Use of Solvent Mapping for Characterizing the Binding Site and for Predicting the Inhibition of the Human Ether-á-Go-Go-Related K+ Channel.

Molecular dynamics was used to optimize the droperidol-hERG complex obtained from docking. To accommodate the inhibitor, residues T623, S624, V625, G648, Y652, and F656 did not move significantly during the simulation, while F627 moved significantly. Binding sites in cryo-EM structures and in structures obtained from molecular dynamics simulations were characterized using solvent mapping and Atlas ligands, which were negative images of the binding site, were generated. Atlas ligands were found to be useful for identifying human ether-á-go-go-related potassium channel (hERG) inhibitors by aligning compounds to them or by guiding the docking of compounds in the binding site. A molecular dynamics optimized structure of hERG led to improved predictions using either compound alignment to the Atlas ligand or docking. The structure was also found to be suitable to define a strategy for lowering inhibition based on the proposed binding mode of compounds in the channel.

Tetrodecadazinone, a novel tetrodecamycin-pyridazinone hybrid with anti-liver fibrosis activity from Streptomyces sp. HU051.

Tetrodecadazinone (1), a novel tetrodecamycin-pyridazinone hybrid possessing a new 1,2-dimethyl-1-(2-methylnonyl)decahydronaphthalene skeleton, and 4-hydroxydihydrotetrodecamycin (2) were separated from a culture of Streptomyces sp. HU051, together with a known compound, dihydrotetrodecamycin (3). Diverse spectroscopic approaches were applied to assign the structures of 1-3, and the structure of 1 was further confirmed by single crystal X-ray diffraction analysis. Compound 1 is the first example of a pyridazinone-containing natural product. Biosynthetically, 1 is proposed to be derived from a Michael addition reaction of a PKS-derived tetrodecamycin and a piperazic-acid-derived pyridazinone. Biological evaluation revealed 1 could reduce the expressions of extracellular matrix proteins (fibronectin and collagen I) and α-smooth muscle actin (α-SMA) in transforming growth factor-ß (TGF-ß1)-activated LX-2 cells. Preliminary mechanism study showed 1 exerted its anti-liver fibrosis effect by regulating TGF-ß1/Smad2/3 signaling pathway.

The influence of calculated physicochemical properties of compounds on their ADMET profiles.

We analyzed the influence of calculated physicochemical properties of more than 20,000 compounds on their P-gp and BCRP mediated efflux, microsomal stability, hERG inhibition, and plasma protein binding. Our goal was to provide guidance for designing compounds with desired pharmacokinetic profiles. Our analysis showed that compounds with ClogP less than 3 and molecular weight less than 400 will have high microsomal stability and low plasma protein binding. Compounds with logD less than 2.2 and/or basic pKa larger than 5.3 are likely to be BCRP substrates and compounds with basic pKa less than 5.2 and/or acidic pKa less than 13.4 are less likely to inhibit hERG. Based on these results, compounds with MW < 400, ClogP < 3, basic pKa < 5.2 and acidic pKa < 13.4 are likely to have good bioavailability and low hERG inhibition.

Chemogenomics analysis of drug targets for the treatment of acute promyelocytic leukemia.

The main challenges in treating acute promyelocytic leukemia (APL) are currently early mortality, relapse, refractory disease after induction therapy, and drug resistance to ATRA and ATO. In this study, a computational chemogenomics approach was used to identify new molecular targets and drugs for APL treatment. The transcriptional profiles induced by APL were compared with those induced by genetic or chemical perturbations. The genes that can reverse the transcriptional profiles induced by APL when perturbed were considered to be potential therapeutic targets for APL. Drugs targeting these genes or proteins are predicted to be able to treat APL if they can reverse the APL-induced transcriptional profiles. To improve the target identification accuracy of the above correlation method, we plotted the functional protein association networks of the predicted targets by STRING. The results determined PML, RARA, SPI1, HDAC3, CEBPA, NPM1, ABL1, BCR, PTEN, FOS, PDGFRB, FGFR1, NUP98, AFF1, and MEIS1 to be top candidates. Interestingly, the functions of PML, RARA, HDAC3, CEBPA, NPM1, ABL, and BCR in APL have been previously reported in the literature. This is the first chemogenomics analysis predicting potential APL drug targets, and the findings could be used to guide the design of new drugs targeting refractory and recurrent APL.

Prediction of Orthosteric and Allosteric Regulations on Cannabinoid Receptors Using Supervised Machine Learning Classifiers.

Designing highly selective compounds to protein subtypes and developing allosteric modulators targeting them are critical considerations to both drug discovery and mechanism studies for cannabinoid receptors. It is challenging but in demand to have classifiers to identify active ligands from inactive or random compounds and distinguish allosteric modulators from orthosteric ligands. In this study, supervised machine learning classifiers were built for two subtypes of cannabinoid receptors, CB1 and CB2. Three types of features, including molecular descriptors, MACCS fingerprints, and ECFP6 fingerprints, were calculated to evaluate the compound sets from diverse aspects. Deep neural networks, as well as conventional machine learning algorithms including support vector machine, naïve Bayes, logistic regression, and ensemble learning, were applied. Their performances on the classification with different types of features were compared and discussed. According to the receiver operating characteristic curves and the calculated metrics, the advantages and drawbacks of each algorithm were investigated. The feature ranking was followed to help extract useful knowledge about critical molecular properties, substructural keys, and circular fingerprints. The extracted features will then facilitate the research on cannabinoid receptors by providing guidance on preferred properties for compound modification and novel scaffold design. Besides using conventional molecular docking studies for compound virtual screening, machine-learning-based decision-making models provide alternative options. This study can be of value to the application of machine learning in the area of drug discovery and compound development.

SQSTM1/p62: A Potential Target for Neurodegenerative Disease.

Neurodegenerative diseases, characterized by a progressive loss of brain function, affect the lives of millions of individuals worldwide. The complexity of the brain poses a challenge for scientists trying to map the biochemical and physiological pathways to identify areas of pathological errors. Brain samples of patients with neurodegenerative diseases have been shown to contain large amounts of misfolded and abnormally aggregated proteins, resulting in dysfunction in certain brain centers. Removal of these abnormal molecules is essential in maintaining protein homeostasis and overall neuronal health. Macroautophagy is a major route by which cells achieve this. Administration of certain autophagy-enhancing compounds has been shown to provide therapeutic effects for individuals with neurodegenerative conditions. SQSTM1/p62 is a scaffold protein closely involved in the macroautophagy process. p62 functions to anchor the ubiquitinated proteins to the autophagosome membrane, promoting degradation of unwanted molecules. Modulators targeting p62 to induce autophagy and promote its protective pathways for aggregate protein clearance have high potential in the treatment of these conditions. Additionally, causal relationships have been found between errors in regulation of SQSTM1/p62 and the development of a variety of neurodegenerative disorders, including Alzheimer's, Parkinson's, Huntington's, amyotrophic lateral sclerosis, and frontotemporal lobar degeneration. Furthermore, SQSTM1/p62 also serves as a signaling hub for multiple pathways associated with neurodegeneration, providing a potential therapeutic target in the treatment of neurodegenerative diseases. However, rational design of a p62-oriented autophagy modulator that can balance the negative and positive functions of multiple domains in p62 requires further efforts in the exploration of the protein structure and pathological basis.

Chemogenomics Systems Pharmacology Mapping of Potential Drug Targets for Treatment of Traumatic Brain Injury.

Traumatic brain injury (TBI) is associated with high mortality and morbidity. Though the death rate of initial trauma has dramatically decreased, no drug has been developed to effectively limit the progression of the secondary injury caused by TBI. TBI appears to be a predisposing risk factor for Alzheimer's disease (AD), whereas the molecular mechanisms remain unknown. In this study, we have conducted a research investigation of computational chemogenomics systems pharmacology (CSP) to identify potential drug targets for TBI treatment. TBI-induced transcriptional profiles were compared with those induced by genetic or chemical perturbations, including drugs in clinical trials for TBI treatment. The protein-protein interaction network of these predicted targets were then generated for further analyses. Some protein targets when perturbed, exhibit inverse transcriptional profiles in comparison with the profiles induced by TBI, and they were recognized as potential therapeutic targets for TBI. Drugs acting on these targets are predicted to have the potential for TBI treatment if they can reverse the TBI-induced transcriptional profiles that lead to secondary injury. In particular, our results indicated that TRPV4, NEUROD1, and HPRT1 were among the top therapeutic target candidates for TBI, which are congruent with literature reports. Our analyses also suggested the strong associations between TBI and AD, as perturbations on AD-related genes, such as APOE, APP, PSEN1, and MAPT, can induce similar gene expression patterns as those of TBI. To the best of our knowledge, this is the first CSP-based gene expression profile analyses for predicting TBI-related drug targets, and the findings could be used to guide the design of new drugs targeting the secondary injury caused by TBI.

Synergism of antihypertensives and cholinesterase inhibitors in Alzheimer's disease.

INTRODUCTION: We investigated the effect of antihypertensive (aHTN) medications and cholinesterase inhibitors (ChEIs) on the cognitive decline in patients with Alzheimer's disease (AD) and analyzed synergism by chemogenomics systems pharmacology mapping. METHODS: We compared the effect of aHTN drugs on Mini-Mental State Examination scores in 617 AD patients with hypertension, and studied the synergistic effects. RESULTS: The combination of diuretics, calcium channel blockers, and renin-angiotensin-aldosterone system blockers showed slower cognitive decline compared with other aHTN groups (Δß = +1.46, P < .0001). aHTN medications slow down cognitive decline in ChEI users (Δß = +0.56, P = .006), but not in non-ChEI users (Δß = -0.31, P = .53). DISCUSSION: aHTN and ChEI drugs showed synergistic effects. A combination of diuretics, renin-angiotensin-aldosterone system blockers, and calcium channel blockers had the slowest cognitive decline. The chemogenomics systems pharmacology-identified molecular targets provide system pharmacology interpretation of the synergism of the drugs in clinics. The results suggest that improving vascular health is essential for AD treatment and provide a novel direction for AD drug development.

Discovery of Novel Ligands for TNF-α and TNF Receptor-1 through Structure-Based Virtual Screening and Biological Assay.

Tumor necrosis factor α (TNF-α) is overexpressed in various diseases, and it has been a validated therapeutic target for autoimmune diseases. All therapeutics currently used to target TNF-α are biomacromolecules, and limited numbers of TNF-α chemical inhibitors have been reported, which makes the identification of small-molecule alternatives an urgent need. Recent studies have mainly focused on identifying small molecules that directly bind to TNF-α or TNF receptor-1 (TNFR1), inhibit the interaction between TNF-α and TNFR1, and/or regulate related signaling pathways. In this study, we combined in silico methods with biophysical and cell-based assays to identify novel antagonists that bind to TNF-α or TNFR1. Pharmacophore model filtering and molecular docking were applied to identify potential TNF-α antagonists. In regard to TNFR1, we constructed a three-dimensional model of the TNF-α-TNFR1 complex and carried out molecular dynamics simulations to sample the conformations. The residues in TNF-α that have been reported to play important roles in the TNF-α-TNFR1 complex were removed to form a pocket for further virtual screening of TNFR1-binding ligands. We obtained 20 virtual hits and tested them using surface plasmon resonance-based assays, which resulted in one ligand that binds to TNFR1 and four ligands with different scaffolds that bind to TNF-α. T1 and R1, the two most active compounds with Kd values of 11 and 16 µM for TNF-α and TNFR1, respectively, showed activities similar to those of known antagonists. Further cell-based assays also demonstrated that T1 and R1 have similar activities compared to the known TNF-α antagonist C87. Our work has not only produced several TNF-α and TNFR1 antagonists with novel scaffolds for further structural optimization but also showcases the power of our in silico methods for TNF-α- and TNFR1-based drug discovery.

Chemogenomics knowledgebase and systems pharmacology for hallucinogen target identification-Salvinorin A as a case study.

Drug abuse is a serious problem worldwide. Recently, hallucinogens have been reported as a potential preventative and auxiliary therapy for substance abuse. However, the use of hallucinogens as a drug abuse treatment has potential risks, as the fundamental mechanisms of hallucinogens are not clear. So far, no scientific database is available for the mechanism research of hallucinogens. We constructed a hallucinogen-specific chemogenomics database by collecting chemicals, protein targets and pathways closely related to hallucinogens. This information, together with our established computational chemogenomics tools, such as TargetHunter and HTDocking, provided a one-step solution for the mechanism study of hallucinogens. We chose salvinorin A, a potent hallucinogen extracted from the plant Salvia divinorum, as an example to demonstrate the usability of our platform. With the help of HTDocking program, we predicted four novel targets for salvinorin A, including muscarinic acetylcholine receptor 2, cannabinoid receptor 1, cannabinoid receptor 2 and dopamine receptor 2. We looked into the interactions between salvinorin A and the predicted targets. The binding modes, pose and docking scores indicate that salvinorin A may interact with some of these predicted targets. Overall, our database enriched the information of systems pharmacological analysis, target identification and drug discovery for hallucinogens.

Cardiovascular Disease Chemogenomics Knowledgebase-guided Target Identification and Drug Synergy Mechanism Study of an Herbal Formula.

Combination therapy is a popular treatment for various diseases in the clinic. Among the successful cases, Traditional Chinese Medicinal (TCM) formulae can achieve synergistic effects in therapeutics and antagonistic effects in toxicity. However, characterizing the underlying molecular synergisms for the combination of drugs remains a challenging task due to high experimental expenses and complication of multicomponent herbal medicines. To understand the rationale of combination therapy, we investigated Sini Decoction, a well-known TCM consisting of three herbs, as a model. We applied our established diseases-specific chemogenomics databases and our systems pharmacology approach TargetHunter to explore synergistic mechanisms of Sini Decoction in the treatment of cardiovascular diseases. (1) We constructed a cardiovascular diseases-specific chemogenomics database, including drugs, target proteins, chemicals, and associated pathways. (2) Using our implemented chemoinformatics tools, we mapped out the interaction networks between active ingredients of Sini Decoction and their targets. (3) We also in silico predicted and experimentally confirmed that the side effects can be alleviated by the combination of the components. Overall, our results demonstrated that our cardiovascular disease-specific database was successfully applied for systems pharmacology analysis of a complicated herbal formula in predicting molecular synergetic mechanisms, and led to better understanding of a combinational therapy.

Difference and Influence of Inactive and Active States of Cannabinoid Receptor Subtype CB2: From Conformation to Drug Discovery.

Cannabinoid receptor 2 (CB2), a G protein-coupled receptor (GPCR), is a promising target for the treatment of neuropathic pain, osteoporosis, immune system, cancer, and drug abuse. The lack of an experimental three-dimensional CB2 structure has hindered not only the development of studies of conformational differences between the inactive and active CB2 but also the rational discovery of novel functional compounds targeting CB2. In this work, we constructed models of both inactive and active CB2 by homology modeling. Then we conducted two comparative 100 ns molecular dynamics (MD) simulations on the two systems-the active CB2 bound with both the agonist and G protein and the inactive CB2 bound with inverse agonist-to analyze the conformational difference of CB2 proteins and the key residues involved in molecular recognition. Our results showed that the inactive CB2 and the inverse agonist remained stable during the MD simulation. However, during the MD simulations, we observed dynamical details about the breakdown of the "ionic lock" between R131(3.50) and D240(6.30) as well as the outward/inward movements of transmembrane domains of the active CB2 that bind with G proteins and agonist (TM5, TM6, and TM7). All of these results are congruent with the experimental data and recent reports. Moreover, our results indicate that W258(6.48) in TM6 and residues in TM4 (V164(4.56)-L169(4.61)) contribute greatly to the binding of the agonist on the basis of the binding energy decomposition, while residues S180-F183 in extracellular loop 2 (ECL2) may be of importance in recognition of the inverse agonist. Furthermore, pharmacophore modeling and virtual screening were carried out for the inactive and active CB2 models in parallel. Among all 10 hits, two compounds exhibited novel scaffolds and can be used as novel chemical probes for future studies of CB2. Importantly, our studies show that the hits obtained from the inactive CB2 model mainly act as inverse agonist(s) or neutral antagonist(s) at low concentration. Moreover, the hit from the active CB2 model also behaves as a neutral antagonist at low concentration. Our studies provide new insight leading to a better understanding of the structural and conformational differences between two states of CB2 and illuminate the effects of structure on virtual screening and drug design.

Multi-Functional Diarylurea Small Molecule Inhibitors of TRPV1 with Therapeutic Potential for Neuroinflammation.

Transient receptor potential vanilloid type 1 (TRPV1), a heat-sensitive calcium channel protein, contributes to inflammation as well as to acute and persistent pain. Since TRPV1 occupies a central position in pathways of neuronal inflammatory signaling, it represents a highly attractive potential therapeutic target for neuroinflammation. In the present work, we have in silico identified a series of diarylurea analogues for hTRPV1, of which 11 compounds showed activity in the nanomolar to micromolar range as validated by in vitro biological assays. Then, we utilized molecular docking to explore the detailed interactions between TRPV1 and the compounds to understand the contributions of the different substituent groups. Tyr511, Leu518, Leu547, Thr550, Asn551, Arg557, and Leu670 were important for the recognition of the small molecules by TRPV1. A hydrophobic group in R2 or a polar/hydrophilic group in R1 contributed significantly to the activities of the antagonists at TRPV1. In addition, the subtle different binding pose of meta-chloro in place of para-fluoro in the R2 group converted antagonism into partial agonism, as was predicted by our short-term molecular dynamics (MD) simulation and validated by bioassay. Importantly, compound 15, one of our best TRPV1 inhibitors, also showed potential binding affinity (1.39 µM) at cannabinoid receptor 2 (CB2), which is another attractive target for immuno-inflammation diseases. Furthermore, compound 1 and its diarylurea analogues were predicted to target the C-X-C chemokine receptor 2 (CXCR2), although bioassay validation of CXCR2 with these compounds still needs to be performed. This prediction from the modeling is of interest, since CXCR2 is also a potential therapeutic target for chronic inflammatory diseases. Our findings provide novel strategies to develop a small molecule inhibitor to simultaneously target two or more inflammation-related proteins for the treatment of a wide range of inflammatory disorders including neuroinflammation and neurodegenerative diseases with potential synergistic effect.

Computational Advances for the Development of Allosteric Modulators and Bitopic Ligands in G Protein-Coupled Receptors.

Allosteric modulators of G protein-coupled receptors (GPCRs), which target at allosteric sites, have significant advantages against the corresponding orthosteric compounds including higher selectivity, improved chemical tractability or physicochemical properties, and reduced risk of receptor oversensitization. Bitopic ligands of GPCRs target both orthosteric and allosteric sites. Bitopic ligands can improve binding affinity, enhance subtype selectivity, stabilize receptors, and reduce side effects. Discovering allosteric modulators or bitopic ligands for GPCRs has become an emerging research area, in which the design of allosteric modulators is a key step in the detection of bitopic ligands. Radioligand binding and functional assays ([(35)S]GTPγS and ERK1/2 phosphorylation) are used to test the effects for potential modulators or bitopic ligands. High-throughput screening (HTS) in combination with disulfide trapping and fragment-based screening are used to aid the discovery of the allosteric modulators or bitopic ligands of GPCRs. When used alone, these methods are costly and can often result in too many potential drug targets, including false positives. Alternatively, low-cost and efficient computational approaches are useful in drug discovery of novel allosteric modulators and bitopic ligands to help refine the number of targets and reduce the false-positive rates. This review summarizes the state-of-the-art computational methods for the discovery of modulators and bitopic ligands. The challenges and opportunities for future drug discovery are also discussed.

Allosteric Binding Site and Activation Mechanism of Class C G-Protein Coupled Receptors: Metabotropic Glutamate Receptor Family.

Metabotropic glutamate receptors (mGluR) are mainly expressed in the central nervous system (CNS) and contain eight receptor subtypes, named mGluR1 to mGluR8. The crystal structures of mGluR1 and mGluR5 that are bound with the negative allosteric modulator (NAM) were reported recently. These structures provide a basic model for all class C of G-protein coupled receptors (GPCRs) and may aid in the design of new allosteric modulators for the treatment of CNS disorders. However, these structures are only combined with NAMs in the previous reports. The conformations that are bound with positive allosteric modulator (PAM) or agonist of mGluR1/5 remain unknown. Moreover, the structural information of the other six mGluRs and the comparisons of the mGluRs family have not been explored in terms of their binding pockets, the binding modes of different compounds, and important binding residues. With these crystal structures as the starting point, we built 3D structural models for six mGluRs by using homology modeling and molecular dynamics (MD) simulations. We systematically compared their allosteric binding sites/pockets, the important residues, and the selective residues by using a series of comparable dockings with both the NAM and the PAM. Our results show that several residues played important roles for the receptors' selectivity. The observations of detailed interactions between compounds and their correspondent receptors are congruent with the specificity and potency of derivatives or compounds bioassayed in vitro. We then carried out 100 ns MD simulations of mGluR5 (residue 26-832, formed by Venus Flytrap domain, a so-called cysteine-rich domain, and 7 trans-membrane domains) bound with antagonist/NAM and with agonist/PAM. Our results show that both the NAM and the PAM seemed stable in class C GPCRs during the MD. However, the movements of "ionic lock," of trans-membrane domains, and of some activation-related residues in 7 trans-membrane domains of mGluR5 were congruent with the findings in class A GPCRs. Finally, we selected nine representative bound structures to perform 30 ns MD simulations for validating the stabilities of interactions, respectively. All these bound structures kept stable during the MD simulations, indicating that the binding poses in this present work are reasonable. We provided new insight into better understanding of the structural and functional roles of the mGluRs family and facilitated the future structure-based design of novel ligands of mGluRs family with therapeutic potential.