A Quantum Mechanics-Based Method to Predict Intramolecular Hydrogen Bond Formation Reflecting P-glycoprotein Recognition.

Passive membrane permeability and an active transport process are key determinants for penetrating the blood-brain barrier. P-glycoprotein (P-gp), a well-known transporter, serves as the primary gatekeeper, having broad substrate specificity. A strategy to increase passive permeability and impair P-gp recognition is intramolecular hydrogen bonding (IMHB). 3 is a potent brain penetrant BACE1 inhibitor with high permeability and low P-gp recognition, although slight modifications to its tail amide group significantly affect P-gp efflux. We hypothesized that the difference in the propensity to form IMHB could impact P-gp recognition. Single-bond rotation at the tail group enables both IMHB forming and unforming conformations. We developed a quantum-mechanics-based method to predict IMHB formation ratios (IMHBRs). In a given data set, IMHBRs accounted for the corresponding temperature coefficients measured in NMR experiments, correlating with P-gp efflux ratios. Furthermore, the method was applied in hNK2 receptor antagonists, demonstrating that the IMHBR could be applied to other drug targets involving IMHB.

Identification of cap-dependent endonuclease inhibitors with broad-spectrum activity against bunyaviruses.

Viral hemorrhagic fevers caused by members of the order Bunyavirales comprise endemic and emerging human infections that are significant public health concerns. Despite the disease severity, there are few therapeutic options available, and therefore effective antiviral drugs are urgently needed to reduce disease burdens. Bunyaviruses, like influenza viruses (IFVs), possess a cap-dependent endonuclease (CEN) that mediates the critical cap-snatching step of viral RNA transcription. We screened compounds from our CEN inhibitor (CENi) library and identified specific structural compounds that are 100 to 1,000 times more active in vitro than ribavirin against bunyaviruses, including Lassa virus, lymphocytic choriomeningitis virus (LCMV), and Junin virus. To investigate their inhibitory mechanism of action, drug-resistant viruses were selected in culture. Whole-genome sequencing revealed that amino acid substitutions in the CEN region of drug-resistant viruses were located in similar positions as those of the CEN α3-helix loop of IFVs derived under drug selection. Thus, our studies suggest that CENi compounds inhibit both bunyavirus and IFV replication in a mechanistically similar manner. Structural analysis revealed that the side chain of the carboxyl group at the seventh position of the main structure of the compound was essential for the high antiviral activity against bunyaviruses. In LCMV-infected mice, the compounds significantly decreased blood viral load, suppressed symptoms such as thrombocytopenia and hepatic dysfunction, and improved survival rates. These data suggest a potential broad-spectrum clinical utility of CENis for the treatment of both severe influenza and hemorrhagic diseases caused by bunyaviruses.

Peptide-to-Small Molecule: A Pharmacophore-Guided Small Molecule Lead Generation Strategy from High-Affinity Macrocyclic Peptides.

Recent technological innovations have led to the development of methods for the rapid identification of high-affinity macrocyclic peptides for a wide range of targets; however, it is still challenging to achieve the desired activity and membrane permeability at the same time. Here, we propose a novel small molecule lead discovery strategy, ″Peptide-to-Small Molecule″, which is a combination of rapid identification of high-affinity macrocyclic peptides via peptide display screening followed by pharmacophore-guided de novo design of small molecules, and demonstrate the applicability using nicotinamide N-methyltransferase (NNMT) as a target. Affinity selection by peptide display technology identified macrocyclic peptide 1 that exhibited good enzymatic inhibitory activity but no cell-based activity. Thereafter, a peptide pharmacophore-guided de novo design and further structure-based optimization resulted in highly potent and cell-active small molecule 14 (cell-free IC50 = 0.0011 µM, cell-based IC50 = 0.40 µM), indicating that this strategy could be a new option for drug discovery.

Optimization of Cyclic Peptide Property Using Chromatographic Capacity Factor on Permeability of Passive Cell Membrane and Human Induced Pluripotent Stem Cell-Derived Intestinal Membrane.

Cyclic peptides have attracted increasing attention as a privileged class of molecules addressing undruggable targets. Cell permeability of cyclic peptides has remained a challenging issue owing to their molecular properties. Various efficiency metrics have emerged to assess this issue. Among them, the lipophilic permeability efficiency (LPE) metric is the difference between an experimental 1,9-decadiene-water partition coefficient at pH 7.4 (log Ddec/w) and calculated octanol/water partition coefficients (ALogP). This metric provides insight into how structural changes affect permeability. Here, we demonstrate the chromatographic capacity factor (log k') of cyclic peptides using reversed-phase liquid chromatography as an alternative to log Ddec/w, which enables efficient and reliable experimental lipophilicity for the adoption of LPE in early drug discovery. The log k' indicates the passive membrane permeability of cyclic peptides and can be used to optimize passive membrane permeability in combination with other parameters. In addition, intestinal membrane permeability of cyclic peptides on human induced pluripotent stem cell-derived intestinal epithelial cells was achieved with log k' and high passive membrane permeability, although cyclic peptides are P-glycoprotein substrates. These approaches could facilitate optimization of properties of cyclic peptides for oral administration and contribute to the successful discovery and development of cyclic peptides.

Discovery of S-217622, a Noncovalent Oral SARS-CoV-2 3CL Protease Inhibitor Clinical Candidate for Treating COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in millions of deaths and threatens public health and safety. Despite the rapid global spread of COVID-19 vaccines, effective oral antiviral drugs are urgently needed. Here, we describe the discovery of S-217622, the first oral noncovalent, nonpeptidic SARS-CoV-2 3CL protease inhibitor clinical candidate. S-217622 was discovered via virtual screening followed by biological screening of an in-house compound library, and optimization of the hit compound using a structure-based drug design strategy. S-217622 exhibited antiviral activity in vitro against current outbreaking SARS-CoV-2 variants and showed favorable pharmacokinetic profiles in vivo for once-daily oral dosing. Furthermore, S-217622 dose-dependently inhibited intrapulmonary replication of SARS-CoV-2 in mice, indicating that this novel noncovalent inhibitor could be a potential oral agent for treating COVID-19.

Macrocyclic Peptides as a Novel Class of NNMT Inhibitors: A SAR Study Aimed at Inhibitory Activity in the Cell.

Nicotinamide N-methyltransferase (NNMT), which catalyzes the methylation of nicotinamide, is a cytosolic enzyme that has attracted much attention as a therapeutic target for a variety of diseases. However, despite the considerable interest in this target, reports of NNMT inhibitors have still been limited to date. In this work, utilizing in vitro translated macrocyclic peptide libraries, we identified peptide 1 as a novel class of NNMT inhibitors. Further exploration based on the X-ray cocrystal structures of the peptides with NNMT provided a dramatic improvement in inhibitory activity (peptide 23: IC50 = 0.15 nM). Furthermore, by balance of the peptides' lipophilicity and biological activity, inhibitory activity against NNMT in cell-based assay was successfully achieved (peptide 26: cell-based IC50 = 770 nM). These findings illuminate the potential of cyclic peptides as a relatively new drug discovery modality even for intracellular targets.

Role of ARHGAP24 in ADP Ribosylation Factor 6 (ARF6)-dependent Pseudopod Formation in Human Breast Carcinoma Cells.

BACKGROUND/AIM: The small GTPase ADP ribosylation factor 6 (ARF6) promotes carcinoma cell invasion and metastasis through remodeling of actin cytoskeleton and formation of pseudopod that is regulated by RAC. RHO GTPase activating protein 24 (ARHGAP24), a RAC-specific GTPase activating protein, binds to activated ARF6 and is recruited to the plasma membrane. The aim of the present study was to demonstrate if ARHGAP24 is involved in the ARF6-mediated formation of pseudopods in breast carcinoma cells. MATERIALS AND METHODS: The formation of pseudopods induced by activated ARF6 was monitored using MDA-MB-231 human breast carcinoma cells. The effect of knockdown of endogenous ARHGAP24 by siRNA was examined. RESULTS: Knockdown of ARHGAP24 in MDA-MB-231 carcinoma cells increased the lifespan of pseudopods to retract, which resulted in increased length of pseudopods induced by activated ARF6. ARHGAP24 required a binding site of ARF6 to achieve ARF6-dependent actin remodeling. CONCLUSION: ARHGAP24 may regulate pseudopod formation downstream of activated ARF6 in MDA-MB-231 human breast carcinoma cells.

Cosolvent-Based Molecular Dynamics for Ensemble Docking: Practical Method for Generating Druggable Protein Conformations.

Protein flexibility is a major hurdle in current structure-based virtual screening (VS). In spite of the recent advances in high-performance computing, protein-ligand docking methods still demand tremendous computational cost to take into account the full degree of protein flexibility. In this context, ensemble docking has proven its utility and efficiency for VS studies, but it still needs a rational and efficient method to select and/or generate multiple protein conformations. Molecular dynamics (MD) simulations are useful to produce distinct protein conformations without abundant experimental structures. In this study, we present a novel strategy that makes use of cosolvent-based molecular dynamics (CMD) simulations for ensemble docking. By mixing small organic molecules into a solvent, CMD can stimulate dynamic protein motions and induce partial conformational changes of binding pocket residues appropriate for the binding of diverse ligands. The present method has been applied to six diverse target proteins and assessed by VS experiments using many actives and decoys of DEKOIS 2.0. The simulation results have revealed that the CMD is beneficial for ensemble docking. Utilizing cosolvent simulation allows the generation of druggable protein conformations, improving the VS performance compared with the use of a single experimental structure or ensemble docking by standard MD with pure water as the solvent.

AutoDock-GIST: Incorporating Thermodynamics of Active-Site Water into Scoring Function for Accurate Protein-Ligand Docking.

Water plays a significant role in the binding process between protein and ligand. However, the thermodynamics of water molecules are often underestimated, or even ignored, in protein-ligand docking. Usually, the free energies of active-site water molecules are substantially different from those of waters in the bulk region. The binding of a ligand to a protein causes a displacement of these waters from an active site to bulk, and this displacement process substantially contributes to the free energy change of protein-ligand binding. The free energy of active-site water molecules can be calculated by grid inhomogeneous solvation theory (GIST), using molecular dynamics (MD) and the trajectory of a target protein and water molecules. Here, we show a case study of the combination of GIST and a docking program and discuss the effectiveness of the displacing gain of unfavorable water in protein-ligand docking. We combined the GIST-based desolvation function with the scoring function of AutoDock4, which is called AutoDock-GIST. The proposed scoring function was assessed employing 51 ligands of coagulation factor Xa (FXa), and results showed that both scoring accuracy and docking success rate were improved. We also evaluated virtual screening performance of AutoDock-GIST using FXa ligands in the directory of useful decoys-enhanced (DUD-E), thus finding that the displacing gain of unfavorable water is effective for a successful docking campaign.

Protein-ligand docking using fitness learning-based artificial bee colony with proximity stimuli.

Protein-ligand docking is an optimization problem, which aims to identify the binding pose of a ligand with the lowest energy in the active site of a target protein. In this study, we employed a novel optimization algorithm called fitness learning-based artificial bee colony with proximity stimuli (FlABCps) for docking. Simulation results revealed that FlABCps improved the success rate of docking, compared to four state-of-the-art algorithms. The present results also showed superior docking performance of FlABCps, in particular for dealing with highly flexible ligands and proteins with a wide and shallow binding pocket.