Prediction of the binding mechanism of a selective DNA methyltransferase 3A inhibitor by molecular simulation.

DNA methylation is an epigenetic mechanism that introduces a methyl group at the C5 position of cytosine. This reaction is catalyzed by DNA methyltransferases (DNMTs) and is essential for the regulation of gene transcription. The DNMT1 and DNMT3A or -3B family proteins are known targets for the inhibition of DNA hypermethylation in cancer cells. A selective non-nucleoside DNMT3A inhibitor was developed that mimics S-adenosyl-l-methionine and deoxycytidine; however, the mechanism of selectivity is unclear because the inhibitor-protein complex structure determination is absent. Therefore, we performed docking and molecular dynamics simulations to predict the structure of the complex formed by the association between DNMT3A and the selective inhibitor. Our simulations, binding free energy decomposition analysis, structural isoform comparison, and residue scanning showed that Arg688 of DNMT3A is involved in the interaction with this inhibitor, as evidenced by its significant contribution to the binding free energy. The presence of Asn1192 at the corresponding residues in DNMT1 results in a loss of affinity for the inhibitor, suggesting that the interactions mediated by Arg688 in DNMT3A are essential for selectivity. Our findings can be applied in the design of DNMT-selective inhibitors and methylation-specific drug optimization procedures.

Site Identification and Next Choice Protocol for Hit-to-Lead Optimization.

Time efficiency and cost savings are major challenges in drug discovery and development. In this process, the hit-to-lead stage is expected to improve efficiency because it primarily exploits the trial-and-error approach of medicinal chemists. This study proposes a site identification and next choice (SINCHO) protocol to improve the hit-to-lead efficiency. This protocol selects an anchor atom and growth site pair, which is desirable for a hit-to-lead strategy starting from a 3D complex structure. We developed and fine-tuned the protocol using a training data set and assessed it using a test data set of the preceding hit-to-lead strategy. The protocol was tested for experimentally determined structures and molecular dynamics (MD) ensembles. The protocol had a high prediction accuracy for applying MD ensembles, owing to the consideration of protein flexibility. The SINCHO protocol enables medicinal chemists to visualize and modify functional groups in a hit-to-lead manner.

AAp-MSMD: Amino Acid Preference Mapping on Protein-Protein Interaction Surfaces Using Mixed-Solvent Molecular Dynamics.

Peptides have attracted much attention recently owing to their well-balanced properties as drugs against protein-protein interaction (PPI) surfaces. Molecular simulation-based predictions of binding sites and amino acid residues with high affinity to PPI surfaces are expected to accelerate the design of peptide drugs. Mixed-solvent molecular dynamics (MSMD), which adds probe molecules or fragments of functional groups as solutes to the hydration model, detects the binding hotspots and cryptic sites induced by small molecules. The detection results vary depending on the type of probe molecule; thus, they provide important information for drug design. For rational peptide drug design using MSMD, we proposed MSMD with amino acid residue probes, named amino acid probe-based MSMD (AAp-MSMD), to detect hotspots and identify favorable amino acid types on protein surfaces to which peptide drugs bind. We assessed our method in terms of hotspot detection at the amino acid probe level and binding free energy prediction with amino acid probes at the PPI site for the complex structure that formed the PPI. In hotspot detection, the max-spatial probability distribution map (max-PMAP) obtained from AAp-MSMD detected the PPI site, to which each type of amino acid can bind favorably. In the binding free energy prediction using amino acid probes, ΔGFE obtained from AAp-MSMD roughly estimated the experimental binding affinities from the structure-activity relationship. AAp-MSMD, with amino acid probes, provides estimated binding sites and favorable amino acid types at the PPI site of a target protein.

Discovery of a Hidden Trypanosoma cruzi Spermidine Synthase Binding Site and Inhibitors through In Silico, In Vitro, and X-ray Crystallography.

In drug discovery research, the selection of promising binding sites and understanding the binding mode of compounds are crucial fundamental studies. The current understanding of the proteins-ligand binding model extends beyond the simple lock and key model to include the induced-fit model, which alters the conformation to match the shape of the ligand, and the pre-existing equilibrium model, selectively binding structures with high binding affinity from a diverse ensemble of proteins. Although methods for detecting target protein binding sites and virtual screening techniques using docking simulation are well-established, with numerous studies reported, they only consider a very limited number of structures in the diverse ensemble of proteins, as these methods are applied to a single structure. Molecular dynamics (MD) simulation is a method for predicting protein dynamics and can detect potential ensembles of protein binding sites and hidden sites unobservable in a single-point structure. In this study, to demonstrate the utility of virtual screening with protein dynamics, MD simulations were performed on Trypanosoma cruzi spermidine synthase to obtain an ensemble of dominant binding sites with a high probability of existence. The structure of the binding site obtained through MD simulation revealed pockets in addition to the active site that was present in the initial structure. Using the obtained binding site structures, virtual screening of 4.8 million compounds by docking simulation, in vitro assays, and X-ray analysis was conducted, successfully identifying two hit compounds.

Obesity-induced metabolic imbalance allosterically modulates CtBP2 to inhibit PPAR-alpha transcriptional activity.

Maintenance of metabolic homeostasis is secured by metabolite-sensing systems, which can be overwhelmed by constant macronutrient surplus in obesity. Not only the uptake processes but also the consumption of energy substrates determine the cellular metabolic burden. We herein describe a novel transcriptional system in this context comprised of peroxisome proliferator-activated receptor alpha (PPARα), a master regulator for fatty acid oxidation, and C-terminal binding protein 2 (CtBP2), a metabolite-sensing transcriptional corepressor. CtBP2 interacts with PPARα to repress its activity, and the interaction is enhanced upon binding to malonyl-CoA, a metabolic intermediate increased in tissues in obesity and reported to suppress fatty acid oxidation through inhibition of carnitine palmitoyltransferase 1. In line with our preceding observations that CtBP2 adopts a monomeric configuration upon binding to acyl-CoAs, we determined that mutations in CtBP2 that shift the conformational equilibrium toward monomers increase the interaction between CtBP2 and PPARα. In contrast, metabolic manipulations that reduce malonyl-CoA decreased the formation of the CtBP2-PPARα complex. Consistent with these in vitro findings, we found that the CtBP2-PPARα interaction is accelerated in obese livers while genetic deletion of CtBP2 in the liver causes derepression of PPARα target genes. These findings support our model where CtBP2 exists primarily as a monomer in the metabolic milieu of obesity to repress PPARα, representing a liability in metabolic diseases that can be exploited to develop therapeutic approaches.

Pocket to concavity: a tool for the refinement of protein-ligand binding site shape from alpha spheres.

SUMMARY: Understanding the binding site of the target protein is essential for rational drug design. Pocket detection software predicts the ligand binding site of the target protein; however, the predicted protein pockets are often excessively estimated in comparison with the actual volume of the bound ligands. This study proposes a refinement tool for the pockets predicted by an alpha sphere-based approach, Pocket to Concavity (P2C). P2C is divided into two modes: Ligand-Free (LF) and Ligand-Bound (LB) modes. The LF mode provides the shape of the deep and druggable concavity where the core scaffold can bind. The LB mode searches the deep concavity around the bound ligand. Thus, P2C is useful for identifying and designing desirable compounds in Structure-Based Drug Design (SBDD). AVAILABILITY AND IMPLEMENTATION: Pocket to Concavity is freely available at https://github.com/genki-kudo/Pocket-to-Concavity. This tool is implemented in Python3 and Fpocket2.

Novel Calcium-Binding Ablating Mutations Induce Constitutive RET Activity and Drive Tumorigenesis.

Distinguishing oncogenic mutations from variants of unknown significance (VUS) is critical for precision cancer medicine. Here, computational modeling of 71,756 RET variants for positive selection together with functional assays of 110 representative variants identified a three-dimensional cluster of VUSs carried by multiple human cancers that cause amino acid substitutions in the calmodulin-like motif (CaLM) of RET. Molecular dynamics simulations indicated that CaLM mutations decrease interactions between Ca2+ and its surrounding residues and induce conformational distortion of the RET cysteine-rich domain containing the CaLM. RET-CaLM mutations caused ligand-independent constitutive activation of RET kinase by homodimerization mediated by illegitimate disulfide bond formation. RET-CaLM mutants possessed oncogenic and tumorigenic activities that could be suppressed by tyrosine kinase inhibitors targeting RET. This study identifies calcium-binding ablating mutations as a novel type of oncogenic mutation of RET and indicates that in silico-driven annotation of VUSs of druggable oncogenes is a promising strategy to identify targetable driver mutations. SIGNIFICANCE: Comprehensive proteogenomic and in silico analyses of a vast number of VUSs identify a novel set of oncogenic and druggable mutations in the well-characterized RET oncogene.

A unique peptide-based pharmacophore identifies an inhibitory compound against the A-subunit of Shiga toxin.

Shiga toxin (Stx), a major virulence factor of enterohemorrhagic Escherichia coli (EHEC), can cause fatal systemic complications. Recently, we identified a potent inhibitory peptide that binds to the catalytic A-subunit of Stx. Here, using biochemical structural analysis and X-ray crystallography, we determined a minimal essential peptide motif that occupies the catalytic cavity and is required for binding to the A-subunit of Stx2a, a highly virulent Stx subtype. Molecular dynamics simulations also identified the same motif and allowed determination of a unique pharmacophore for A-subunit binding. Notably, a series of synthetic peptides containing the motif efficiently inhibit Stx2a. In addition, pharmacophore screening and subsequent docking simulations ultimately identified nine Stx2a-interacting molecules out of a chemical compound database consisting of over 7,400,000 molecules. Critically, one of these molecules markedly inhibits Stx2a both in vitro and in vivo, clearly demonstrating the significance of the pharmacophore for identifying therapeutic agents against EHEC infection.

Inverse Mixed-Solvent Molecular Dynamics for Visualization of the Residue Interaction Profile of Molecular Probes.

To ensure efficiency in discovery and development, the application of computational technology is essential. Although virtual screening techniques are widely applied in the early stages of drug discovery research, the computational methods used in lead optimization to improve activity and reduce the toxicity of compounds are still evolving. In this study, we propose a method to construct the residue interaction profile of the chemical structure used in the lead optimization by performing "inverse" mixed-solvent molecular dynamics (MSMD) simulation. Contrary to constructing a protein-based, atom interaction profile, we constructed a probe-based, protein residue interaction profile using MSMD trajectories. It provides us the profile of the preferred protein environments of probes without co-crystallized structures. We assessed the method using three probes: benzamidine, catechol, and benzene. As a result, the residue interaction profile of each probe obtained by MSMD was a reasonable physicochemical description of the general non-covalent interaction. Moreover, comparison with the X-ray structure containing each probe as a ligand shows that the map of the interaction profile matches the arrangement of amino acid residues in the X-ray structure.

Chemosynthetic ethanolamine plasmalogen stimulates gonadotropin secretion from bovine gonadotrophs by acting as a potential GPR61 agonist.

Brain ethanolamine plasmalogens (EPls) are unique alkenylacyl-glycerophospholipids and the only recognized ligands of G-protein-coupled receptor 61 (GPR61), a newly identified receptor that colocalizes with GnRH receptors on gonadotrophs. As the chemical synthesis of EPl is challenging, only one chemosynthetic EPl, 1-(1Z-octadecenyl)- 2-oleoyl-sn-glycero-3-phosphoethanolamine (PLAPE; C18:0-C18:1), is commercially available. Therefore, we tested the hypothesis that PLAPE stimulates gonadotropin secretion from bovine gonadotrophs. We prepared anterior pituitary cells from healthy, post-pubertal heifers, cultured for 3.5 d, and then treated them with increasing concentrations (0, 0.5, 5, 50, or 500 pg/mL) of PLAPE for 5 mi, before either no treatment or GnRH stimulation. After 2 h, medium samples were harvested for FSH and LH assays. PLAPE (5-500 pg/mL) stimulated (P < 0.01) basal FSH and LH secretion, and such stimulation effects were inhibited by a SMAD pathway inhibitor. In the presence of GnRH, PLAPE at 0.5 and 5 pg/mL stimulated FSH and LH secretion (P < 0.01). However, a higher dose of PLAPE (500 pg/mL) suppressed GnRH-induced FSH and LH, and such suppressive effects were inhibited by an ERK pathway inhibitor. PLAPE stimulated gonadotropin secretion in the presence of EPls extracted from the brains of young heifers, but not old cows. Additionally, we performed in silico molecular-docking simulations using the deep-learning algorithm, AlphaFold2. The simulations revealed the presence of three binding sites for PLAPE in the three-dimensional structural model of GPR61. In conclusion, PLAPE stimulated gonadotropin secretion from bovine gonadotrophs and might act as a chemosynthetic agonist of GPR61.

Molecular action of larvicidal flavonoids on ecdysteroidogenic glutathione S-transferase Noppera-bo in Aedes aegypti.

BACKGROUND: Mosquito control is a crucial global issue for protecting the human community from mosquito-borne diseases. There is an urgent need for the development of selective and safe reagents for mosquito control. Flavonoids, a group of chemical substances with variable phenolic structures, such as daidzein, have been suggested as potential mosquito larvicides with less risk to the environment. However, the mode of mosquito larvicidal action of flavonoids has not been elucidated. RESULTS: Here, we report that several flavonoids, including daidzein, inhibit the activity of glutathione S-transferase Noppera-bo (Nobo), an enzyme used for the biosynthesis of the insect steroid hormone ecdysone, in the yellow fever mosquito Aedes aegypti. The crystal structure of the Nobo protein of Ae. aegypti (AeNobo) complexed with the flavonoids and its molecular dynamics simulation revealed that Glu113 forms a hydrogen bond with the flavonoid inhibitors. Consistent with this observation, substitution of Glu113 with Ala drastically reduced the inhibitory activity of the flavonoids against AeNobo. Among the identified flavonoid-type inhibitors, desmethylglycitein (4',6,7-trihydroxyisoflavone) exhibited the highest inhibitory activity in vitro. Moreover, the inhibitory activities of the flavonoids correlated with the larvicidal activity, as desmethylglycitein suppressed Ae. aegypti larval development more efficiently than daidzein. CONCLUSION: Our study demonstrates the mode of action of flavonoids on the Ae. aegypti Nobo protein at the atomic, enzymatic, and organismal levels.

The transcriptional corepressor CtBP2 serves as a metabolite sensor orchestrating hepatic glucose and lipid homeostasis.

Biological systems to sense and respond to metabolic perturbations are critical for the maintenance of cellular homeostasis. Here we describe a hepatic system in this context orchestrated by the transcriptional corepressor C-terminal binding protein 2 (CtBP2) that harbors metabolite-sensing capabilities. The repressor activity of CtBP2 is reciprocally regulated by NADH and acyl-CoAs. CtBP2 represses Forkhead box O1 (FoxO1)-mediated hepatic gluconeogenesis directly as well as Sterol Regulatory Element-Binding Protein 1 (SREBP1)-mediated lipogenesis indirectly. The activity of CtBP2 is markedly defective in obese liver reflecting the metabolic perturbations. Thus, liver-specific CtBP2 deletion promotes hepatic gluconeogenesis and accelerates the progression of steatohepatitis. Conversely, activation of CtBP2 ameliorates diabetes and hepatic steatosis in obesity. The structure-function relationships revealed in this study identify a critical structural domain called Rossmann fold, a metabolite-sensing pocket, that is susceptible to metabolic liabilities and potentially targetable for developing therapeutic approaches.

Non-steroidal inhibitors of Drosophila melanogaster steroidogenic glutathione S-transferase Noppera-bo.

Insect growth regulators (IGRs) can be developed by elucidating the molecular mechanisms of insect-specific biological events. Because insect molting, and metamorphosis are controlled by ecdysteroids, their biosynthetic pathways can serve as targets for IGR development. The glutathione S-transferase Noppera-bo (Nobo), which is conserved in dipteran and lepidopteran species, plays an essential role in ecdysteroid biosynthesis. Our previous study using 17ß-estradiol as a molecular probe revealed that Asp113 of Drosophila melanogaster Nobo (DmNobo) is essential for its biological function. However, to develop IGRs with a greater Nobo inhibitory activity than 17ß-estradiol, further structural information is warranted. Here, we report five novel non-steroidal DmNobo inhibitors. Analysis of crystal structures of complexes revealed that DmNobo binds these inhibitors in an Asp113-independent manner. Among amino acid residues at the substrate-recognition site, conformation of conserved Phe39 was dynamically altered upon inhibitor binding. Therefore, these inhibitors can serve as seed compounds for IGR development.

Identification of key interactions between SARS-CoV-2 main protease and inhibitor drug candidates.

The number of cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (COVID-19) has reached over 114,000. SARS-CoV-2 caused a pandemic in Wuhan, China, in December 2019 and is rapidly spreading globally. It has been reported that peptide-like anti-HIV-1 drugs are effective against SARS-CoV Main protease (Mpro). Due to the close phylogenetic relationship between SARS-CoV and SARS-CoV-2, their main proteases share many structural and functional features. Thus, these drugs are also regarded as potential drug candidates targeting SARS-CoV-2 Mpro. However, the mechanism of action of SARS-CoV-2 Mpro at the atomic-level is unknown. In the present study, we revealed key interactions between SARS-CoV-2 Mpro and three drug candidates by performing pharmacophore modeling and 1 µs molecular dynamics (MD) simulations. His41, Gly143, and Glu166 formed interactions with the functional groups that were common among peptide-like inhibitors in all MD simulations. These interactions are important targets for potential drugs against SARS-CoV-2 Mpro.

An integrated approach to unravel a crucial structural property required for the function of the insect steroidogenic Halloween protein Noppera-bo.

Ecdysteroids are the principal steroid hormones essential for insect development and physiology. In the last 18 years, several enzymes responsible for ecdysteroid biosynthesis encoded by Halloween genes were identified and genetically and biochemically characterized. However, the tertiary structures of these proteins have not yet been characterized. Here, we report the results of an integrated series of in silico, in vitro, and in vivo analyses of the Halloween GST protein Noppera-bo (Nobo). We determined crystal structures of Drosophila melanogaster Nobo (DmNobo) complexed with GSH and 17ß-estradiol, a DmNobo inhibitor. 17ß-Estradiol almost fully occupied the putative ligand-binding pocket and a prominent hydrogen bond formed between 17ß-estradiol and Asp-113 of DmNobo. We found that Asp-113 is essential for 17ß-estradiol-mediated inhibition of DmNobo enzymatic activity, as 17ß-estradiol did not inhibit and physically interacted less with the D113A DmNobo variant. Asp-113 is highly conserved among Nobo proteins, but not among other GSTs, implying that this residue is important for endogenous Nobo function. Indeed, a homozygous nobo allele with the D113A substitution exhibited embryonic lethality and an undifferentiated cuticle structure, a phenocopy of complete loss-of-function nobo homozygotes. These results suggest that the nobo family of GST proteins has acquired a unique amino acid residue that appears to be essential for binding an endogenous sterol substrate to regulate ecdysteroid biosynthesis. To the best of our knowledge, ours is the first study describing the structural characteristics of insect steroidogenic Halloween proteins. Our findings provide insights relevant for applied entomology to develop insecticides that specifically inhibit ecdysteroid biosynthesis.

A prospective compound screening contest identified broader inhibitors for Sirtuin 1.

Potential inhibitors of a target biomolecule, NAD-dependent deacetylase Sirtuin 1, were identified by a contest-based approach, in which participants were asked to propose a prioritized list of 400 compounds from a designated compound library containing 2.5 million compounds using in silico methods and scoring. Our aim was to identify target enzyme inhibitors and to benchmark computer-aided drug discovery methods under the same experimental conditions. Collecting compound lists derived from various methods is advantageous for aggregating compounds with structurally diversified properties compared with the use of a single method. The inhibitory action on Sirtuin 1 of approximately half of the proposed compounds was experimentally accessed. Ultimately, seven structurally diverse compounds were identified.

Molecular Dynamics Simulation reveals the mechanism by which the Influenza Cap-dependent Endonuclease acquires resistance against Baloxavir marboxil.

Baloxavir marboxil (BXM), an antiviral drug for influenza virus, inhibits RNA replication by binding to RNA replication cap-dependent endonuclease (CEN) of influenza A and B viruses. Although this drug was only approved by the FDA in October 2018, drug resistant viruses have already been detected from clinical trials owing to an I38 mutation of CEN. To investigate the reduction of drug sensitivity by the I38 mutant variants, we performed a molecular dynamics (MD) simulation on the CEN-BXM complex structure to analyze variations in the mode of interaction. Our simulation results suggest that the side chain methyl group of I38 in CEN engages in a CH-pi interaction with the aromatic ring of BXM. This interaction is abolished in various I38 mutant variants. Moreover, MD simulation on various mutation models and binding free energy prediction by MM/GBSA method suggest that the I38 mutation precludes any interaction with the aromatic ring of BXA and thereby reduces BXA sensitivity.

Parallel cascade selection molecular dynamics to screen for protein complexes generated by rigid docking.

We propose a flexible docking simulation based on parallel cascade selection molecular dynamics (PaCS-MD) as a post-processing treatment after a rigid docking simulation. PaCS-MD has been proposed as an enhanced sampling method for generating structural transition pathways from a given reactant to a product. The PaCS-MD cycle consists of the following two steps: (1) selections of important initial structures and (2) their conformational resampling from the selected initial structures. By repeating the conformational resampling from the important initial structures, structural transitions from the reactant to the product are gradually promoted. In the present flexible docking simulation, decoys (protein complexes) are generated by the rigid docking simulation a priori and employed as products of PaCS-MD. Then PaCS-MD is applied to reproduce association processes to the decoys from a reactant (completely separated proteins). To judge whether PaCS-MD found the association processes or not, the root-mean-square deviation measured from decoy (RMSDdecoy) was defined and monitored during the PaCS-MD cycles. By checking the RMSDdecoy values, a set of decoys is screened as a non-near native protein complex. In more detail, PaCS-MD detects near native protein complexes from the generated decoys by imposing a threshold (cutoff) for RMSDdecoy, i.e. RMSDdecoy < cutoff. As a demonstration, the present flexible docking addressed dimerization processes of K48-linked ubiquitin dimer without a covalent bond between its monomers. Finally, PaCS-MD screened out non-near native protein complexes from decoys generated by a rigid docking simulation.

Temperature-pressure shuffling outlier flooding method enhances the conformational sampling of proteins.

Outlier flooding method (OFLOOD) is an efficient conformational sampling method developed by the authors. In the present study, to further enhance the conformational sampling efficiency, a set of parameters (temperatures and pressures) specified as inputs in the original OFLOOD were shuffled before restarting the short-time molecular dynamics (MD) simulations. Because of the diversity of these parameters, it was confirmed that the extended OFLOOD becomes superior to the original one in finding the folding pathways of Trp-cage. © 2019 Wiley Periodicals, Inc.

Compound property enhancement by virtual compound synthesis.

During drug discovery, drug candidates are narrowed down over several steps to develop pharmaceutical products. The theoretical chemical space in such steps is estimated to be [Formula: see text]. To cover that space, extensive virtual compound libraries have been developed; however, the compilation of extensive libraries comes at large computational cost. Thus, to reduce the computational cost, researchers have constructed custom-made virtual compound libraries that focus on target diseases. In this study, we develop a system that generates virtual compound libraries from input compounds. When a user inputs a compound, the system recursively applies virtual synthetic reaction rules to the compound to improve its properties. The synthetic pathway can also be traced by the user because the reaction rules in this system are based on real organic synthesis reactions. This system has useful functions for effective drug design, such as structural preservation, allowing the substructures necessary for potency to be maintained. In this paper, to confirm the effect of directional reaction sets, we applied the reaction sets to 100 compounds. Moreover, to confirm that the system can reproduce real synthetic pathways, the synthetic pathways of Ibuprofen and Ofloxacin were explored by inputting isobutyl benzene and 7,8-difluoro-2,3-dihydro-3-methyl-4H-benzoxazine. This application is available at the following URL: http://enh.sekijima-lab.org .