Monovalent cation binding to model systems and the macrocyclic depsipeptide, emodepside.

This study focuses on the systematic exploration of the emodepside conformations bound to monovalent K+ ion using quantum mechanical density functional theory (DFT) calculations at the M06-2X/6-31+G(d,p) level of theory. Nine conformers of emodepside and their complexes with K+ ion were characterized as stationary points on the potential energy surface. The conformational isomers were examined for their 3D structures, bonding, energetics, and interactions with the cation. A cavitand-like structure (CC) is identified to be the energetically most stable arrangement. To arrive at a better understanding of the K+ ion binding, calculations were initially performed on complexes formed by the K+ and Na+ ions with model ligands (methyl ester and N,N-dimethyl acetamide). Both the natural bond orbital (NBO) method and the block-localized wavefunction (BLW) energy decomposition approach was employed to assess the bonding and energetic contributions stabilizing the ion-bound model complexes. Finally, the solvent effect was evaluated through complete geometry optimizations and energy minimizations for the model ion-ligand complexes and the emodepside-K+ bound complexes using an implicit solvent model mimicking water and DMSO.

In Pursuit of an Allosteric Human Tropomyosin Kinase A (hTrkA) Inhibitor for Chronic Pain.

Human ß-nerve growth factor (ß-NGF) and its associated receptor, human tropomyosin receptor kinase A (hTrkA), have been demonstrated to be key factors in the perception of pain. However, efficacious small molecule therapies targeting the intracellularly located hTrkA kinase have not been explored thoroughly for pain management. Herein, we report the pharmacological properties of a selective hTrkA allosteric inhibitor, 1. 1 was shown to be active against the full length hTrkA, showing preferential binding for the inactive kinase, and was confirmed through the X-ray of hTrkA···1 bound complex. 1 was also found to inhibit ß-NGF induced neurite outgrowth in rat PC12 cells. Daily oral administration of 1 improved the joint compression threshold of rats injected intra-articularly with monoiodoacetate over a 14-day period. The efficacy of 1 in a relevant chronic pain model of osteoarthritis coupled with in vitro confirmation of target mediation makes allosteric hTrkA inhibitors potential candidates for modulating pain.

Connecting the conformational behavior of cyclic octadepsipeptides with their ionophoric property and membrane permeability.

Cyclic octadepsipeptides such as PF1022A and its synthetic derivative emodepside exhibit anthelmintic activity with the latter sold as a commercial drug treatment against gastrointestinal nematodes for animal health use. The structure-permeability relationship of these cyclic depsipeptides that could ultimately provide insights into the compound bioavailability is not yet well understood. The fully N-methylated amide backbone and apolar sidechain residues do not allow for the formation of intramolecular hydrogen bonds, normally observed in the membrane-permeable conformations of cyclic peptides. Hence, any understanding gained on these depsipeptides would serve as a prototype for future design strategies. In previous nuclear magnetic resonance (NMR) studies, two macrocyclic core conformers of emodepside were detected, one with all backbone amides in trans-configuration (hereon referred as the symmetric conformer) and the other with one amide in cis-configuration (hereon referred as the asymmetric conformer). In addition, these depsipeptides were also reported to be ionophores with a preference of potassium over sodium. In this study, we relate the conformational behavior of PF1022A, emodepside, and closely related analogs with their ionophoric characteristic probed using NMR and molecular dynamics (MD) simulations and finally evaluated their passive membrane permeability using PAMPA. We find that the equilibrium between the two core conformers shifts more towards the symmetric conformer upon addition of monovalent cations with selectivity for potassium over sodium. Both the NMR experiments and the theoretical Markov state models based on extensive MD simulations indicate a more rigid backbone for the asymmetric conformation, whereas the symmetric conformation shows greater flexibility. The experimental results further advocate for the symmetric conformation binding the cation. The PAMPA results suggest that the investigated depsipeptides are retained in the membrane, which may be advantageous for the likely target, a membrane-bound potassium channel.

Synthetic inhibitor leads of human tropomyosin receptor kinase A (hTrkA).

In silico virtual screening followed by in vitro biochemical, biophysical, and cellular screening resulted in the identification of distinctly different hTrkA kinase domain inhibitor scaffolds. X-ray structural analysis of representative inhibitors bound to hTrkA kinase domain defined the binding mode and rationalized the mechanism of action. Preliminary assessment of the sub-type selectivity against the closest hTrkB isoform, and early ADME guided the progression of select inhibitor leads in the screening cascade. The possibility of the actives sustaining to known hTrkA resistance mutations assessed in silico offers initial guidance into the required multiparametric lead optimization to arrive at a clinical candidate.

Lead identification and characterization of hTrkA type 2 inhibitors.

Virtual in silico structure-guided modeling, followed by in vitro biochemical screening of a subset of commercially purchasable compound collection resulted in the identification of several human tropomyosin receptor kinase A (hTrkA) inhibitors that bind the orthosteric ATP site and exhibit binding preference for the inactive kinase conformation. The type 2 binding mode with the DFG-out and αC-helix out hTrkA kinase domain conformation was confirmed from X-ray crystallographic solution of a representative inhibitor analog, 1b. Additional hTrkA and hTrkB (selectivity) assays in recombinant cells, neurite outgrowth inhibition using rat PC12 cells, early ADME profiling, and preliminary pharmacokinetic evaluation in rodents guided the lead inhibitor progression in the discovery screening funnel.

Type 2 inhibitor leads of human tropomyosin receptor kinase (hTrkA).

In silico virtual screening using the ligand-based ROCS approach and the commercially purchasable compound collection from the ZINC database resulted in the identification of distinctly different and novel acetamide core frameworks with series representatives 1a and 2a exhibiting nanomolar affinity in the kinase domain only hTrkA HTRF biochemical assay. Additional experimental validation using the Caliper technology with either the active or inactive kinase conditions demonstrated the leads, 1a and 2a, to preferentially bind the kinase inactive state. X-ray structural analysis of the kinase domain of hTrkA…1a/2a complexes confirmed the kinase, bind the inhibitor leads in the inactive state and to exhibit a type 2 binding mode with the DFG-out and αC-helix out conformation. The leads also demonstrated sub-micromolar activity in the full length hTrkA cell-based assay and selectivity against the closely related hTrkB isoform. However, the poor microsomal stability and permeability of the leads is suggestive of a multiparametric lead optimization effort requirement for further progression.

Deciphering the Allosteric Binding Mechanism of the Human Tropomyosin Receptor Kinase A ( hTrkA) Inhibitors.

Access to cryptic binding pockets or allosteric sites on a kinase that present themselves when the enzyme is in a specific conformational state offers a paradigm shift in designing the next generation small molecule kinase inhibitors. The current work showcases an extensive and exhaustive array of in vitro biochemical and biophysical tools and techniques deployed along with structural biology efforts of inhibitor-bound kinase complexes to characterize and confirm the cryptic allosteric binding pocket and docking mode of the small molecule actives identified for hTrkA. Specifically, assays were designed and implemented to lock the kinase in a predominantly active or inactive conformation and the effect of the kinase inhibitor probed to understand the hTrkA binding and hTrkB selectivity. The current outcome suggests that inhibitors with a fast association rate take advantage of the inactive protein conformation and lock the kinase state by also exhibiting a slow off-rate. This in turn shifts the inactive/active state protein conformational equilibrium cycle, affecting the subsequent downstream signaling.

In silico ligand-based modeling of hBACE-1 inhibitors.

Alzheimer's disease is a chronic neurodegenerative disease affecting more than 30 million people worldwide. Development of small molecule inhibitors of human ß-secretase 1 (hBACE-1) is being the focus of pharmaceutical industry for the past 15-20 years. Here, we successfully applied multiple ligand-based in silico modeling techniques to understand the inhibitory activities of a diverse set of small molecule hBACE-1 inhibitors reported in the scientific literature. Strikingly, the use of only a small subset of 230 (13%) molecules allowed us to develop quality models that performed reasonably well on the validation set of 1,476 (87%) inhibitors. Varying the descriptor sets and the complexity of the modeling techniques resulted in only minor improvements to the model's performance. The current results demonstrate that predictive models can be built by choosing appropriate modeling techniques in spite of using small datasets consisting of diverse chemical classes, a scenario typical in triaging of high-throughput screening results to identify false negatives. We hope that these encouraging results will help the community to develop more predictive models that would support research efforts for the debilitating Alzheimer's disease. Additionally, the integrated diversity of the techniques employed will stimulate scientists in the field to use in silico statistical modeling techniques like these to derive better models to help advance the drug discovery projects faster.

Computational Modeling of ß-Secretase 1 (BACE-1) Inhibitors Using Ligand Based Approaches.

The binding affinities (IC50) reported for diverse structural and chemical classes of human ß-secretase 1 (BACE-1) inhibitors in literature were modeled using multiple in silico ligand based modeling approaches and statistical techniques. The descriptor space encompasses simple binary molecular fingerprint, one- and two-dimensional constitutional, physicochemical, and topological descriptors, and sophisticated three-dimensional molecular fields that require appropriate structural alignments of varied chemical scaffolds in one universal chemical space. The affinities were modeled using qualitative classification or quantitative regression schemes involving linear, nonlinear, and deep neural network (DNN) machine-learning methods used in the scientific literature for quantitative-structure activity relationships (QSAR). In a departure from tradition, ∼20% of the chemically diverse data set (205 compounds) was used to train the model with the remaining ∼80% of the structural and chemical analogs used as part of an external validation (1273 compounds) and prospective test (69 compounds) sets respectively to ascertain the model performance. The machine-learning methods investigated herein performed well in both the qualitative classification (∼70% accuracy) and quantitative IC50 predictions (RMSE ∼ 1 log). The success of the 2D descriptor based machine learning approach when compared against the 3D field based technique pursued for hBACE-1 inhibitors provides a strong impetus for systematically applying such methods during the lead identification and optimization efforts for other protein families as well.

Structure-Guided Design of IACS-9571, a Selective High-Affinity Dual TRIM24-BRPF1 Bromodomain Inhibitor.

The bromodomain containing proteins TRIM24 (tripartite motif containing protein 24) and BRPF1 (bromodomain and PHD finger containing protein 1) are involved in the epigenetic regulation of gene expression and have been implicated in human cancer. Overexpression of TRIM24 correlates with poor patient prognosis, and BRPF1 is a scaffolding protein required for the assembly of histone acetyltransferase complexes, where the gene of MOZ (monocytic leukemia zinc finger protein) was first identified as a recurrent fusion partner in leukemia patients (8p11 chromosomal rearrangements). Here, we present the structure guided development of a series of N,N-dimethylbenzimidazolone bromodomain inhibitors through the iterative use of X-ray cocrystal structures. A unique binding mode enabled the design of a potent and selective inhibitor 8i (IACS-9571) with low nanomolar affinities for TRIM24 and BRPF1 (ITC Kd = 31 nM and ITC Kd = 14 nM, respectively). With its excellent cellular potency (EC50 = 50 nM) and favorable pharmacokinetic properties (F = 29%), 8i is a high-quality chemical probe for the evaluation of TRIM24 and/or BRPF1 bromodomain function in vitro and in vivo.

Comprehending renin inhibitor's binding affinity using structure-based approaches.

The performance of several structure-based design (SBD) approaches in predicting the binding affinity of diverse small molecule inhibitors co-crystallized to human renin was assessed to ascertain the modeling tool and method of choice required when dealing with structure-based lead optimization projects. Most of the SBD approaches investigated here were able to provide qualitative guidance, but quantitative accuracy as well as decisive discrimination between [in]actives is still not within reach. Such an outcome suggests that the current methods need improvement to capture the overall physics of the binding phenomenon for consistent applications in a lead optimization setting.

Computational modeling and design of renin inhibitors.

The recently introduced field-based QSAR was employed to develop robust quantitative 3D QSAR models to comprehend the activity of several structurally diverse classes of small molecule renin inhibitors reported in literature. A reasonable predictive model with an r(2) (pred) of ~0.67 and rmse of 0.79 was achieved for an external validation set of ~150 compounds centered on the model developed using ~450 training set compounds. Based on the developed 3D QSAR models and additional insights gained from reported X-ray structures, opportunity for activity improvements in the [aza]indole scaffold was explored using a carefully designed virtual library of ~2300 compounds. The potential for success of such combined structure-guided and ligand-based approach was justified when the resulting prediction was compared against a representative with supporting experimental results.

An integrated computational workflow for efficient and quantitative modeling of renin inhibitors.

A new integrated computational workflow that couples the strength of the molecular overlay methods to achieve rapid and automated alignments along with 3D-QSAR techniques like CoMFA and CoMSIA for quantitative binding affinity prediction is presented. The results obtained from such techniques are compared with rule-based Topomer CoMFA method, where possible. The developed 3D-QSAR models were prospectively used to predict the affinities of new compounds designed through R-group deconvolution starting from the core chemical scaffold and subsequent virtual combinatorial library enumeration. The general applicability of the seamless in silico modeling workflow is demonstrated using several datasets reported for small molecule inhibitors of renin.

Allosteric antagonist binding sites in class B GPCRs: corticotropin receptor 1.

The 41 amino acid neuropeptide, corticotropin-releasing factor (CRF) and its associated receptors CRF(1)-R and CRF(2)-R have been targeted for treating stress related disorders. Both CRF(1)-R and CRF(2)-R belong to the class B G-protein coupled receptors for which little information is known regarding the small molecule antagonist binding characteristics. However, it has been shown recently that different non-peptide allosteric ligands stabilize different receptor conformations for CRF(1)-R and hence an understanding of the ligand induced receptor conformational changes is important in the pharmacology of ligand binding. In this study, we modeled the receptor and identified the binding sites of representative small molecule allosteric antagonists for CRF(1)-R. The predicted binding sites of the investigated compounds are located within the transmembrane (TM) domain encompassing TM helices 3, 5 and 6. The docked compounds show strong interactions with H228 on TM3 and M305 on TM5 that have also been implicated in the binding by site directed mutation studies. H228 forms a hydrogen bond of varied strengths with all the antagonists in this study and this is in agreement with the decreased binding affinity of several compounds with H228F mutation. Also mutating M305 to Ile showed a sharp decrease in the calculated binding energy whereas the binding energy loss on M305 to Leu was less significant. These results are in qualitative agreement with the decrease in binding affinities observed experimentally. We further predicted the conformational changes in CRF(1)-R induced by the allosteric antagonist NBI-27914. Movement of TM helices 3 and 5 are dominant and generates three degenerate conformational states two of which are separated by an energy barrier from the third, when bound to NBI-27914. Binding of NBI-27914 was predicted to improve the interaction of the ligand with M305 and also enhanced the aromatic stacking between the ligand and F232 on TM3. A virtual ligand screening of ~13,000 compounds seeded with ~350 CRF(1)-R specific active antagonists performed on the NBI-27914 stabilized conformation of CRF(1)-R yielded a 44% increase in enrichment compared to the initially modeled receptor conformation at a 10% cutoff. The NBI-27914 stabilized conformation also shows a high enrichment for high affinity antagonists compared to the weaker ones. Thus, the conformational changes induced by NBI-27914 improved the ligand screening efficiency of the CRF(1)-R model and demonstrate a generalized application of the method in drug discovery.

Computational Modeling of Kinase Inhibitor Selectivity.

An exhaustive computational exercise on a comprehensive set of 15 therapeutic kinase inhibitors was undertaken to identify as to which compounds hit which kinase off-targets in the human kinome. Although the kinase selectivity propensity of each inhibitor against ∼480 kinase targets is predicted, we compared our predictions to ∼280 kinase targets for which consistent experimental data are available and demonstrate an overall average prediction accuracy and specificity of ∼90%. A comparison of the predictions was extended to an additional ∼60 kinases for sorafenib and sunitinib as new experimental data were reported recently with similar prediction accuracy. The successful predictive capabilities allowed us to propose predictions on the remaining kinome targets in an effort to repurpose known kinase inhibitors to these new kinase targets that could hold therapeutic potential.

Computational approaches for modeling human intestinal absorption and permeability.

Human intestinal absorption (HIA) is an important roadblock in the formulation of new drug substances. Computational models are needed for the rapid estimation of this property. The measurements are determined via in vivo experiments or in vitro permeability studies. We present several computational models that are able to predict the absorption of drugs by the human intestine and the permeability through human Caco-2 cells. The training and prediction sets were derived from literature sources and carefully examined to eliminate compounds that are actively transported. We compare our results to models derived by other methods and find that the statistical quality is similar. We believe that models derived from both sources of experimental data would provide greater consistency in predictions. The performance of several QSPR models that we investigated to predict outside the training set for either experimental property clearly indicates that caution should be exercised while applying any of the models for quantitative predictions. However, we are able to show that the qualitative predictions can be obtained with close to a 70% success rate.

Integrated approaches to perform in silico drug discovery.

Computer assisted, or in silico, drug discovery approaches play an important role in the search for small molecule hits and leads. These include structure- and ligand-based methods, as well as data mining and QSAR. They are used to analyze and predict ligand-receptor binding, as well as pharmacokinentic profiles of compounds with therapeutic potential. A diversity of offerings is publically/commercially available for performing these tasks. Each offering comprises select combinations of in silico methods. Efficient in silico drug discovery requires effective use of combinations of these tools. Unfortunately, no single vendor offering integrates all in silico capabilities. Typically, different vendors offer different "flavors" of the same method and specific "flavors" have associated strengths and weaknesses. Furthermore, significant inter-vendor format incompatibilities exist. Consequently, extensive scripting as well as manual intervention is required in order to overcome disparate data formats. In this article, we introduce the architecture and implementation of a highly efficient, and automated in silico drug discovery engine that integrates multi-vendor software. A single graphical user interface enables the user to 'Click & Configure' modeling tools and permits 'Mix & Matching' components from various vendors. It deploys a 'Divide & Conquer' strategy to marshal the resources of a multi-node compute cluster for compute-intensive tasks. This basic framework in performing in silico modeling activities (work-flow automation) envisions the integration of structure-based, ligand-based, and other modes of in silico drug discovery.

Computational models to predict blood-brain barrier permeation and CNS activity.

The blood-brain permeation of a structurally diverse set of 281 compounds was modeled using linear regression and a multivariate genetic partial least squares (G/PLS) approach. Key structural features affecting the logarithm of blood-brain partitioning (logBB) were captured through statistically significant quantitative structure-activity relationship (QSAR) models. These relationships reveal the importance of logP, polar surface area, and a variety of electrotopological indices for accurate predictions of logBB. The best models reveal an excellent correlation (r > 0.9) for a training set of 58 compounds. Likewise, the comparison of the average logBB values obtained from an ensemble of QSAR models with experimental values also verifies the statistical quality of the models (r > 0.9). The models provide good agreement (r approximately 0.7) between the predicted logBB values for 34 molecules in the external validation set and the experimental values. To further validate the models for use during the drug discovery process, a prediction set of 181 drugs with reported CNS penetration data was used. A >70% success rate is obtained by using any of the QSAR models in the qualitative prediction for CNS permeable (active) drugs. A lower success rate (approximately 60%) was obtained for the best model for CNS impermeable (inactive) drugs. Combining the predictions obtained from all the models (consensus) did not significantly improve the discrimination of CNS active and CNS inactive molecules. Finally, using the therapeutic classification as a guiding tool, the CNS penetration capability of over 2000 compounds in the Synthline database was estimated. The results were very similar to the smaller set of 181 compounds.

Cation-pi interactions: an energy decomposition analysis and its implication in delta-opioid receptor-ligand binding.

The nature and strength of the cation-pi interaction in protein-ligand binding are modeled by considering a series of nonbonded complexes involving N-substituted piperidines and substituted monocylic aromatics that mimic the delta-opioid receptor-ligand binding. High-level ab initio quantum mechanical calculations confirm the importance of such cation-pi interactions, whose intermolecular interaction energy ranges from -6 to -12 kcal/mol. A better understanding of the electrostatics, polarization, and other intermolecular interactions is obtained by appropriately decomposing the total interaction energy into their individual components. The energy decomposition analysis is also useful for parametrizing existing molecular mechanics force fields that could then account for energetic contributions arising out of cation-pi interactions in biomolecules. The present results further provide a framework for interpreting experimental results from point mutation reported for the delta-opioid receptor.