous result, 14 of these bound detectably, with affinities ranging from 8 µM to 2 mM. In crystal structures, four of these new ligands superposed well with the docking predictions but two did not, reflecting unanticipated interactions with newly ordered waters molecules. Comparing recognition between this open cavity and its buried analog begins to isolate the roles of ordered solvent in a system that lends itself readily to prospective testing and that may be broadly useful to the community.