Fast, accurate ranking of engineered proteins by target-binding propensity using structure modeling.

Deep-learning-based methods for protein structure prediction have achieved unprecedented accuracy, yet their utility in the engineering of protein-based binders remains constrained due to a gap between the ability to predict the structures of candidate proteins and the ability toprioritize proteins by their potential to bind to a target. To bridge this gap, we introduce Automated Pairwise Peptide-Receptor Analysis for Screening Engineered proteins (APPRAISE), a method for predicting the target-binding propensity of engineered proteins. After generating structural models of engineered proteins competing for binding to a target using an established structure prediction tool such as AlphaFold-Multimer or ESMFold, APPRAISE performs a rapid (under 1 CPU second per model) scoring analysis that takes into account biophysical and geometrical constraints. As proof-of-concept cases, we demonstrate that APPRAISE can accurately classify receptor-dependent vs. receptor-independent adeno-associated viral vectors and diverse classes of engineered proteins such as miniproteins targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike, nanobodies targeting a G-protein-coupled receptor, and peptides that specifically bind to transferrin receptor or programmed death-ligand 1 (PD-L1). APPRAISE is accessible through a web-based notebook interface using Google Colaboratory (https://tiny.cc/APPRAISE). With its accuracy, interpretability, and generalizability, APPRAISE promises to expand the utility of protein structure prediction and accelerate protein engineering for biomedical applications.

Repurposed drugs in combinations exert additive anti-chikungunya virus activity: an in-vitro study.

Chikungunya virus (CHIKV) infection causes chikungunya, a viral disease that currently has no specific antiviral treatment. Several repurposed drug candidates have been investigated for the treatment of the disease. In order to improve the efficacy of the known drugs, combining drugs for treatment is a promising approach. The current study was undertaken to explore the antiviral activity of a combination of repurposed drugs that were reported to have anti-CHIKV activity. We explored the effect of different combinations of six effective drugs (2-fluoroadenine, emetine, lomibuvir, enalaprilat, metyrapone and resveratrol) at their non-toxic concentrations against CHIKV under post infection treatment conditions in Vero cells. Focus-forming unit assay, real time RT-PCR, immunofluorescence assay, and western blot were used to determine the virus titre. The results revealed that the combination of 2-fluoroadenine with either metyrapone or emetine or enalaprilat exerted inhibitory activity against CHIKV under post-infection treatment conditions. The effect of these drug combinations was additive in nature compared to the effect of the individual drugs. The results suggest an additive anti-viral effect of these drug combinations against CHIKV. The findings could serve as an outline for the development of an innovative therapeutic approach in the future to treat CHIKV-infected patients.

Identification of two novel chemical classes of Autotaxin (ATX) inhibitors using Enalos Asclepios KNIME nodes.

Autotaxin is a secreted lysophospholipase D which is a member of the ectonucleotide pyrophosphatase/phosphodiesterase family converting extracellular lysophosphatidylcholine and other non-choline lysophospholipids, such as lysophosphatidylethanolamine and lysophosphatidylserine, to the lipid mediator lysophosphatidic acid. Autotaxin is implicated in various fibroproliferative diseases including interstitial lung diseases, such as idiopathic pulmonary fibrosis and hepatic fibrosis, as well as in cancer. In this study, we present an effort of identifying ATX inhibitors that bind to allosteric ATX binding sites using the Enalos Asclepios KNIME Node. All the available PDB crystal structures of ATX were collected, prepared, and aligned. Visual examination of these structures led to the identification of four crystal structures of human ATX co-crystallized with four known inhibitors. These inhibitors bind to five binding sites with five different binding modes. These five binding sites were thereafter used to virtually screen a compound library of 14,000 compounds to identify molecules that bind to allosteric sites. Based on the binding mode and interactions, the docking score, and the frequency that a compound comes up as a top-ranked among the five binding sites, 24 compounds were selected for in vitro testing. Finally, two compounds emerged with inhibitory activity against ATX in the low micromolar range, while their mode of inhibition and binding pattern were also studied. The two derivatives identified herein can serve as "hits" towards developing novel classes of ATX allosteric inhibitors.

Development of Small-Molecule Anti-HIV-1 Agents Targeting HIV-1 Capsid Proteins.

The capsid of human immunodeficiency virus type 1 (HIV-1) forms a conical structure by assembling oligomers of capsid (CA) proteins and is a virion shell that encapsulates viral RNA. The inhibition of the CA function could be an appropriate target for suppression of HIV-1 replication because the CA proteins are highly conserved among many strains of HIV-1, and the drug targeting CA, lenacapavir, has been clinically developed by Gilead Sciences, Inc. Interface hydrophobic interactions between two CA molecules via the Trp184 and Met185 residues in the CA sequence are indispensable for conformational stabilization of the CA multimer. Our continuous studies found two types of small molecules with different scaffolds, MKN-1 and MKN-3, designed by in silico screening as a dipeptide mimic of Trp184 and Met185 have significant anti-HIV-1 activity. In the present study, MKN-1 derivatives have been designed and synthesized. Their structure-activity relationship studies found some compounds having potent anti-HIV activity. The present results should be useful in the design of novel CA-targeting molecules with anti-HIV activity.

Identification of a Histone Deacetylase 8 Inhibitor through Drug Screenings Based on Machine Learning.

Histone deacetylase 8 (HDAC8) is a zinc-dependent HDAC that catalyzes the deacetylation of nonhistone proteins. It is involved in cancer development and HDAC8 inhibitors are promising candidates as anticancer agents. However, most reported HDAC8 inhibitors contain a hydroxamic acid moiety, which often causes mutagenicity. Therefore, we used machine learning for drug screening and attempted to identify non-hydroxamic acids as HDAC8 inhibitors. In this study, we established a prediction model based on the random forest (RF) algorithm for screening HDAC8 inhibitors because it exhibited the best predictive accuracy in the training dataset, including data generated by the synthetic minority over-sampling technique (SMOTE). Using the trained RF-SMOTE model, we screened the Osaka University library for compounds and selected 50 virtual hits. However, the 50 hits in the first screening did not show HDAC8-inhibitory activity. In the second screening, using the RF-SMOTE model, which was established by retraining the dataset including 50 inactive compounds, we identified non-hydroxamic acid 12 as an HDAC8 inhibitor with an IC50 of 842 nM. Interestingly, its IC50 values for HDAC1 and HDAC3-inhibitory activity were 38 and 12 µM, respectively, showing that compound 12 has high HDAC8 selectivity. Using machine learning, we expanded the chemical space for HDAC8 inhibitors and identified non-hydroxamic acid 12 as a novel HDAC8 selective inhibitor.

In Silico Screening and Experimental Verification of Near-Infrared-Emissive Two-Boron-Doped Polycyclic Aromatic Hydrocarbons.

Embedding two boron atoms into a polycyclic aromatic hydrocarbon (PAH) leads to the formation of a neutral analogue that is isoelectronic to the corresponding dicationic PAH skeleton, which can significantly alter its electronic structure. Based on this concept, we explore herein the identification of near-infrared (NIR)-emissive PAHs with the aid of an in silico screening method. Using perylene as the PAH scaffold, we embedded two boron atoms and fused two thiophene rings to it. Based on this design concept, all possible structures (ca. 2500 entities) were generated using a comprehensive structure generator. Time-dependent DFT calculations were conducted on all these structures, and promising candidates were extracted based on the vertical excitation energy, transition dipole moment, and atomization energy per bond. One of the extracted dithieno-diboraperylene candidates was synthesized and indeed exhibited emission at 724 nm with a quantum yield of 0.40 in toluene, demonstrating the validity of this screening method. This modification was further applied to other PAHs, and a series of thienobora-modified PAHs was synthesized.

ENNAVIA is a novel method which employs neural networks for antiviral and anti-coronavirus activity prediction for therapeutic peptides.

Viruses represent one of the greatest threats to human health, necessitating the development of new antiviral drug candidates. Antiviral peptides often possess excellent biological activity and a favourable toxicity profile, and therefore represent a promising field of novel antiviral drugs. As the quantity of sequencing data grows annually, the development of an accurate in silico method for the prediction of peptide antiviral activities is important. This study leverages advances in deep learning and cheminformatics to produce a novel sequence-based deep neural network classifier for the prediction of antiviral peptide activity. The method outperforms the existent best-in-class, with an external test accuracy of 93.9%, Matthews correlation coefficient of 0.87 and an Area Under the Curve of 0.93 on the dataset of experimentally validated peptide activities. This cutting-edge classifier is available as an online web server at https://research.timmons.eu/ennavia, facilitating in silico screening and design of peptide antiviral drugs by the wider research community.

Pharmacoinformatics and molecular dynamics simulation-based phytochemical screening of neem plant (Azadiractha indica) against human cancer by targeting MCM7 protein.

Minichromosome maintenance complex component 7 (MCM7) belongs to the minichromosome maintenance family that is important for the initiation of eukaryotic DNA replication. Overexpression of the MCM7 protein is relative to cellular proliferation and responsible for aggressive malignancy in various cancers. Mechanistically, inhibition of MCM7 significantly reduces the cellular proliferation associated with cancer. To date, no effective small molecular candidate has been identified that can block the progression of cancer induced by the MCM7 protein. Therefore, the study has been designed to identify small molecular-like natural drug candidates against aggressive malignancy associated with various cancers by targeting MCM7 protein. To identify potential compounds against the targeted protein a comprehensive in silico drug design including molecular docking, ADME (Absorption, Distribution, Metabolism and Excretion), toxicity, and molecular dynamics (MD) simulation approaches has been applied. Seventy phytochemicals isolated from the neem tree (Azadiractha indica) were retrieved and screened against MCM7 protein by using the molecular docking simulation method, where the top four compounds have been chosen for further evaluation based on their binding affinities. Analysis of ADME and toxicity properties reveals the efficacy and safety of the selected four compounds. To validate the stability of the protein-ligand complex structure MD simulations approach has also been performed to the protein-ligand complex structure, which confirmed the stability of the selected three compounds including CAS ID:105377-74-0, CID:12308716 and CID:10505484 to the binding site of the protein. In the study, a comprehensive data screening process has performed based on the docking, ADMET properties, and MD simulation approaches, which found a good value of the selected four compounds against the targeted MCM7 protein and indicates as a promising and effective human anticancer agent.

Therapeutic strategies for identifying small molecules against prion diseases.

Prion diseases are fatal neurodegenerative disorders, for which there are no effective therapeutic and diagnostic agents. The main pathological hallmark has been identified as conformational changes of the cellular isoform prion protein (PrPC) to a misfolded isoform of the prion protein (PrPSc). Targeting PrPC and its conversion to PrPSc is still the central dogma in prion drug discovery, particularly in in silico and in vitro screening endeavors, leading to the identification of many small molecules with therapeutic potential. Nonetheless, multiple pathological targets are critically involved in the intricate pathogenesis of prion diseases. In this context, multi-target-directed ligands (MTDLs) emerge as valuable therapeutic approach for their potential to effectively counteract the complex etiopathogenesis by simultaneously modulating multiple targets. In addition, diagnosis occurs late in the disease process, and consequently a successful therapeutic intervention cannot be provided. In this respect, small molecule theranostics, which combine imaging and therapeutic properties, showed tremendous potential to cure and diagnose in vivo prion diseases. Herein, we review the major advances in prion drug discovery, from anti-prion small molecules identified by means of in silico and in vitro screening approaches to two rational strategies, namely MTDLs and theranostics, that have led to the identification of novel compounds with an expanded anti-prion profile.

A polo-like kinase inhibitor identified by computational repositioning attenuates pulmonary fibrosis.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a fatal fibrotic lung disease with few effective therapeutic options. Recently, drug repositioning, which involves identifying novel therapeutic potentials for existing drugs, has been popularized as a new approach for the development of novel therapeutic reagents. However, this approach has not yet been fully utilized in the field of pulmonary fibrosis. METHODS: The present study identified novel therapeutic options for pulmonary fibrosis using a systematic computational approach for drug repositioning based on integration of public gene expression signatures of drug and diseases (in silico screening approach). RESULTS: Among the top compounds predicted to be therapeutic for IPF by the in silico approach, we selected BI2536, a polo-like kinase (PLK) 1/2 inhibitor, as a candidate for treating pulmonary fibrosis using an in silico analysis. However, BI2536 accelerated mortality and weight loss rate in an experimental mouse model of pulmonary fibrosis. Because immunofluorescence staining revealed that PLK1 expression was dominant in myofibroblasts while PLK2 expression was dominant in lung epithelial cells, we next focused on the anti-fibrotic effect of the selective PLK1 inhibitor GSK461364. Consequently, GSK461364 attenuated pulmonary fibrosis with acceptable mortality and weight loss in mice. CONCLUSIONS: These findings suggest that targeting PLK1 may be a novel therapeutic approach for pulmonary fibrosis by inhibiting lung fibroblast proliferation without affecting lung epithelial cells. In addition, while in silico screening is useful, it is essential to fully determine the biological activities of candidates by wet-lab validation studies.

Shotgun metagenomics and computational profiling of the plastisphere microbiome: unveiling the potential of enzymatic production and plastic degradation.

Plastic pollution is one of the most resilient types of pollution and is considered a global environmental threat, particularly in the marine environment. This study aimed to identify plastic-degrading bacteria from the plastisphere and their pharmaceutical and therapeutic potential. We collected samples from soil and aquatic plastisphere to identify the bacterial communities using shotgun metagenomic sequencing and bioinformatic tools. Results showed that the microbiome comprised 93% bacteria, 0.29% archaea, and 3.87% unidentified microbes. Of these 93% of bacteria, 54% were Proteobacteria, 23.9% were Firmicutes, 13% were Actinobacteria, and 2.1% were other phyla. We found that the plastisphere microbiome was involved in degrading synthetic and polyhydroxy alkanoate (PHA) plastic, biosurfactant production, and can thrive under high temperatures. However, no association existed between thermophiles, synthetic plastic or PHA degraders, and biosurfactant-producing bacterial species except for Pseudomonas. Other plastisphere inhabiting plastic degrading microbes include Streptomyces, Bacillus, Achromobacter, Azospirillum, Bacillus, Brevundimonas, Clostridium, Paenibacillus, Rhodococcus, Serratia, Staphylococcus, Thermobifida, and Thermomonospora. However, the plastisphere microbiome showed potential for producing secondary metabolites that were found to act as anticancer, antitumor, anti-inflammatory, antimicrobial, and enzyme stabilizers. These results revealed that the plastisphere microbiome upholds clinical and environmental significance as it can open future portals in a multi-directional way.

Novel drug transporter substrates identification: An innovative approach based on metabolomic profiling, in silico ligand screening and biological validation.

Solute carrier (SLC) transport proteins are fundamental for the translocation of endogenous compounds and drugs across membranes, thus playing a critical role in disease susceptibility and drug response. Because only a limited number of transporter substrates are currently known, the function of a large number of SLC transporters is elusive. Here, we describe the proof-of-concept of a novel strategy to identify SLC transporter substrates exemplarily for the proton-coupled peptide transporter (PEPT) 2 (SLC15A2) and multidrug and toxin extrusion (MATE) 1 transporter (SLC47A1), which are important renal transporters of drug reabsorption and excretion, respectively. By combining metabolomic profiling of mice with genetically-disrupted transporters, in silico ligand screening and in vitro transport studies for experimental validation, we identified nucleobases and nucleoside-derived anticancer and antiviral agents (flucytosine, cytarabine, gemcitabine, capecitabine) as novel drug substrates of the MATE1 transporter. Our data confirms the successful applicability of this new approach for the identification of transporter substrates in general, which may prove particularly relevant in drug research.

Design, synthesis, structure-activity relationship studies, and evaluation of novel GLS1 inhibitors.

Glutaminase converts glutamine into glutamic acid and has two isoforms: glutaminase 1 (GLS1) and glutaminase 2 (GLS2). GLS1 is overexpressed in several tumors, and research to develop glutaminase inhibitors as antitumor drugs is currently underway. The present study examined candidate GLS1 inhibitors using in silico screening and attempted to synthesize novel GLS1 inhibitors and assess their GLS1 inhibitory activities in a mouse kidney extract and against recombinant mouse and human GLS1. Novel compounds were synthesized using compound C as the lead compound, and their GLS1 inhibitory activities were evaluated using the mouse kidney extract. Among the derivatives tested, the trans-4-hydroxycyclohexylamide derivative 2j exhibited the strongest inhibitory activity. We also assessed the GLS1 inhibitory activities of the derivatives 2j, 5i, and 8a against recombinant mouse and human GLS1. The derivatives 5i and 8a significantly decreased the production of glutamic acid at 10 mM. In conclusion, we herein identified two compounds that exhibited GLS1 inhibitory activities with equal potencies as known GLS1 inhibitors. These results will contribute to the development of effective novel GLS1 inhibitors with more potent inhibitory activity.

In silico drug repurposing for coronavirus (COVID-19): screening known HCV drugs against the SARS-CoV-2 spike protein bound to angiotensin-converting enzyme 2 (ACE2) (6M0J).

In this study, FDA-approved HCV antiviral drugs and their structural analogues-several of them in clinical trials-were tested for their inhibitory properties toward the SARS-CoV-2 spike protein bound to angiotensin-converting enzyme 2 (6M0J) using a virtual screening approach and computational chemistry methods. The most stable structures and the corresponding binding affinities of thirteen such antiviral compounds were obtained. Frontier molecular orbital theory, global reactivity descriptors, molecular docking calculations and electrostatic potential analysis were used to hypothesize the bioactivity of these drugs against 6M0J. It is found that an increased affinity for the protein is shown by inhibitors with large compound volume, relatively higher electrophilicity index, aromatic rings and heteroatoms that participate in hydrogen bonding. Among the tested drugs, four compounds 10-13 showed excellent results-binding affinities - 11.2 to - 11.5 kcal mol-1. These four top scoring compounds may act as lead compounds for further experimental validation, clinical trials and even for the development of more potent antiviral agents against the SARS-CoV-2. Approved HCV drugs and analogues were tested for their bioactivity towards the SARS-CoV-2 (6M0J) using virtual screening, ESP and MD analysis.

Combined in silico strategy for repurposing DrugBank entries towards introducing potential anti-SARS-CoV-2 drugs.

The emergence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) from China in December 2019 led to the coronavirus disorder 2019 pandemic, which has affected tens of millions of humans worldwide. Various in silico research via bio-cheminformatics methods were performed to examine the efficiency of a range of repurposed approved drugs with a new role as anti-SARS-CoV-2 drugs. The current study has been performed to screen the approved drugs in the DrugBank database based on a novel bioinformatics/cheminformatics strategy to repurpose available approved drugs towards introducing them as a possible anti-SARS-CoV-2 drug. As a result, 96 approved drugs with the best docking scores passed through several relevant filters were presented as the candidate drugs with potential novel antiviral activities against the SARS-CoV-2 virus.

Structure-Based Design and Pharmacophore-Based Virtual Screening of Combinatorial Library of Triclosan Analogues Active against Enoyl-Acyl Carrier Protein Reductase of Plasmodium falciparum with Favourable ADME Profiles.

Cost-effective therapy of neglected and tropical diseases such as malaria requires everlasting drug discovery efforts due to the rapidly emerging drug resistance of the plasmodium parasite. We have carried out computational design of new inhibitors of the enoyl-acyl carrier protein reductase (ENR) of Plasmodium falciparum (PfENR) using computer-aided combinatorial and pharmacophore-based molecular design. The Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) complexation QSAR model was developed for triclosan-based inhibitors (TCL) and a significant correlation was established between the calculated relative Gibbs free energies of complex formation (∆∆Gcom) between PfENR and TCL and the observed inhibitory potencies of the enzyme (IC50exp) for a training set of 20 known TCL analogues. Validation of the predictive power of the MM-PBSA QSAR model was carried out with the generation of 3D QSAR pharmacophore (PH4). We obtained a reasonable correlation between the relative Gibbs free energy of complex formation ∆∆Gcom and IC50exp values, which explained approximately 95% of the PfENR inhibition data: pIC50exp=-0.0544×∆∆Gcom+6.9336,R2=0.95. A similar agreement was established for the PH4 pharmacophore model of the PfENR inhibition (pIC50exp=0.9754×pIC50pre+0.1596, R2=0.98). Analysis of enzyme-inhibitor binding site interactions suggested suitable building blocks to be used in a virtual combinatorial library of 33,480 TCL analogues. Structural information derived from the complexation model and the PH4 pharmacophore guided us through in silico screening of the virtual combinatorial library of TCL analogues to finally identify potential new TCL inhibitors effective at low nanomolar concentrations. Virtual screening of the library by PfENR-PH4 led to a predicted IC50pre value for the best inhibitor candidate as low as 1.9 nM. Finally, the stability of PfENR-TCLx complexes and the flexibility of the active conformation of the inhibitor for selected top-ranking TCL analogues were checked with the help of molecular dynamics. This computational study resulted in a set of proposed new potent inhibitors with predicted antimalarial effects and favourable pharmacokinetic profiles that act on a novel pharmacological target, PfENR.

In Silico and In Vitro Search for Dual Inhibitors of the Trypanosoma brucei and Leishmania major Pteridine Reductase 1 and Dihydrofolate Reductase.

The parasites Trypanosoma brucei (Tb) and Leishmania major (Lm) cause the tropical diseases sleeping sickness, nagana, and cutaneous leishmaniasis. Every year, millions of humans, as well as animals, living in tropical to subtropical climates fall victim to these illnesses' health threats. The parasites' frequent drug resistance and widely spread natural reservoirs heavily impede disease prevention and treatment. Due to pteridine auxotrophy, trypanosomatid parasites have developed a peculiar enzyme system consisting of dihydrofolate reductase-thymidylate synthase (DHFR-TS) and pteridine reductase 1 (PTR1) to support cell survival. Extending our previous studies, we conducted a comparative study of the T. brucei (TbDHFR, TbPTR1) and L. major (LmDHFR, LmPTR1) enzymes to identify lead structures with a dual inhibitory effect. A pharmacophore-based in silico screening of three natural product databases (approximately 4880 compounds) was performed to preselect possible inhibitors. Building on the in silico results, the inhibitory potential of promising compounds was verified in vitro against the recombinant DHFR and PTR1 of both parasites using spectrophotometric enzyme assays. Twelve compounds were identified as dual inhibitors against the Tb enzymes (0.2 µM < IC50 < 85.1 µM) and ten against the respective Lm enzymes (0.6 µM < IC50 < 84.5 µM). These highly promising results may represent the starting point for the future development of new leads and drugs utilizing the trypanosomatid pteridine metabolism as a target.

ACE-Inhibitory Peptides Identified from Quinoa Bran Glutelin-2 Hydrolysates: In Silico Screening and Characterization, Inhibition Mechanisms of ACE, Coordination with Zinc Ions, and Stability.

To obtain Angiotensin-I-Converting Enzyme (ACE) inhibition peptides with Zn-chelating capacity, quinoa bran glutelin-2 hydrolysates (QBGH) by Flavourzyme and Papain were subjected to Sephadex G-15 gel chromatography, reverse phase-high liquid performance chromatography and UPLC-ESI-MS/MS analysis. Four oligopeptides including GGGSGH, EAGAE, AGGGAGGG and AVPKPS were identified. Of these, only the hexapeptide AVPKPS had both ACE-inhibitory activity (IC50: 123.13 µmol/L) and Zn-chelating ability (17.36 mg/g). Molecular docking showed AVPKPS could bind with active residues Glu384 and Ala354 (both belong to the central S1 pocket of ACE including) through short hydrogen bond and hydrophobic interactions, respectively. Inhibition kinetics verified that AVPKPS was a competitive inhibitor of ACE. Moreover, AVPKPS can affect the zinc tetrahedral coordination in ACE through binding with residues His387 and His383. Fourier-transform infrared spectroscopy analysis demonstrated that the amino and carboxyl groups of AVPKPS were the main chelating sites for zinc ions. Under the gastrointestinal digestion, the ACE inhibition capacity of AVPKPS was relatively stable, and the zinc solubility of AVPKPS-zinc complexes was more stable than zinc sulfate (p < 0.05). These results suggest that quinoa peptides have potential applications as ingredients for antihypertension or zinc fortification.

The Stat3 inhibitor F0648-0027 is a potential therapeutic against rheumatoid arthritis.

Rheumatoid arthritis (RA) is a disease characterized by chronic joint inflammation, pain and joint destruction, leading to alteration in activities of daily living, yet pathological mechanisms underlying the condition are not fully clarified. To date, various therapeutic agents have been developed as RA therapy including DMARDs and/or biological agents that target inflammatory cytokines or inhibit JAK. Here we asked whether inhibiting signal transducer and activator of transcription 3 (Stat3) activity would antagonize RA. Stat3 forms dimers when activated and undergoes nuclear translocalization; thus we screened approximately 4.9 million small compounds as potential blockers of protein-protein interactions required for Stat3 dimerization using in silico screening. We identified 15 as strong candidates as potential blockers of protein-protein interactions required for Stat3 dimerization using in silico screening from those compounds. Four of the 15 significantly inhibited expression of IL-6 and RANKL, both of which are direct targets of Stat3, induced by IL-6. Among four, one compound, F0648-0027, significantly inhibited arthritis development without apparent adverse effects in vivo in collagen-induced arthritis model mice. F0648-0027 also significantly blocked Stat3 phosphorylation and nuclear localization following IL-6 stimulation of fibroblasts. These data suggest that Stat3 is a target for collagen-induced arthritis in mice, and that F0648-0027 could serve as a therapeutic reagent against comparable conditions in humans.

From GWAS to drug screening: repurposing antipsychotics for glioblastoma.

BACKGROUND: Glioblastoma is currently an incurable cancer. Genome-wide association studies have demonstrated that 41 genetic variants are associated with glioblastoma and may provide an option for drug development. METHODS: We investigated FDA-approved antipsychotics for their potential treatment of glioblastoma based on genome-wide association studies data using a 'pathway/gene-set analysis' approach. RESULTS: The in-silico screening led to the discovery of 12 candidate drugs. DepMap portal revealed that 42 glioma cell lines show higher sensitivities to 12 candidate drugs than to Temozolomide, the current standard treatment for glioblastoma. CONCLUSION: In particular, cell lines showed significantly higher sensitivities to Norcyclobenzaprine and Protriptyline which were predicted to bind targets to disrupt a certain molecular function such as DNA repair, response to hormones, or DNA-templated transcription, and may lead to an effect on survival-related pathways including cell cycle arrest, response to ER stress, glucose transport, and regulation of autophagy. However, it is recommended that their mechanism of action and efficacy are further determined.