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In the hit identification stage of drug discovery, a diverse chemical space needs to be explored to identify initial hits. Contrary to empirical scoring functions, absolute protein-ligand binding free energy perturbation (ABFEP) provides a theoretically more rigorous and accurate description of protein-ligand binding thermodynamics and could in principle greatly improve the hit rates in virtual screening. In this work, we describe an implementation of an accurate and reliable ABFEP method in FEP+. We validated the ABFEP method on eight congeneric compound series binding to eight protein receptors including both neutral and charged ligands. For ligands with net charges, the alchemical ion approach is adopted to avoid artifacts in electrostatic potential energy calculations. The calculated binding free energies are highly correlated with experimental results with the weighted average of R2 of 0.55 for the entire dataset and an overall RMSE of 1.1 kcal/mol when protein reorganization effect upon ligand binding was accounted for. Through ABFEP calculations using apo versus holo protein structures, we demonstrated that the protein conformational and protonation state changes between the apo and holo proteins are the main physical factors contributing to the protein reorganization free energy manifested by the overestimation of raw ABFEP calculated binding free energies using the holo structures of the proteins. Furthermore, we performed ABFEP calculations in three virtual screening applications for hit enrichment. ABFEP greatly improves the hit rates as compared to docking scores or other methods like metadynamics. The highly accurate ABFEP results demonstrated in this work position it as a useful tool to improve the hit rates in virtual screening, thus facilitate hit discovery.

When preparing training, validation and test sets for machine learning on molecular datasets, it is desirable to combine two requirements: 1) robustness, i.e. making a test set that is chemically dissimilar from the training set; 2) data balance, i.e. ensuring that the proportion of data points and the distribution of data labels (categorical) / data values (continuous) are as homogeneous as possible among the sets, for each individual property to model, while partitioning the overall set of compounds as required. Recent literature shows that meeting both these requirements simultaneously is sometimes very difficult. This is especially true for multi-task learning, but also for single-task learning if one aims to balance the distribution of data labels or values, too. In this work we present a method that resolves this issue by first carrying out a chemistry-guided clustering of the initial dataset to ensure the separation of chemical matter, and subsequently applying linear programming to select the lists of clusters that – once assembled into the final sets – result in the best possible data balance.

The computational prediction of relative binding free energies is a crucial goal for drug discovery, and G protein-coupled receptors (GPCRs) are arguably the most important drug target class. However, they present increased complexity to model compared to soluble globular proteins. Despite breakthroughs, experimental X-ray crystal and cryo-EM structures are challenging to attain, meaning computational models of the receptor and ligand binding mode are sometimes necessary. This leads to uncertainty in understanding ligand-protein binding induced changes such as, water positioning and displacement, side chain positioning, hydrogen bond networks, and the overall structure of the hydration shell around the ligand and protein. In other words, the very elements that define structure activity relationships (SARs) and are crucial for accurate binding free energy calculations are typically more uncertain for GPCRs. In this work we use free energy perturbation (FEP) to predict the relative binding free energies for ligands of two different GPCRs. We pinpoint the key aspects for success such as the important role of key water molecules, amino acid ionization states, and the benefit of equilibration with specific ligands. Initial calculations following typical FEP setup and execution protocols delivered no correlation with experiment, but we show how results are improved in a logical and systematic way. This approach gave, in the best cases, a coefficient of determination (R2) compared with experiment in the range of 0.6-0.9 and mean unsigned errors compared to experiment of 0.6-0.7 kcal/mol. We anticipate that our findings will be applicable to other difficult-to-model protein ligand data sets and be of wide interest for the community to continue improving FE binding energy predictions.

CC chemokine receptors 2 (CCR2) and 5 (CCR5) are involved in many inflammatory diseases; however, most CCR2 and CCR5 clinical candidates have been unsuccessful. (Pre)clinical evidence suggests that dual CCR2/CCR5 inhibition might be more effective in the treatment of such multifactorial diseases. In this regard, the highly conserved intracellular binding site in chemokine receptors provides a new avenue for the design of multitarget ligands. In this study, we synthesized and evaluated the biological activity of a series of triazolopyrimidinone derivatives in CCR2 and CCR5. Radioligand binding assays first showed that they bind to the intracellular site of CCR2, and in combination with functional assays on CCR5, we explored structure−affinity/activity relationships in both receptors. Although most compounds were CCR2-selective, 39 and 43 inhibited β-arrestin recruitment in CCR5 with high potency. Moreover, these compounds displayed an insurmountable mechanism of inhibition in both receptors, which holds promise for improved efficacy in inflammatory diseases.

Acute myeloid leukemia (AML) is characterized by fast progression and low survival rates, in which Fms-like tyrosine kinase 3 (FLT3) receptor mutations have been identified as a driver mutation in cancer progression in a subgroup of AML patients. Clinical trials have shown emergence of drug resistant mutants, emphasizing the ongoing need for new chemical matter to enable the treatment of this disease. Here, we present the discovery and topological structure-activity relationship (SAR) study of analogs of isoquinolinesulfonamide H-89, a well-known PKA inhibitor, as FLT3 inhibitors. Surprisingly, we found that the SAR was not consistent with the observed binding mode of H-89 in PKA. Matched molecular pair analysis resulted in the identification of highly active sub-nanomolar azaindoles as novel FLT3-inhibitors. Structure based modelling using the FLT3 crystal structure suggested an alternative, flipped binding orientation of the new inhibitors.

Chemical tools to monitor drug-target engagement of endogenously expressed protein kinases are highly desirable for preclinical target validation in drug discovery. Here, we describe a chemical genetics strategy to selectively study target engagement of endogenous kinases. By substituting a serine residue into cysteine at the DFG-1 position in the ATP-binding pocket, we sensitize the non-receptor tyrosine kinase FES towards covalent labeling by a complementary fluorescent chemical probe. This mutation is introduced in the endogenous FES gene of HL-60 cells using CRISPR/Cas9 gene editing. Leveraging the temporal and acute control offered by our strategy, we show that FES activity is dispensable for differentiation of HL-60 cells towards macrophages. Instead, FES plays a key role in neutrophil phagocytosis via SYK kinase activation. This chemical genetics strategy holds promise as a target validation method for kinases., Chemical tools to monitor drug-target engagement of endogenous enzymes are essential for preclinical target validation. Here, the authors present a chemical genetics strategy to study target engagement of endogenous kinases, achieving specific labeling and inactivation of FES kinase to provide insights into FES’ role in neutrophil phagocytosis.

The recent crystal structures of CC chemokine receptors 2 and 9 (CCR2 and CCR9) have provided structural evidence for an allosteric, intracellular binding site. The high conservation of residues involved in this site suggests its presence in most chemokine receptors, including the close homologue CCR1. By using [H-3]CCR2-RA-[R], a high-affinity, CCR2 intracellular ligand, we report an intracellular binding site in CCR1, where this radioligand also binds with high affinity. In addition, we report the synthesis and biological characterization of a series of pyrrolone derivatives for CCR1 and CCR2, which allowed us to identify several high-affinity intracellular ligands, including selective and potential multitarget antagonists. Evaluation of selected compounds in a functional [S-35]GTP gamma S assay revealed that they act as inverse agonists in CCR1, providing a new manner of pharmacological modulation. Thus, this intracellular binding site enables the design of selective and multitarget inhibitors as a novel therapeutic approach.

[Image: see text] The development of covalent ligands for G protein-coupled receptors (GPCRs) is not a trivial process. Here, we report a streamlined workflow thereto from synthesis to validation, exemplified by the discovery of a covalent antagonist for the human adenosine A(3) receptor (hA(3)AR). Based on the 1H,3H-pyrido[2,1-f]purine-2,4-dione scaffold, a series of ligands bearing a fluorosulfonyl warhead and a varying linker was synthesized. This series was subjected to an affinity screen, revealing compound 17b as the most potent antagonist. In addition, a nonreactive methylsulfonyl derivative 19 was developed as a reversible control compound. A series of assays, comprising time-dependent affinity determination, washout experiments, and [(35)S]GTPγS binding assays, then validated 17b as the covalent antagonist. A combined in silico hA(3)AR-homology model and site-directed mutagenesis study was performed to demonstrate that amino acid residue Y265(7.36) was the unique anchor point of the covalent interaction. This workflow might be applied to other GPCRs to guide the discovery of covalent ligands.

In this work, we present a case study to explore the challenges associated with finding novel molecules for a receptor that has been studied in depth and has a wealth of chemical information available. Specifically, we apply a previously described protocol that incorporates explicit water molecules in the ligand binding site to prospectively screen over 2.5 million drug-like and lead-like compounds from the commercially available eMolecules database in search of novel binders to the adenosine A2A receptor (A2AAR). A total of seventy-one compounds were selected for purchase and biochemical assaying based on high ligand efficiency and high novelty (Tanimoto coefficient ≤0.25 to any A2AAR tested compound). These molecules were then tested for their affinity to the adenosine A2A receptor in a radioligand binding assay. We identified two hits that fulfilled the criterion of ~50 % radioligand displacement at a concentration of 10 μM. Next we selected an additional eight novel molecules that were predicted to make a bidentate interaction with Asn2536.55, a key interacting residue in the binding pocket of the A2AAR. None of these eight molecules were found to be active. Based on these results we discuss the advantages of structure-based methods and the challenges associated with finding chemically novel molecules for well-explored targets. Electronic supplementary material The online version of this article (doi:10.1007/s10822-016-9963-7) contains supplementary material, which is available to authorized users.

The sodium ion site is an allosteric site conserved among many G protein-coupled receptors (GPCRs). Amiloride 1 and 5-(N,N-hexamethylene)amiloride 2 (HMA) supposedly bind in this sodium ion site and can influence orthosteric ligand binding. The availability of a high-resolution X-ray crystal structure of the human adenosine A2A receptor (hA2AAR), in which the allosteric sodium ion site was elucidated, makes it an appropriate model receptor for investigating the allosteric site. In this study, we report the synthesis and evaluation of novel 5'-substituted amiloride derivatives as hA2AAR allosteric antagonists. The potency of the amiloride derivatives was assessed by their ability to displace orthosteric radioligand [(3)H]4-(2-((7-amino-2-(furan-2-yl)-[1,2,4]triazolo[1,5-a]-[1,3,5]triazin-5-yl)amino)ethyl)phenol ([(3)H]ZM-241,385) from both the wild-type and sodium ion site W246A mutant hA2AAR. 4-Ethoxyphenethyl-substituted amiloride 12l was found to be more potent than both amiloride and HMA, and the shift in potency between the wild-type and mutated receptor confirmed its likely binding to the sodium ion site.

The interpretation of high-dimensional structure-activity data sets in drug discovery to predict ligand-protein interaction landscapes is a challenging task. Here we present Drug Discovery Maps (DDM), a machine learning model that maps the activity profile of compounds across an entire protein family, as illustrated here for the kinase family. DDM is based on the t-distributed stochastic neighbor embedding (t-SNE) algorithm to generate a visualization of molecular and biological similarity. DDM maps chemical and target space and predicts the activities of novel kinase inhibitors across the kinome. The model was validated using independent data sets and in a prospective experimental setting, where DDM predicted new inhibitors for FMS-like tyrosine kinase 3 (FLT3), a therapeutic target for the treatment of acute myeloid leukemia. Compounds were resynthesized, yielding highly potent, cellularly active FLT3 inhibitors. Biochemical assays confirmed most of the predicted off-targets. DDM is further unique in that it is completely open-source and available as a ready-to-use executable to facilitate broad and easy adoption.

We report the synthesis and biological evaluation of new derivatives of Capadenoson, a former drug candidate that was previously advanced to phase IIa clinical trials. 19 of the 20 ligands show an affinity below 100 nM at the human adenosine A1 receptor (hA1AR) and display a wide range of residence times at this target (from approx. 5 min (compound 10) up to 132 min (compound 5)). Structure-affinity and structure-kinetics relationships were established, and computational studies of a homology model of the hA1AR revealed crucial interactions for both the affinity and dissociation kinetics of this family of ligands. These results were also combined with global metrics (Ligand Efficiency, cLogP), showing the importance of binding kinetics as an additional way to better select a drug candidate amongst seemingly similar leads.

Recently we identified a sodium ion binding pocket in a high-resolution structure of the human adenosine A2A receptor. In the present study we explored this binding site through site-directed mutagenesis and molecular dynamics simulations. Amino acids in the pocket were mutated to alanine, and their influence on agonist and antagonist affinity, allosterism by sodium ions and amilorides, and receptor functionality was explored. Mutation of the polar residues in the Na(+) pocket were shown to either abrogate (D52A(2.50) and N284A(7.49)) or reduce (S91A(3.39), W246A(6.48), and N280A(7.45)) the negative allosteric effect of sodium ions on agonist binding. Mutations D52A(2.50) and N284A(7.49) completely abolished receptor signaling, whereas mutations S91A(3.39) and N280A(7.45) elevated basal activity and mutations S91A(3.39), W246A(6.48), and N280A(7.45) decreased agonist-stimulated receptor signaling. In molecular dynamics simulations D52A(2.50) directly affected the mobility of sodium ions, which readily migrated to another pocket formed by Glu13(1.39) and His278(7.43). The D52A(2.50) mutation also decreased the potency of amiloride with respect to ligand displacement but did not change orthosteric ligand affinity. In contrast, W246A(6.48) increased some of the allosteric effects of sodium ions and amiloride, whereas orthosteric ligand binding was decreased. These new findings suggest that the sodium ion in the allosteric binding pocket not only impacts ligand affinity but also plays a vital role in receptor signaling. Because the sodium ion binding pocket is highly conserved in other class A G protein-coupled receptors, our findings may have a general relevance for these receptors and may guide the design of novel synthetic allosteric modulators or bitopic ligands.

The increase of publicly available bioactivity data in recent years has fueled and catalyzed research in chemogenomics, data mining, and modeling approaches. As a direct result, over the past few years a multitude of different methods have been reported and evaluated, such as target fishing, nearest neighbor similarity-based methods, and Quantitative Structure Activity Relationship (QSAR)-based protocols. However, such studies are typically conducted on different datasets, using different validation strategies, and different metrics. In this study, different methods were compared using one single standardized dataset obtained from ChEMBL, which is made available to the public, using standardized metrics (BEDROC and Matthews Correlation Coefficient). Specifically, the performance of Naïve Bayes, Random Forests, Support Vector Machines, Logistic Regression, and Deep Neural Networks was assessed using QSAR and proteochemometric (PCM) methods. All methods were validated using both a random split validation and a temporal validation, with the latter being a more realistic benchmark of expected prospective execution. Deep Neural Networks are the top performing classifiers, highlighting the added value of Deep Neural Networks over other more conventional methods. Moreover, the best method (‘DNN_PCM’) performed significantly better at almost one standard deviation higher than the mean performance. Furthermore, Multi-task and PCM implementations were shown to improve performance over single task Deep Neural Networks. Conversely, target prediction performed almost two standard deviations under the mean performance. Random Forests, Support Vector Machines, and Logistic Regression performed around mean performance. Finally, using an ensemble of DNNs, alongside additional tuning, enhanced the relative performance by another 27% (compared with unoptimized ‘DNN_PCM’). Here, a standardized set to test and evaluate different machine learning algorithms in the context of multi-task learning is offered by providing the data and the protocols.Graphical Abstract. Electronic supplementary material The online version of this article (doi:10.1186/s13321-017-0232-0) contains supplementary material, which is available to authorized users.

The structure of the human A2A adenosine receptor has been elucidated by X-ray crystallography with a high affinity non-xanthine antagonist, ZM241385, bound to it. This template molecule served as a starting point for the incorporation of reactive moieties that cause the ligand to covalently bind to the receptor. In particular, we incorporated a fluorosulfonyl moiety onto ZM241385, which yielded LUF7445 (4-((3-((7-amino-2-(furan-2-yl)-[1, 2, 4]triazolo[1,5-a][1, 3, 5]triazin-5-yl)amino)propyl)carbamoyl)benzene sulfonyl fluoride). In a radioligand binding assay, LUF7445 acted as a potent antagonist, with an apparent affinity for the hA2A receptor in the nanomolar range. Its apparent affinity increased with longer incubation time, suggesting an increasing level of covalent binding over time. An in silico A2A-structure-based docking model was used to study the binding mode of LUF7445. This led us to perform site-directed mutagenesis of the A2A receptor to probe and validate the target lysine amino acid K153 for covalent binding. Meanwhile, a functional assay combined with wash-out experiments was set up to investigate the efficacy of covalent binding of LUF7445. All these experiments led us to conclude LUF7445 is a valuable molecular tool for further investigating covalent interactions at this receptor. It may also serve as a prototype for a therapeutic approach in which a covalent antagonist may be needed to counteract prolonged and persistent presence of the endogenous ligand adenosine.KEYWORDS: A2A adenosine receptor; Adenosine; Covalent antagonist; G protein-coupled receptors; Radioligand binding

The expression of human G protein-coupled receptors (GPCRs) in Saccharomyces cerevisiae containing chimeric yeast/mammalian Gα subunits provides a useful tool for the study of GPCR activation. In this study, we used a one-GPCR-one-G protein yeast screening method in combination with molecular modeling and mutagenesis studies to decipher the interaction between GPCRs and the C-terminus of different α-subunits of G proteins. We chose the human adenosine A2B receptor (hA2BR) as a paradigm, a typical class A GPCR that shows promiscuous behavior in G protein coupling in this yeast system. The wild-type hA2BR and five mutant receptors were expressed in 8 yeast strains with different humanized G proteins, covering the four major classes: Gαi, Gαs, Gαq, and Gα12. Our experiments showed that a tyrosine residue (Y) at the C-terminus of the Gα subunit plays an important role in controlling the activation of GPCRs. Receptor residues R103(3.50) and I107(3.54) are vital too in G protein-coupling and the activation of the hA2BR, whereas L213(IL3) is more important in G protein inactivation. Substitution of S235(6.36) to alanine provided the most divergent G protein-coupling profile. Finally, L236(6.37) substitution decreased receptor activation in all G protein pathways, although to a different extent. In conclusion, our findings shed light on the selectivity of receptor/G protein coupling, which may help in further understanding GPCR signaling.

A significant challenge and potential high-value application of computer-aided drug design is the accurate prediction of protein–ligand binding affinities. Free energy perturbation (FEP) using molecular dynamics (MD) sampling is among the most suitable approaches to achieve accurate binding free energy predictions, due to the rigorous statistical framework of the methodology, correct representation of the energetics, and thorough treatment of the important degrees of freedom in the system (including explicit waters). Recent advances in sampling methods and force fields coupled with vast increases in computational resources have made FEP a viable technology to drive hit-to-lead and lead optimization, allowing for more efficient cycles of medicinal chemistry and the possibility to explore much larger chemical spaces. However, previous FEP applications have focused on systems with high-resolution crystal structures of the target as starting points—something that is not always available in drug discovery projects. As such, the ability to apply FEP on homology models would greatly expand the domain of applicability of FEP in drug discovery. In this work we apply a particular implementation of FEP, called FEP+, on congeneric ligand series binding to four diverse targets: a kinase (Tyk2), an epigenetic bromodomain (BRD4), a transmembrane GPCR (A2A), and a protein–protein interaction interface (BCL-2 family protein MCL-1). We apply FEP+ using both crystal structures and homology models as starting points and find that the performance using homology models is generally on a par with the results when using crystal structures. The robustness of the calculations to structural variations in the input models can likely be attributed to the conformational sampling in the molecular dynamics simulations, which allows the modeled receptor to adapt to the “real” conformation for each ligand in the series. This work exemplifies the advantages of using all-atom simulation methods with full system flexibility and offers promise for the general application of FEP to homology models, although additional validation studies should be performed to further understand the limitations of the method and the scenarios where FEP will work best.

Cardiotoxicity is a side effect that plagues modern drug design and is very often due to the off-target blockade of the human ether-à-go-go related gene (hERG) potassium channel. To better understand the structural determinants of this blockade, we designed and synthesized a series of 40 derivatives of clofilium, a class III antiarrhythmic agent. These were evaluated in radioligand binding and patch-clamp assays to establish structure-affinity relationships (SAR) for this potassium channel. Efforts were especially focused on studying the influence of the structural rigidity and the nature of the linkers composing the clofilium scaffold. It was shown that introducing triple bonds and oxygen atoms in the n-butyl linker of the molecule greatly reduced affinity without significantly modifying the pKa of the essential basic nitrogen. These findings could prove useful in the first stages of drug discovery as a systematic way of reducing the risk of hERG K(+) channel blockade-induced cardiotoxicity.

The structure of the human A2A adenosine receptor has been elucidated by X-ray crystallography with a high affinity non-xanthine antagonist, ZM241385, bound to it. This template molecule served as a starting point for the incorporation of reactive moieties that cause the ligand to covalently bind to the receptor. In particular, we incorporated a fluorosulfonyl moiety onto ZM241385, which yielded LUF7445 (4-((3-((7-amino-2-(furan-2-yl)-[1, 2, 4]triazolo[1,5-a][1, 3, 5]triazin-5-yl)amino)propyl)carbamoyl)benzene sulfonyl fluoride). In a radioligand binding assay, LUF7445 acted as a potent antagonist, with an apparent affinity for the hA2A receptor in the nanomolar range. Its apparent affinity increased with longer incubation time, suggesting an increasing level of covalent binding over time. An in silico A2A-structure-based docking model was used to study the binding mode of LUF7445. This led us to perform site-directed mutagenesis of the A2A receptor to probe and validate the target lysine amino acid K153 for covalent binding. Meanwhile, a functional assay combined with wash-out experiments was set up to investigate the efficacy of covalent binding of LUF7445. All these experiments led us to conclude LUF7445 is a valuable molecular tool for further investigating covalent interactions at this receptor. It may also serve as a prototype for a therapeutic approach in which a covalent antagonist may be needed to counteract prolonged and persistent presence of the endogenous ligand adenosine. Electronic supplementary material The online version of this article (doi:10.1007/s11302-016-9549-9) contains supplementary material, which is available to authorized users.

We report the synthesis and biological evaluation of new 2-amino-4,5-diarylpyrimidines as selective antagonists at the adenosine A

The adhesion G protein-coupled receptors [ADGRs/class B2 G protein-coupled receptors (GPCRs)] constitute an ancient family of GPCRs that have recently been demonstrated to play important roles in cellular and developmental processes. Here, we describe a first insight into the structure-function relationship of ADGRs using the family member ADGR subfamily G member 4 (ADGRG4)/GPR112 as a model receptor. In a bioinformatics approach, we compared conserved, functional elements of the well-characterized class A and class B1 secretin-like GPCRs with the ADGRs. We identified several potential equivalent motifs and subjected those to mutational analysis. The importance of the mutated residues was evaluated by examining their effect on the high constitutive activity of the N-terminally truncated ADGRG4/GPR112 in a 1-receptor-1-G protein Saccharomyces cerevisiae screening system and was further confirmed in a transfected mammalian human embryonic kidney 293 cell line. We evaluated the results in light of the crystal structures of the class A adenosine A2A receptor and the class B1 corticotropin-releasing factor receptor 1. ADGRG4 proved to have functionally important motifs resembling class A, class B, and combined elements, but also a unique highly conserved ADGR motif (H3.33). Given the high conservation of these motifs and residues across the adhesion GPCR family, it can be assumed that these are general elements of ADGR function.-Peeters, M. C., Mos, I., Lenselink, E. B., Lucchesi, M., IJzerman, A. P., Schwartz, T. W. Getting from A to B-exploring the activation motifs of the class B adhesion G protein-coupled receptor subfamily G member 4/GPR112.

Proteochemometrics (PCM) is a predictive bioactivity modelling method to simultaneously model the bioactivity of multiple ligands against multiple targets. Therefore, PCM permits to explore the selectivity and promiscuity of ligands on biomolecular systems of different complexity, such proteins or even cell-line models. In practice, each ligand-target interaction is encoded by the concatenation of ligand and target descriptors. These descriptors are then used to train a single machine learning model. This simultaneous inclusion of both chemical and target information enables the extra- and interpolation to predict the bioactivity of compounds on targets, which can be not present in the training set. In this thesis, a methodological advance in the field is firstly introduced, namely how Bayesian inference (Gaussian Processes) can be successfully applied in the context of PCM for (i) the prediction of compounds bioactivity along with the error estimation of the prediction; (ii) the determination of the applicability domain of a PCM model; and (iii) the inclusion of experimental uncertainty of the bioactivity measurements. Additionally, the influence of noise in bioactivity models is benchmarked across a panel of 12 machine learning algorithms, showing that the noise in the input data has a marked and different influence on the predictive power of the considered algorithms. Subsequently, two R packages are presented. The first one, Chemically Aware Model Builder (camb), constitues an open source platform for the generation of predictive bioactivity models. The functionalities of camb include : (i) normalized chemical structure representation, (ii) calculation of 905 one- and two-dimensional physicochemical descriptors, and of 14 fingerprints for small molecules, (iii) 8 types of amino acid descriptors, (iv) 13 whole protein sequence descriptors, and (iv) training, validation and visualization of predictive models. The second package, conformal, permits the calculation of confidence intervals for individual predictions in the case of regression, and P values for classification settings. The usefulness of PCM to concomitantly optimize compounds selectivity and potency is subsequently illustrated in the context of two application scenarios, which are: (a) modelling isoform-selective cyclooxygenase inhibition; and (b) large-scale cancer cell-line drug sensitivity prediction, where the predictive signal of several cell-line profiling data is benchmarked (among others): basal gene expression, gene copy-number variation, exome sequencing, and protein abundance data. Overall, the application of PCM in these two case scenarios let us conclude that PCM is a suitable technique to model the activity of ligands exhibiting uncorrelated bioactivity profiles across a panel of targets, which can range from protein binding sites (a), to cancer cell-lines (b).

The chemokine receptor CCR2 is a G protein-coupled receptor that is involved in many diseases characterized by chronic inflammation, and therefore a large variety of CCR2 small molecule antagonists has been developed. On the basis of their chemical structures these antagonists can roughly be divided into two groups with most likely two topographically distinct binding sites. The aim of the current study was to identify the binding site of one such group of ligands, exemplified by three allosteric antagonists, CCR2-RA-[R], JNJ-27141491, and SD-24. We first used a chimeric CCR2/CCR5 receptor approach to obtain insight into the binding site of the allosteric antagonists and additionally introduced eight single point mutations in CCR2 to further characterize the putative binding pocket. All constructs were studied in radioligand binding and/or functional IP turnover assays, providing evidence for an intracellular binding site for CCR2-RA-[R], JNJ-27141491, and SD-24. For CCR2-RA-[R] the most important residues for binding were found to be the highly conserved tyrosine Y(7.53) and phenylalanine F(8.50) of the NPxxYx(5,6)F motif, as well as V(6.36) at the bottom of TM-VI and K(8.49) in helix-VIII. These findings demonstrate for the first time the presence of an allosteric intracellular binding site for CCR2 antagonists. This contributes to an increased understanding of the interactions of diverse ligands at CCR2 and may allow for a more rational design of future allosteric antagonists.

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.