Two Novel Metformin Carboxylate Salts and the Accidental Discovery of Two 1,3,5-Triazine Antihyperglycemic Agent.

Metformin (MET), commonly marketed as a hydrochloride salt (MET-HCl) for better pharmacokinetic profile over the free base, would release a high concentration of chloride ions and cause adverse gastrointestinal effects. The preparation of chloride-free MET salts could potentially circumvent this issue. In this study, seven carboxylic acids (formic acid, acetic acid, malonic acid, succinic acid, fumaric acid, cinnamic acid, and acetylsalicylic acid) were used for preparing MET carboxylate salts. When compared with MET-HCl, all MET salts/salt hydrates show lower dissolution rates in pH 6.8 phosphate buffer. However, the cinnamic acid and acetylsalicylic acid show significantly higher dissolution rates in the forms of MET salt/salt hydrate. In the permeability test, the permeability of the MET in all of the salts was not improved. However, the permeability of cinnamic acid in the MET cinnamate is reduced, and the permeability of acetylsalicylic acid in the MET acetylsalicylate is increased. Meanwhile, at a higher crystallization temperature, the acetone solvent and a hydrolyzed product of acetylsalicylic acid react with MET respectively, leading to two unexpected 1,3,5-triazine derivatives. The results of in vitro bioactivity assays indicate that one of the triazine molecules promote glucose consumption more effectively than MET-HCl, and had relatively weak lactate production ability at low concentration. This glucose metabolism regulating compound may serve as a novel lead antihyperglycemic agent for further optimization.

Translocation Mechanism of Allosteric Sodium Ions in ß2-Adrenoceptor.

The allosteric modulation of G-protein-coupled receptors (GPCRs) by sodium ions has received significant attention as the crystal structures of several receptors show the binding of sodium ions (Na+) at the conserved D2.50. Theoretical studies have shown that extracellular Na+ would enter the allosteric D2.50 via the orthosteric site. However, it remains unclear how the bound allosteric Na+ would leave the GPCRs. In this study, we performed molecular dynamics (MD) simulations to illustrate the energy barriers of Na+ transfer through the transmembrane helix bundle of ß2AR. In contrast to the postulations from other GPCRs, the translocation of this allosteric Na+ into the intracellular side is found to be significantly difficult. Hence, the translocation direction could be receptor-specific.

Accurate Physical Property Predictions via Deep Learning.

Neural networks and deep learning have been successfully applied to tackle problems in drug discovery with increasing accuracy over time. There are still many challenges and opportunities to improve molecular property predictions with satisfactory accuracy even further. Here, we proposed a deep-learning architecture model, namely Bidirectional long short-term memory with Channel and Spatial Attention network (BCSA), of which the training process is fully data-driven and end to end. It is based on data augmentation and SMILES tokenization technology without relying on auxiliary knowledge, such as complex spatial structure. In addition, our model takes the advantages of the long- and short-term memory network (LSTM) in sequence processing. The embedded channel and spatial attention modules in turn specifically identify the prime factors in the SMILES sequence for predicting properties. The model was further improved by Bayesian optimization. In this work, we demonstrate that the trained BSCA model is capable of predicting aqueous solubility. Furthermore, our proposed method shows noticeable superiorities and competitiveness in predicting oil-water partition coefficient, when compared with state-of-the-art graphs models, including graph convoluted network (GCN), message-passing neural network (MPNN), and AttentiveFP.

MolADI: A Web Server for Automatic Analysis of Protein-Small Molecule Dynamic Interactions.

Protein-ligand interaction analysis is important for drug discovery and rational protein design. The existing online tools adopt only a single conformation of the complex structure for calculating and displaying the interactions, whereas both protein residues and ligand molecules are flexible to some extent. The interactions evolved with time in the trajectories are of greater interest. MolADI is a user-friendly online tool which analyzes the protein-ligand interactions in detail for either a single structure or a trajectory. Interactions can be viewed easily with both 2D graphs and 3D representations. MolADI is available as a web application.

Enhancing the Signaling of GPCRs via Orthosteric Ions.

G protein-coupled receptors play essential roles in cellular processes such as neuronal signaling, vision, olfaction, tasting, and metabolism. As GPCRs are the most important drug targets, understanding their interactions with ligands is of utmost importance for discovering related new medicines. In many GPCRs, an allosteric sodium ion next to the highly conserved residue D2.50 has been proposed to stabilize the inactive receptor state by mediating interactions between transmembrane helices. Here, we probed the existence of internal and functionally important sodium ions in the dopamine D2 receptor, using molecular dynamics simulations. Besides a new sodium ion at the allosteric ligand binding site, we discovered an additional sodium ion, located close to the orthosteric ligand binding site. Through cell-based activation assays, the signaling of D2 receptor with site-specific mutations was tested against a series of chemically modified agonists. We concluded an important structural role of this newly discovered orthosteric sodium ion in modulating the receptor signaling: It enables the coordination of a polar residue in the ligand binding site with an appropriately designed agonist molecule. An identical interaction was also observed in a recently released high-resolution crystal structure of mu-opioid receptor, which was reresolved in this work. Probably because of similar interactions, various metal ions have been found to increase the signaling of many other GPCRs. This unique principle and strategy could be used to optimize the drug activity of GPCR. Our findings open a new mechanistic opportunity of GPCR signaling and help design the next generation of drugs targeting GPCRs.

Clinical HDAC Inhibitors Are Effective Drugs to Prevent the Entry of SARS-CoV2.

The outbreak of COVID-19 by the end of 2019 has posed serious health threats to humanity and jeopardized the global economy. However, no effective drugs are available to treat COVID-19 currently and there is a great demand to fight against it. Here, we combined computational screening and an efficient cellular pseudotyped virus system, confirming that clinical HDAC inhibitors can efficiently prevent SARS-CoV-2 and potentially be used to fight against COVID-19.

Advancing Drug Discovery via Artificial Intelligence.

Drug discovery and development are among the most important translational science activities that contribute to human health and wellbeing. However, the development of a new drug is a very complex, expensive, and long process which typically costs 2.6 billion USD and takes 12 years on average. How to decrease the costs and speed up new drug discovery has become a challenging and urgent question in industry. Artificial intelligence (AI) combined with new experimental technologies is expected to make the hunt for new pharmaceuticals quicker, cheaper, and more effective. We discuss here emerging applications of AI to improve the drug discovery process.

New Binding Sites, New Opportunities for GPCR Drug Discovery.

Many central biological events rely on protein-ligand interactions. The identification and characterization of protein-binding sites for ligands are crucial for the understanding of functions of both endogenous ligands and synthetic drug molecules. G protein-coupled receptors (GPCRs) typically detect extracellular signal molecules on the cell surface and transfer these chemical signals across the membrane, inducing downstream cellular responses via G proteins or ß-arrestin. GPCRs mediate many central physiological processes, making them important targets for modern drug discovery. Here, we focus on the most recent breakthroughs in finding new binding sites and binding modes of GPCRs and their potentials for the development of new medicines.

Exploring a new ligand binding site of G protein-coupled receptors.

Identifying a target ligand binding site is an important step for structure-based rational drug design as shown here for G protein-coupled receptors (GPCRs), which are among the most popular drug targets. We applied long-time scale molecular dynamics simulations, coupled with mutagenesis studies, to two prototypical GPCRs, the M3 and M4 muscarinic acetylcholine receptors. Our results indicate that unlike synthetic antagonists, which bind to the classic orthosteric site, the endogenous agonist acetylcholine is able to diffuse into a much deeper binding pocket. We also discovered that the most recently resolved crystal structure of the LTB4 receptor comprised a bound inverse agonist, which extended its benzamidine moiety to the same binding pocket discovered in this work. Analysis on all resolved GPCR crystal structures indicated that this new pocket could exist in most receptors. Our findings provide new opportunities for GPCR drug discovery.

Structural and Computational Insight into the Catalytic Mechanism of Limonene Epoxide Hydrolase Mutants in Stereoselective Transformations.

Directed evolution of limonene epoxide hydrolase (LEH), which catalyzes the hydrolytic desymmetrization reactions of cyclopentene oxide and cyclohexene oxide, results in (R,R)- and (S,S)-selective mutants. Their crystal structures combined with extensive theoretical computations shed light on the mechanistic intricacies of this widely used enzyme. From the computed activation energies of various pathways, we discover the underlying stereochemistry for favorable reactions. Surprisingly, some of the most enantioselective mutants that rapidly convert cyclohexene oxide do not catalyze the analogous transformation of the structurally similar cyclopentene oxide, as shown by additional X-ray structures of the variants harboring this slightly smaller substrate. We explain this puzzling observation on the basis of computational calculations which reveal a disrupted alignment between nucleophilic water and cyclopentene oxide due to the pronounced flexibility of the binding pocket. In contrast, in the stereoselective reactions of cyclohexene oxide, reactive conformations are easily reached. The unique combination of structural and computational data allows insight into mechanistic details of this epoxide hydrolase and provides guidance for future protein engineering in reactions of structurally different substrates.

GyrI-like proteins catalyze cyclopropanoid hydrolysis to confer cellular protection.

GyrI-like proteins are widely distributed in prokaryotes and eukaryotes, and recognized as small-molecule binding proteins. Here, we identify a subfamily of these proteins as cyclopropanoid cyclopropyl hydrolases (CCHs) that can catalyze the hydrolysis of the potent DNA-alkylating agents yatakemycin (YTM) and CC-1065. Co-crystallography and molecular dynamics simulation analyses reveal that these CCHs share a conserved aromatic cage for the hydrolytic activity. Subsequent cytotoxic assays confirm that CCHs are able to protect cells against YTM. Therefore, our findings suggest that the evolutionarily conserved GyrI-like proteins confer cellular protection against diverse xenobiotics via not only binding, but also catalysis.

Designing Safer Analgesics via µ-Opioid Receptor Pathways.

Pain is both a major clinical and economic problem, affecting more people than diabetes, heart disease, and cancer combined. While a variety of prescribed or over-the-counter (OTC) medications are available for pain management, opioid medications, especially those acting on the µ-opioid receptor (µOR) and related pathways, have proven to be the most effective, despite some serious side effects including respiration depression, pruritus, dependence, and constipation. It is therefore imperative that both academia and industry develop novel µOR analgesics which retain their opioid analgesic properties but with fewer or no adverse effects. In this review we outline recent progress towards the discovery of safer opioid analgesics.

Implementing WebGL and HTML5 in Macromolecular Visualization and Modern Computer-Aided Drug Design.

Web browsers have long been recognized as potential platforms for remote macromolecule visualization. However, the difficulty in transferring large-scale data to clients and the lack of native support for hardware-accelerated applications in the local browser undermine the feasibility of such utilities. With the introduction of WebGL and HTML5 technologies in recent years, it is now possible to exploit the power of a graphics-processing unit (GPU) from a browser without any third-party plugin. Many new tools have been developed for biological molecule visualization and modern drug discovery. In contrast to traditional offline tools, real-time computing, interactive data analysis, and cross-platform analyses feature WebGL- and HTML5-based tools, facilitating biological research in a more efficient and user-friendly way.

The Principles of Ligand Specificity on beta-2-adrenergic receptor.

G protein-coupled receptors are recognized as one of the largest families of membrane proteins. Despite sharing a characteristic seven-transmembrane topology, G protein-coupled receptors regulate a wide range of cellular signaling pathways in response to various physical and chemical stimuli, and prevail as an important target for drug discovery. Notably, the recent progress in crystallographic methods led to a breakthrough in elucidating the structures of membrane proteins. The structures of ß2-adrenergic receptor bound with a variety of ligands provide atomic details of the binding modes of agonists, antagonists and inverse agonists. In this study, we selected four representative molecules from each functional class of ligands and investigated their impacts on ß2-adrenergic receptor through a total of 12 × 100 ns molecular dynamics simulations. From the obtained trajectories, we generated molecular fingerprints exemplifying propensities of protein-ligand interactions. For each functional class of compounds, we characterized and compared the fluctuation of the protein backbone, the volumes in the intracellular pockets, the water densities in the receptors, the domain interaction networks as well as the movements of transmembrane helices. We discovered that each class of ligands exhibits a distinct mode of interactions with mainly TM5 and TM6, altering the shape and eventually the state of the receptor. Our findings provide insightful prospective into GPCR targeted structure-based drug discoveries.

The Molecular Mechanism of P2Y1 Receptor Activation.

Human purinergic G protein-coupled receptor P2Y1 (P2Y1 R) is activated by adenosine 5'-diphosphate (ADP) to induce platelet activation and thereby serves as an important antithrombotic drug target. Crystal structures of P2Y1 R revealed that one ligand (MRS2500) binds to the extracellular vestibule of this GPCR, whereas another (BPTU) occupies the surface between transmembrane (TM) helices TM2 and TM3. We introduced a total of 20 µs all-atom long-timescale molecular dynamic (MD) simulations to inquire why two molecules in completely different locations both serve as antagonists while ADP activates the receptor. Our results indicate that BPTU acts as an antagonist by stabilizing extracellular helix bundles leading to an increase of the lipid order, whereas MRS2500 blocks signaling by occupying the ligand binding site. Both antagonists stabilize an ionic lock within the receptor. However, binding of ADP breaks this ionic lock, forming a continuous water channel that leads to P2Y1 R activation.

Predictability of the polymorphs of small organic compounds: crystal structure predictions of four benchmark blind test molecules.

Predicting the crystal structure of an organic molecule from first principles has been a major challenge in physical chemistry. Recently, the application of Density Functional Theory including a dispersive energy correction (the DFT(d) method) has been shown to be a reliable method for predicting experimental structures based purely on their ranking according to lattice energy. Further validation results of the application of the DFT(d) method to four organic molecules are presented here. The compounds were targets (labelled molecule II, VI, VII and XI) in previous blind tests of crystal structure prediction, and their structures proved difficult to predict. However, this study shows that the DFT(d) approach is capable of predicting the solid state structures of these small molecules. For molecule VII, the most stable (rank 1) predicted crystal structure corresponds to the experimentally observed structure. For molecule VI, the rank 1, 2 and 3 predicted structures correspond to the three experimental polymorphs, forms I, III and II, respectively. For molecules II and XI, their rank 1 predicted structures are energetically more stable than those corresponding to the experimental crystal structures, and were not found amongst the structures submitted by the participants in the blind tests. The rank 1 structure of molecule II is predicted to exist under high pressure, whilst the rank 1 structure predicted for molecule XI has the same space group and hydrogen bonding pattern as observed in the crystal of 1-amino-1-methyl-cyclopropane, which is structurally related to molecule XI. The experimental crystal structure of molecule II corresponds to the rank 4 prediction, 0.8 kJ mol(-1) above the global minimum structure, and the experimental structure of molecule XI corresponds to the rank 2 prediction, 0.4 kJ mol(-1) above the global minimum.