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.

Abstract. 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.

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 K 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.

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 R1313.50 and D2406.30 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 W2586.48 in TM6 and residues in TM4 (V1644.56–L1694.61) 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.

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.

Abstract. 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 transmembrane 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 Bionic 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.

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.

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.