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The hepatic bile acid transporter Na+/taurocholate co-transporting polypeptide (NTCP) was identified in 2012 as the high-affinity hepatic receptor for the hepatitis B and D viruses (HBV/HDV). Since then, this carrier has emerged as promising drug target for HBV/HDV virus entry inhibitors, but the synthetic peptide Hepcludex® of high molecular weight is the only approved HDV entry inhibitor so far. The present study aimed to identify small molecules as novel NTCP inhibitors with anti-viral activity. A ligand-based bioinformatic approach was used to generate and validate appropriate pharmacophore and QSAR (quantitative structure-activity relationship) models. Half-maximal inhibitory concentrations (IC50) for binding inhibition of the HBV/HDV-derived preS1 peptide (as surrogate parameter for virus binding to NTCP) were determined in NTCP-expressing HEK293 cells for 150 compounds of different chemical classes. IC50 values ranged from 2 µM up to >1000 µM. The generated pharmacophore and QSAR models were used for virtual screening of drug-like chemicals from the ZINC15 database (~11 million compounds). The 20 best-performing compounds were then experimentally tested for preS1-peptide binding inhibition in NTCP-HEK293 cells. Among them, four compounds were active and revealed experimental IC50 values for preS1-peptide binding inhibition of 9, 19, 20, and 35 µM, which were comparable to the QSAR-based predictions. All these compounds also significantly inhibited in vitro HDV infection of NTCP-HepG2 cells, without showing any cytotoxicity. The best-performing compound in all assays was ZINC000253533654. In conclusion, the present study demonstrates that virtual compound screening based on NTCP-specific pharmacophore and QSAR models can predict novel active hit compounds for the development of HBV/HDV entry inhibitors.

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Identification of Na+/taurocholate co-transporting polypeptide (NTCP) as high-affinity hepatic entry receptor for the Hepatitis B and D viruses (HBV/HDV) opened the field for target-based development of cell-entry inhibitors. However, most of the HBV/HDV entry inhibitors identified so far also interfere with the physiological bile acid transporter function of NTCP. The present study aimed to identify more virus-selective inhibitors of NTCP by screening of 87 propanolamine derivatives from the former development of intestinal bile acid reabsorption inhibitors (BARIs), which interact with the NTCP-homologous intestinal apical sodium-dependent bile acid transporter (ASBT). In NTCP-HEK293 cells, the ability of these compounds to block the HBV/HDV-derived preS1-peptide binding to NTCP (virus receptor function) as well as the taurocholic acid transport via NTCP (bile acid transporter function) were analyzed in parallel. Hits were subsequently validated by performing in vitro HDV infection experiments in NTCP-HepG2 cells. The most potent compounds S985852, A000295231, and S973509 showed in vitro anti-HDV activities with IC50 values of 15, 40, and 70 µM, respectively, while the taurocholic acid uptake inhibition occurred at much higher IC50 values of 24, 780, and 490 µM, respectively. In conclusion, repurposing of compounds from the BARI class as novel HBV/HDV entry inhibitors seems possible and even enables certain virus selectivity based on structure-activity relationships.

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Artificial Intelligence (AI) plays a pivotal role in drug discovery. In particular artificial neural networks such as deep neural networks or recurrent networks drive this area. Numerous applications in property or activity predictions like physicochemical and ADMET properties have recently appeared and underpin the strength of this technology in quantitative structure-property relationships (QSPR) or quantitative structure-activity relationships (QSAR). Artificial intelligence in de novo design drives the generation of meaningful new biologically active molecules towards desired properties. Several examples establish the strength of artificial intelligence in this field. Combination with synthesis planning and ease of synthesis is feasible and more and more automated drug discovery by computers is expected in the near future.

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We introduce a computationally efficient approximation of vibrational entropy changes (ΔSvib) upon binding to biomolecules based on rigidity theory. From constraint network representations of the binding partners, ΔSvib is estimated from changes in the number of low frequency ("spongy") modes with respect to changes in the networks' coordination number. Compared to ΔSvib computed by normal-mode analysis (NMA), our approach yields significant and good to fair correlations for data sets of protein-protein and protein-ligand complexes. Our approach could be a valuable alternative to NMA-based ΔSvib computation in end-point (free) energy methods.

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Pushing the frontiers of medicinal chemistry: Christa Müller, Dominik Thimm, and Karl-Heinz Baringhaus look back at the events of the 2016 Frontiers in Medicinal Chemistry (FiMC) Conference held in Bonn, Germany. The report highlights the themes & talks in the annual conference hosted by the Joint Division of Medicinal Chemistry of the German Pharmaceutical Society (DPhG) and German Chemical Society (GDCh). It is also an invitation to the 2017 conference in Bern, Switzerland this February 12-15.

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A major uncertainty in binding free energy estimates for protein-ligand complexes by methods such as MM-PB(GB)SA or docking scores results from neglecting or approximating changes in the configurational entropies (ΔSconfig.) of the solutes. In MM/PB(GB)SA-type calculations, ΔSconfig. has usually been estimated in the rigid rotor, harmonic oscillator approximation. Here, we present the development of a computationally efficient method (termed BEERT) to approximate ΔSconfig. in terms of the reduction in translational and rotational freedom of the ligand upon protein-ligand binding (ΔSR/T), starting from the flexible molecule approach. We test the method successfully in binding affinity computations in connection with MM-PBSA effective energies describing changes in gas-phase interactions and solvation free energies. Compared to related work by Ruvinsky and co-workers, clustering bound ligand poses based on interactions with the protein rather than structural similarity of the poses, and an appropriate averaging over single entropies associated with an individual well of the energy landscape of the protein-ligand complex, were found to be crucial. Employing three data sets of protein-ligand complexes of pharmacologically relevant targets for validation, with up to 20, in part related ligands per data set, spanning binding free energies over a range of ≤7 kcal mol-1, reliable and predictive linear models to estimate binding affinities are obtained in all three cases (R2 = 0.54-0.72, p < 0.001, root mean squared error S = 0.78-1.44 kcal mol-1; q2 = 0.34-0.67, p < 0.05, root mean squared error sPRESS = 1.07-1.36 kcal mol-1). These models are markedly improved compared to considering MM-PBSA effective energies alone, scoring functions, and combinations with ΔSconfig. estimates based on the number of rotatable bonds, rigid rotor, harmonic oscillator approximation, or interaction entropy method. As a limitation, our method currently requires a target-specific training data set to identify appropriate scaling coefficients for the MM-PBSA effective energies and BEERT ΔSR/T. Still, our results suggest that the approach is a valuable, computationally more efficient complement to existing rigorous methods for estimating changes in binding free energy across structurally (weakly) related series of ligands binding to one target.

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The sodium-dependent organic anion transporter SOAT specifically transports sulfated steroid hormones and is supposed to play a role in testicular steroid regulation and male fertility. The present study aimed to identify novel specific SOAT inhibitors for further in vitro and in vivo studies on SOAT function. More than 100 compounds of different molecular structures were screened for inhibition of the SOAT-mediated transport of dehydroepiandrosterone sulfate in stably transfected SOAT-HEK293 cells. Twenty-five of these with IC50 values covering four orders of magnitude were selected as training set for 3D pharmacophore modelling. The SOAT pharmacophore features were calculated by CATALYST and consist of three hydrophobic sites and two hydrogen bond acceptors. By substrate database screening, compound T 0511-1698 was predicted as a novel SOAT inhibitor with an IC50 of 15 µM. This value was confirmed by cell-based transport assays. Therefore, the developed SOAT pharmacophore model demonstrated its suitability in predicting novel SOAT inhibitors.

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We present the discovery of low molecular weight inhibitors of human immunodeficiency virus 1 (HIV-1) protease subtype B that were identified by structure-based virtual screening as ligands of an allosteric surface cavity. For pocket identification and prioritization, we performed a molecular dynamics simulation and observed several flexible, partially transient surface cavities. For one of these presumable ligand-binding pockets that are located in the so-called "hinge region" of the identical protease chains, we computed a receptor-derived pharmacophore model, with which we retrieved fragment-like inhibitors from a screening compound pool. The most potent hit inhibited protease activity in vitro in a noncompetitive mode of action. Although attempts failed to crystallize this ligand bound to the enzyme, the study provides proof-of-concept for identifying innovative tool compounds for chemical biology by addressing flexible protein models with receptor pocket-derived pharmacophore screening.

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Quantifying the properties of macromolecules is a prerequisite for understanding their roles in biochemical processes. One of the less-explored geometric features of macromolecules is molecular surface irregularity, or 'roughness', which can be measured in terms of fractal dimension (D). In this study, we demonstrate that surface roughness correlates with ligand binding potential. We quantified the surface roughnesses of biological macromolecules in a large-scale survey that revealed D values between 2.0 and 2.4. The results of our study imply that surface patches involved in molecular interactions, such as ligand-binding pockets and protein-protein interfaces, exhibit greater local fluctuations in their fractal dimensions than 'inert' surface areas. We expect approximately 22 % of a protein's surface outside of the crystallographically known ligand binding sites to be ligandable. These findings provide a fresh perspective on macromolecular structure and have considerable implications for drug design as well as chemical and systems biology.

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The pregnane X receptor (PXR), a member of the nuclear hormone superfamily, regulates the expression of several enzymes and transporters involved in metabolically relevant processes. The significant induction of CYP450 enzymes by PXR, in particular CYP3A4, might significantly alter the metabolism of prescribed drugs. In order to early identify molecules in drug discovery with a potential to activate PXR as antitarget, we developed fast and reliable in silico filters by ligand-based QSAR techniques. Two classification models were established on a diverse dataset of 434 drug-like molecules. A second augmented set allowed focusing on interesting regions in chemical space. These classifiers are based on decision trees combined with a genetic algorithm based variable selection to arrive at predictive models. The classifier for the first dataset on 29 descriptors showed good performance on a test set with a correct classification of both 100% for PXR activators and non-activators plus 87% for activators and 83% for non-activators in an external dataset. The second classifier then correctly predicts 97% activators and 91% non-activators in a test set and 94% for activators and 64% non-activators in an external set of 50 molecules, which still qualifies for application as a filter focusing on PXR activators. Finally a quantitative model for PXR activation for a subset of these molecules was derived using a regression-tree approach combined with GA variable selection. This final model shows a predictive r(2) of 0.774 for the test set and 0.452 for an external set of 33 molecules. Thus, the combination of these filters consistently provide guidelines for lowering PXR activation in novel candidate molecules.

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Protein-protein interfaces are considered difficult targets for small-molecule protein-protein interaction modulators (PPIMs ). Here, we present for the first time a computational strategy that simultaneously considers aspects of energetics and plasticity in the context of PPIM binding to a protein interface. The strategy aims at identifying the determinants of small-molecule binding, hot spots, and transient pockets, in a protein-protein interface in order to make use of this knowledge for predicting binding modes of and ranking PPIMs with respect to their affinity. When applied to interleukin-2 (IL-2), the computationally inexpensive constrained geometric simulation method FRODA outperforms molecular dynamics simulations in sampling hydrophobic transient pockets. We introduce the PPIAnalyzer approach for identifying transient pockets on the basis of geometrical criteria only. A sequence of docking to identified transient pockets, starting structure selection based on hot spot information, RMSD clustering and intermolecular docking energies, and MM-PBSA calculations allows one to enrich IL-2 PPIMs from a set of decoys and to discriminate between subgroups of IL-2 PPIMs with low and high affinity. Our strategy will be applicable in a prospective manner where nothing else than a protein-protein complex structure is known; hence, it can well be the first step in a structure-based endeavor to identify PPIMs.

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The optimization of a lead structure to a development candidate often requires removal of undesirable antitarget activities. To this end, we have developed an approach to extract antitarget activity hotspots from larger databases and to transfer this knowledge onto novel chemical series. These antitarget activity hotspots will be captured as pairs of informative molecules, which are chemically closely related, but differ significantly in biological activity. We illustrate the application of antitarget activity hotspots as informative compound pairs for the optimization of side effects in lead structures for relevant antitargets in pharmaceutical research. The use for prospective design requires establishing a structural link between known antitarget hotspot pairs and a new lead structure: we employ 3D-based similarity comparison for this task. The entire workflow serves as idea generator in early optimization. The feasibility of this approach is demonstrated in several optimization problems related to hERG inhibition, and CYP3A4 inhibition. Several structural examples demonstrate the ability of the 3D-shape searching to identify related scaffolds and the usefulness of the antitarget hotspot information to guide optimization by modulating the undesirable antitarget activity. Such a concept based on the analysis of local similarities and the transfer to 3D-related series is especially promising in those cases, where the construction of antitarget QSAR models fails to detect local SAR trends for guiding the next optimization cycle.

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The identification of new lead structures is a pivotal task in early drug discovery. Molecular de novo design of ligand structures has been successfully applied in various drug discovery projects. Still, the question of the scaffold hopping potential of drug design by adaptive evolutionary optimization has been left unanswered. It was unclear whether de novo design is actually able to leap away from given chemotypes ("activity islands"), allowing for rescaffolding of compounds. We have addressed these questions by scrutinizing different scoring functions of our de novo design software Flux for their ability to enable scaffold-hops for various target classes. We evaluated both the potential bioactivity and the scaffold diversity of de novo generated structures. For several target classes, known lead structures were reconstructed by the de novo algorithm ("lead-hopping"). We demonstrate that for one or multiple templates of a given chemotype, other chemotypes are reached during de novo compound generation, thus indicating successful scaffold-hops.

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Natural products contain scaffold structures that can be systematically exploited for the design of combinatorial compound libraries with druglike properties. We review approaches for scaffold identification, and compare properties and pharmacophoric features of drugs and natural products. In particular, an application of the self-organizing map technique is presented for natural product-derived compound and library design.

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The fluorogenic substrate 6,8-difluoro-4-methylumbiliferyl phosphate (DIFMUP) has been widely used for the detection of serine and threonine phosphatase activities. Here we describe the use of this substrate for the characterization of protein tyrosine phosphatases (PTPs) and for the screening for PTP inhibitors. The measured kinetic and inhibitor constants for DIFMUP cleavage were comparable with those of the widely used but less discriminative and practicable substrates, para-nitrophenylphosphate and phosphotyrosine-containing peptides, respectively. Furthermore, the continuous and highly sensitive assay allows fast and accurate investigations of the type, kinetic behavior, and binding mode of small-molecule inhibitors. We discuss the validation of this assay system for various PTPs and its use in inhibitor screening for PTP1B.

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Kv1.5 channels conduct the ultrarapid delayed rectifier current (IKur) that contributes to action potential repolarization of human atrial myocytes. Block of these channels has been proposed as a treatment for atrial arrhythmias. Here we report a novel and potent inhibitor of Kv1.5 potassium channels, N-benzyl-N-pyridin-3-yl-methyl-2-(toluene-4-sulfonylamino)-benzamide hydrochloride (S0100176), which exhibits features consistent with preferential block of the open state. The IC50 of S0100176 for Kv1.5 expressed in Xenopus oocytes was 0.7 microm. Ala-scanning mutagenesis within the pore helix and the S6 segment, regions that form the walls of the central cavity, was combined with voltage clamp analysis to identify point mutations that altered drug affinity. This approach identified Thr-479, Thr-480, Val-505, Ile-508, and Val-512 as the most important residues for block by S0100176. Mutations of these key residues to Ala or other amino acids caused marked changes in the IC50 of S0100176 (p<0.01). For example, the IC50 of S0100176 increased 362-fold for T480A, 26-fold for V505A, 150-fold for I508A, and 99-fold for V512A. We used modeling to dock S0100176 into the inner cavity of a Kv1.5 pore homology model that was generated based on the crystal structure of KcsA. The docking predicted that the five residues identified by the Ala scan were positioned less than 4.5 A from the compound. Based on the homology models, the positions of the five amino acids identified to interact with S0100176 face toward the central cavity and overlap with putative binding sites for other blockers and voltage-gated potassium channels.

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Similarity searching allows a fast identification of analogues to biologically active molecules. Depending on the applied similarity metrics, either structurally close analogues or more diverse compounds can be identified. This is of particular interest for the analysis of high-throughput screening (HTS) hits. A combination of similarity searching and data mining applied to HTS data derives early structure-activity relationships to guide a subsequent optimization of hits.: