Oxidative stress is the consequence of an abnormal increase of reactive oxygen species (ROS). ROS are generated mainly during the metabolism in both normal and pathological conditions as well as... Show moreOxidative stress is the consequence of an abnormal increase of reactive oxygen species (ROS). ROS are generated mainly during the metabolism in both normal and pathological conditions as well as from exposure to xenobiotics. Xenobiotics can, on the one hand, disrupt molecular machinery involved in redox processes and, on the other hand, reduce the effectiveness of the antioxidant activity. Such dysregulation may lead to oxidative damage when combined with oxidative stress overpassing the cell capacity to detoxify ROS. In this work, a green fluorescent protein (GFP)-tagged nuclear factor erythroid 2-related factor 2 (NRF2)-regulated sulfiredoxin reporter (Srxn1-GFP) was used to measure the antioxidant response of HepG2 cells to a large series of drug and drug-like compounds (2230 compounds). These compounds were then classified as positive or negative depending on cellular response and distributed among different modeling groups to establish structure-activity relationship (SAR) models. A selection of models was used to prospectively predict oxidative stress induced by a new set of compounds subsequently experimentally tested to validate the model predictions. Altogether, this exercise exemplifies the different challenges of developing SAR models of a phenotypic cellular readout, model combination, chemical space selection, and results interpretation. Show less
Protein kinases are a protein family that plays an important role in several complex diseases such as cancer and cardiovascular and immunological diseases. Protein kinases have conserved ATP... Show moreProtein kinases are a protein family that plays an important role in several complex diseases such as cancer and cardiovascular and immunological diseases. Protein kinases have conserved ATP binding sites, which when targeted can lead to similar activities of inhibitors against different kinases. This can be exploited to create multitarget drugs. On the other hand, selectivity (lack of similar activities) is desirable in order to avoid toxicity issues. There is a vast amount of protein kinase activity data in the public domain, which can be used in many different ways. Multitask machine learning models are expected to excel for these kinds of data sets because they can learn from implicit correlations between tasks (in this case activities against a variety of kinases). However, multitask modeling of sparse data poses two major challenges: (i) creating a balanced train-test split without data leakage and (ii) handling missing data. In this work, we construct a protein kinase benchmark set composed of two balanced splits without data leakage, using random and dissimilarity-driven cluster-based mechanisms, respectively. This data set can be used for benchmarking and developing protein kinase activity prediction models. Overall, the performance on the dissimilarity-driven cluster-based split is lower than on random split-based sets for all models, indicating poor generalizability of models. Nevertheless, we show that multitask deep learning models, on this very sparse data set, outperform single-task deep learning and tree-based models. Finally, we demonstrate that data imputation does not improve the performance of (multitask) models on this benchmark set. Show less
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... Show moreCC 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. Show less
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... Show moreThe 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 A3 receptor (hA3AR). 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 [35S]GTPγS binding assays, then validated 17b as the covalent antagonist. A combined in silico hA3AR-homology model and site-directed mutagenesis study was performed to demonstrate that amino acid residue Y2657.36 was the unique anchor point of the covalent interaction. This workflow might be applied to other GPCRs to guide the discovery of covalent ligands. Show less
Janssen, A.P.A.; Grimm, S.H.; Wijdeven, R.H.M.; Lenselink, E.B.; Neefjes, J.; Boeckel, C.A.A. van; ... ; Stelt, M. van der 2019
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... Show moreAcute 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. Show less
Janssen, A.P.A.; Grimm, S.H.; Wijdeven, R.H.M.; Lenselink, E.B.; Neefjes, J.J.C.; Boeckel, C.A.A. van; ... ; Stelt, M. van der 2019
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 ... Show moreThe 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. Show less
Janssen, A.P.A.; Grimm, S.H.; Wijdeven, R.H.M.; Lenselink, E.B.; Neefjes, J.J.C.; Boeckel, C.A.A. van; ... ; Stelt, M. van der 2019
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 ... Show moreThe 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. Show less
Janssen, A.P.A.; Grimm, S.H.; Wijdeven, R.H.M.; Lenselink, E.B.; Neefjes, J.J.C.; Boeckel, C.A.A. van; ... ; Stelt, M. van der 2018
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 ... Show moreThe 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. Show less
Zacarias, N.V.O.; Veldhoven, J.P.D. van; Portner, L.; Spronsen, E. van; Ullo, S; Veenhuizen, M.; ... ; IJzerman, A.P. 2018
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... Show moreThe 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. Show less
In this work, a problem of selecting a subset of molecules, which are potential lead candidates for drug discovery, is considered. Such molecule subset selection problem is formulated as a... Show moreIn this work, a problem of selecting a subset of molecules, which are potential lead candidates for drug discovery, is considered. Such molecule subset selection problem is formulated as a portfolio optimization, well known and studied in financial management. The financial return, more precisely the return rate, is interpreted as return rate from a potential lead and calculated as a product of gain and probability of success (probability that a selected molecule becomes a lead), which is related to performance of the molecule, in particular, its (bio-)activity. The risk is associated with not finding active molecules and is related to the level of diversity of the molecules selected in portfolio. It is due to potential of some molecules to contribute to the diversity of the set of molecules selected in portfolio and hence decreasing risk of portfolio as a whole. Even though such molecules considered in isolation look inefficient, they are located in sparsely sampled regions of chemical space and are different from more promising molecules. One way of computing diversity of a set is associated with a covariance matrix, and here it is represented by the Solow-Polasky measure. Several formulations of molecule portfolio optimization are considered taking into account the limited budget provided for buying molecules and the fixed size of the portfolio. The proposed approach is tested in experimental settings for three molecules datasets using exact and/or evolutionary approaches. The results obtained for these datasets look promising and encouraging for application of the proposed portfolio-based approach for molecule subset selection in real settings.Graphical abstract Show less
While equilibrium binding affinities and in vitro functional antagonism of CB1 receptor antagonists have been studied in detail, little is known on the kinetics of their receptor interaction. In... Show moreWhile equilibrium binding affinities and in vitro functional antagonism of CB1 receptor antagonists have been studied in detail, little is known on the kinetics of their receptor interaction. In this study, we therefore conducted kinetic assays for nine 1-(4,5-diarylthiophene-2-carbonyl)-4-phenylpiperidine-4-carboxamide derivatives and included the CB1 antagonist rimonabant as a comparison. For this we newly developed a dual-point competition association assay with [3H]CP55940 as the radioligand. This assay yielded Kinetic Rate Index (KRI) values from which structure-kinetics relationships (SKR) of hCB1 receptor antagonists could be established. The fast dissociating antagonist 6 had a similar receptor residence time (RT) as rimonabant, i.e. 19 and 14 min, respectively, while the slowest dissociating antagonist (9) had a very long RT of 2222 min, i.e. pseudo-irreversible dissociation kinetics. In functional assays, 9 displayed insurmountable antagonism, while the effects of the shortest RT antagonist 6 and rimonabant were surmountable. Taken together, this study shows that hCB1 receptor antagonists can have very divergent RTs, which are not correlated to their equilibrium affinities. Furthermore, their RTs appear to define their mode of functional antagonism, i.e. surmountable vs. insurmountable. Finally, based on the recently resolved hCB1 receptor crystal structure, we propose that the differences in RT can be explained by a different binding mode of antagonist 9 from short RT antagonists that is able to displace unfavorable water molecules. Taken together, these findings are of importance for future design and evaluation of potent and safe hCB1 receptor antagonists. Show less
Recent crystal structures of multiple G protein-coupled receptors (GPCRs) have revealed a highly conserved intracellular pocket that can be used to modulate these receptors from the inside. This... Show moreRecent crystal structures of multiple G protein-coupled receptors (GPCRs) have revealed a highly conserved intracellular pocket that can be used to modulate these receptors from the inside. This novel intracellular site partially overlaps with the G protein and β-arrestin binding site, providing a new manner of pharmacological intervention. Here we provide an update of the architecture and function of the intracellular region of GPCRs, until now portrayed as the signaling domain. We review the available evidence on the presence of intracellular binding sites among chemokine receptors and other class A GPCRs, as well as different strategies to target it, including small molecules, pepducins, and nanobodies. Finally, the potential advantages of intracellular (allosteric) ligands over orthosteric ligands are also discussed. Show less
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 ... Show moreThe 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. Show less
Janssen, A.P.A.; Grimm, S.H.; Wijdeven, R.H.M.; Lenselink, E.B.; Neefjes, J.; Boeckel, C.A.A. van; ... ; Stelt, M. van der 2018
AbstractWe expanded on a series of pyrido[2,1-f]purine-2,4-dione derivatives as human adenosine A3 receptor (hA3R) antagonists to determine their kinetic profiles and affinities. Many compounds... Show moreAbstractWe expanded on a series of pyrido[2,1-f]purine-2,4-dione derivatives as human adenosine A3 receptor (hA3R) antagonists to determine their kinetic profiles and affinities. Many compounds showed high affinities and a diverse range of kinetic profiles. We found hA3R antagonists with very short residence time (RT) at the receptor (2.2 min for 5) and much longer RTs (e.g., 376 min for 27 or 391 min for 31). Two representative antagonists (5 and 27) were tested in [35S]GTPγS binding assays, and their RTs appeared correlated to their (in)surmountable antagonism. From a kon-koff-KD kinetic map, we divided the antagonists into three subgroups, providing a possible direction for the further development of hA3R antagonists. Additionally, we performed a computational modeling study that sheds light on the crucial receptor interactions, dictating the compounds' binding kinetics. Knowledge of target binding kinetics appears useful for developing and triaging new hA3R antagonists in the early phase of drug discovery. Show less
