Author Correction: Development of a novel anti-hepatitis B virus agent via Sp1.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Development of a novel anti-hepatitis B virus agent via Sp1.

Nucleos(t)ide analog (NA) therapy has proven effective in treating chronic hepatitis B. However, NAs frequently result in viral relapse after the cessation of therapy. This is because NAs cannot fully eliminate the viral episomal covalently closed circular DNA (cccDNA) in the nucleus. In this study, we identified small molecular compounds that control host factors related to viral replication using in silico screening with simulated annealing based on bioinformatics for protein-ligand flexible docking. Twelve chemical compound candidates for alpha-glucosidase (AG) inhibitors were identified from a library of chemical compounds and used to treat fresh human hepatocytes infected with HBV. They were then monitored for their anti-viral effects. HBV replication was inhibited by one candidate (1-[3-(4-tert-butylcyclohexyl)oxy-2-hydroxypropyl]-2,2,6,6-tetramethylpiperidin-4-ol) in a dose-dependent manner. This compound significantly reduced ccc DNA production, compared to Entecavir (p < 0.05), and had a lower anti-AG effect. Gene expression analysis and structural analysis of this compound showed that its inhibitive effect on HBV was via interaction with Sp1. The nuclear transcription factor Sp1 acts on multiple regions of HBV to suppress HBV replication. Identifying candidates that control nuclear transcription factors facilitate the development of novel therapies. Drugs with a mechanism different from NA are promising for the elimination of HBV.

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.

Novel Antifungal Compound Z-705 Specifically Inhibits Protein Kinase C of Filamentous Fungi.

The cell wall integrity signaling (CWIS) pathway is involved in fungal cell wall biogenesis. This pathway is composed of sensor proteins, protein kinase C (PKC), and the mitogen-activated protein kinase (MAPK) pathway, and it controls the transcription of many cell wall-related genes. PKC plays a pivotal role in this pathway; deficiencies in PkcA in the model filamentous fungus Aspergillus nidulans and in MgPkc1p in the rice blast fungus Magnaporthe grisea are lethal. This suggests that PKC in filamentous fungi is a potential target for antifungal agents. In the present study, to search for MgPkc1p inhibitors, we carried out in silico screening by three-dimensional (3D) structural modeling and performed growth inhibition tests for M. grisea on agar plates. From approximately 800,000 candidate compounds, we selected Z-705 and evaluated its inhibitory activity against chimeric PKC expressed in Saccharomyces cerevisiae cells in which the kinase domain of native S. cerevisiae PKC was replaced with those of PKCs of filamentous fungi. Transcriptional analysis of MLP1, which encodes a downstream factor of PKC in S. cerevisiae, and phosphorylation analysis of the mitogen-activated protein kinase (MAPK) Mpk1p, which is activated downstream of PKC, revealed that Z-705 specifically inhibited PKCs of filamentous fungi. Moreover, the inhibitory activity of Z-705 was similar to that of a well-known PKC inhibitor, staurosporine. Interestingly, Z-705 inhibited melanization induced by cell wall stress in M. grisea We discuss the relationships between PKC and melanin biosynthesis.IMPORTANCE A candidate inhibitor of filamentous fungal protein kinase C (PKC), Z-705, was identified by in silico screening. A screening system to evaluate the effects of fungal PKC inhibitors was constructed in Saccharomyces cerevisiae Using this system, we found that Z-705 is highly selective for filamentous fungal PKC in comparison with S. cerevisiae PKC. Analysis of the AGS1 mRNA level, which is regulated by Mps1p mitogen-activated protein kinase (MAPK) via PKC, in the rice blast fungus Magnaporthe grisea revealed that Z-705 had a PKC inhibitory effect comparable to that of staurosporine. Micafungin induced hyphal melanization in M. grisea, and this melanization, which is required for pathogenicity of M. grisea, was inhibited by PKC inhibition by both Z-705 and staurosporine. The mRNA levels of 4HNR, 3HNR, and SCD1, which are essential for melanization in M. grisea, were suppressed by both PKC inhibitors.

Ubiquitin-dependent proteolysis of CXCL7 leads to posterior longitudinal ligament ossification.

Ossification of the posterior longitudinal ligament (OPLL), a spinal ligament, reduces the range of motion in limbs. No treatment is currently available for OPLL, which is why therapies are urgently needed. OPLL occurs in obesity, is more common in men, and has an onset after 40 years of age. The mechanisms underlying OPLL remain unclear. In this study, we performed a serum proteomic analysis in both OPLL patients and healthy subjects to identify factors potentially involved in the development of OPLL, and found reduced levels of a protein that might underlie the pathology of OPLL. We isolated the protein, determined its amino acid sequence, and identified it as chemokine (C-X-C motif) ligand 7 (CXCL7). Based on these proteomics findings, we generated a CXCL7 knockout mouse model to study the molecular mechanisms underlying OPLL. CXCL7-null mice presented with a phenotype of OPLL, showing motor impairment, heterotopic ossification in the posterior ligament tissue, and osteoporosis in vertebrate tissue. To identify the mechanisms of CXCL7 deficiency in OPLL, we searched for single nucleotide polymorphisms and altered DNA exons, but no abnormalities were found. Although miR-340 levels were found to be high in an miRNA array, they were insufficient to reduce CXCL7 levels. Ubiquitin C-terminal hydrolase1 (UCHL1) was found to be overexpressed in CXCL7-null mice and in the sera of patients with OPLL, and was correlated with OPLL severity. Post-translational modifications of proteins with ubiquitin and ubiquitin-like modifiers, orchestrated by a cascade of specialized ubiquitin activating enzyme (E1), ubiquitin conjugating enzyme (E2), and ubiquitin ligase (E3) enzymes, are thought to control a wide range of cellular processes, and alterations in the ubiquitin-proteasome system have been associated with several degenerative disorders. In addition, the OPLL tissue of CXCL7-null mouse and its primary cells expressed the antibody to ubiquitin (linkage-specific K48). Our data clearly show decreased CXCL7 levels in patients with OPLL, and that OPLL developed in mice lacking CXCL7. Tumor necrosis factor receptor-associated factor (TRAF)6 expression was decreased in CXCL7-null mouse primary cells. Furthermore, K48 polyubiquitination was found in posterior longitudinal ligament ossified tissue and primary cells from CXCL7-null mice. We performed a phosphoproteomics analysis in CXCL7-deficient mice and identified increased phosphorylation of mitogen-activated protein kinase kinase (ME3K)15, ubiquitin protein ligase E3C (UBE3C) and protein kinase C (PKC) alpha, suggesting that ubiquitin-dependent degradation is involved in CXCL7 deficiency. Future studies in the CXCL7-null mouse model are, therefore, warranted to investigate the role of ubiquitination in the onset of OPLL. In conclusion, CXCL7 levels may be useful as a serum marker for the progression of OPLL. This study also suggests that increasing CXCL7 levels in patients can serve as an effective therapeutic strategy for the treatment of OPLL.

Retrospective Proteomic Analysis of a Novel, Cancer Metastasis-Promoting RGD-Containing Peptide.

Patients who undergo surgical extirpation of a primary liver carcinoma followed by radiotherapy and chemotherapy leading to complete remission are nevertheless known to develop cancerous metastases 3-10 years later. We retrospectively examined the blood sera collected over 8 years from 30 patients who developed bone metastases after the complete remission of liver cancer to identify serum proteins showing differential expression compared to patients without remission. We detected a novel RGD (Arg-Gly-Asp)-containing peptide derived from the C-terminal portion of fibrinogen in the sera of metastatic patients that appeared to control the EMT (epithelial-mesenchymal transition) of cancer cells, in a process associated with miR-199a-3p. The RGD peptide enhanced new blood vessel growth and increased vascular endothelial growth factor levels when introduced into fertilized chicken eggs. The purpose of this study was to enable early detection of metastatic cancer cells using the novel RGD peptide as a biomarker, and thereby develop new drugs for the treatment of metastatic cancer.

An iterative compound screening contest method for identifying target protein inhibitors using the tyrosine-protein kinase Yes.

We propose a new iterative screening contest method to identify target protein inhibitors. After conducting a compound screening contest in 2014, we report results acquired from a contest held in 2015 in this study. Our aims were to identify target enzyme inhibitors and to benchmark a variety of computer-aided drug discovery methods under identical experimental conditions. In both contests, we employed the tyrosine-protein kinase Yes as an example target protein. Participating groups virtually screened possible inhibitors from a library containing 2.4 million compounds. Compounds were ranked based on functional scores obtained using their respective methods, and the top 181 compounds from each group were selected. Our results from the 2015 contest show an improved hit rate when compared to results from the 2014 contest. In addition, we have successfully identified a statistically-warranted method for identifying target inhibitors. Quantitative analysis of the most successful method gave additional insights into important characteristics of the method used.

Molecular Dynamics Simulations to Determine the Structure and Dynamics of Hepatitis B Virus Capsid Bound to a Novel Anti-viral Drug.

Hepatitis B virus (HBV) chronically infects millions of people worldwide and is a major cause of serious liver diseases, including liver cirrhosis and liver cancer. In our previous study, in silico screening was used to isolate new anti-viral compounds predicted to bind to the HBV capsid. Four of the isolated compounds have been reported to suppress the cellular multiplication of HBV experimentally. In the present study, molecular dynamics simulations of the HBV capsid were performed under rotational symmetry boundary conditions, to clarify how the structure and dynamics of the capsid are affected at the atomic level by the binding of one of the isolated compounds, C13. Two simulations of the free HBV capsid, two further simulations of the capsid-C13 complex, and one simulation of the capsid-AT-130 complex were performed. For statistical confidence, each set of simulations was repeated by five times, changing the simulation conditions. C13 continued to bind at the predicted binding site during the simulations, supporting the hypothesis that C13 is a capsid-binding compound. The structure and dynamics of the HBV capsid were greatly influenced by the binding and release of C13, and these effects were essentially identical to those seen for AT-130, indicating that C13 likely inhibits the function of the HBV capsid.

SFRP1 is a possible candidate for epigenetic therapy in non-small cell lung cancer.

BACKGROUND: Non-small cell lung cancer (NSCLC) remains a lethal disease despite many proposed treatments. Recent studies have indicated that epigenetic therapy, which targets epigenetic effects, might be a new therapeutic methodology for NSCLC. However, it is not clear which objects (e.g., genes) this treatment specifically targets. Secreted frizzled-related proteins (SFRPs) are promising candidates for epigenetic therapy in many cancers, but there have been no reports of SFRPs targeted by epigenetic therapy for NSCLC. METHODS: This study performed a meta-analysis of reprogrammed NSCLC cell lines instead of the direct examination of epigenetic therapy treatment to identify epigenetic therapy targets. In addition, mRNA expression/promoter methylation profiles were processed by recently proposed principal component analysis based unsupervised feature extraction and categorical regression analysis based feature extraction. RESULTS: The Wnt/ß-catenin signalling pathway was extensively enriched among 32 genes identified by feature extraction. Among the genes identified, SFRP1 was specifically indicated to target ß-catenin, and thus might be targeted by epigenetic therapy in NSCLC cell lines. A histone deacetylase inhibitor might reactivate SFRP1 based upon the re-analysis of a public domain data set. Numerical computation validated the binding of SFRP1 to WNT1 to suppress Wnt signalling pathway activation in NSCLC. CONCLUSIONS: The meta-analysis of reprogrammed NSCLC cell lines identified SFRP1 as a promising target of epigenetic therapy for NSCLC.

Inhibition of bacterial undecaprenyl pyrophosphate synthase by small fungal molecules.

Viridicatumtoxin and spirohexaline, small fungal molecules with a tetracyclic scaffold and an additional spirobicyclic ring in common, were found to inhibit bacterial undecaprenyl pyrophosphate (UPP) synthase with IC50 values of 4 and 9 µm, respectively. These molecules showed weak inhibitory activity against catalytically related enzymes such as bacterial octaprenyl pyrophosphate synthase and yeast dehydrodolichyl pyrophosphate synthase, indicating that the compounds preferentially inhibit UPP synthase. They showed antimicrobial activity, particularly against Gram-positive bacteria including methicillin-resistant Staphylococcus aureus (MRSA). Furthermore, molecular modeling strongly suggested that the hydrophobic spirobicyclic ring of viridicatumtoxin interacts with three hydrophobic clefts of the active site in MRSA UPP synthase.

Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target.

A search of broader range of chemical space is important for drug discovery. Different methods of computer-aided drug discovery (CADD) are known to propose compounds in different chemical spaces as hit molecules for the same target protein. This study aimed at using multiple CADD methods through open innovation to achieve a level of hit molecule diversity that is not achievable with any particular single method. We held a compound proposal contest, in which multiple research groups participated and predicted inhibitors of tyrosine-protein kinase Yes. This showed whether collective knowledge based on individual approaches helped to obtain hit compounds from a broad range of chemical space and whether the contest-based approach was effective.

Development of novel hepatitis B virus capsid inhibitor using in silico screening.

Antiviral therapy for chronic hepatitis B that uses nucleos(t)ide analogue is considered effective. However, most drugs of this class frequently result in viral relapse after cessation of therapy as well as the emergence of resistance, thereby limiting their clinical use. In order to increase the therapeutic efficiency of chronic hepatitis B treatments, it is important to survey novel (chemical) reagents targeting other stages of the viral replication process. The aim of this study was to identify novel capsid inhibitor candidates using in silico screening. We discovered four such candidates that decreased the levels of HBV DNA and HBsAg in vitro. These four capsid inhibitor candidates did not induce cell toxicity even at high concentrations. Results from docking simulation showed that the candidates bounded with high affinity with the capsid protein hydrophobic binding site. Identifying direct acting HBV core protein inhibitors increases the likelihood that novel medicines can be developed that allows the combination of novel anti-viral drugs and nucleos(t)ide analogue or interferon for HBV treatment.

Principal component analysis-based unsupervised feature extraction applied to in silico drug discovery for posttraumatic stress disorder-mediated heart disease.

BACKGROUND: Feature extraction (FE) is difficult, particularly if there are more features than samples, as small sample numbers often result in biased outcomes or overfitting. Furthermore, multiple sample classes often complicate FE because evaluating performance, which is usual in supervised FE, is generally harder than the two-class problem. Developing sample classification independent unsupervised methods would solve many of these problems. RESULTS: Two principal component analysis (PCA)-based FE, specifically, variational Bayes PCA (VBPCA) was extended to perform unsupervised FE, and together with conventional PCA (CPCA)-based unsupervised FE, were tested as sample classification independent unsupervised FE methods. VBPCA- and CPCA-based unsupervised FE both performed well when applied to simulated data, and a posttraumatic stress disorder (PTSD)-mediated heart disease data set that had multiple categorical class observations in mRNA/microRNA expression of stressed mouse heart. A critical set of PTSD miRNAs/mRNAs were identified that show aberrant expression between treatment and control samples, and significant, negative correlation with one another. Moreover, greater stability and biological feasibility than conventional supervised FE was also demonstrated. Based on the results obtained, in silico drug discovery was performed as translational validation of the methods. CONCLUSIONS: Our two proposed unsupervised FE methods (CPCA- and VBPCA-based) worked well on simulated data, and outperformed two conventional supervised FE methods on a real data set. Thus, these two methods have suggested equivalence for FE on categorical multiclass data sets, with potential translational utility for in silico drug discovery.

Discovering novel direct acting antiviral agents for HBV using in silico screening.

The treatments for chronic hepatitis B (CHB) are interferon and nucleoside analogues reverse transcriptase (RT) inhibitors. Because both treatments are less than ideal, we conducted to identify novel anti-viral agents for HBV-reverse transcriptase (HBV-RT). We determined the ligand-binding site of the HBV-RT by conducting a homological search of the amino acid sequence and then we also determined not only structural arrangement of the target protein but the target protein-binding site of the ligand using known protein-ligand complexes in registered in the protein data bank (PDB). Finally we simulated binding between the ligand candidates and the HBV-RT and evaluated the degree of binding (in silico screening). PXB cells derived from human-mouse chimeric mouse liver, infected with HBV were administrated with the candidates, and HBVDNA in the culture medium was monitored by realtime qPCR. Among compounds from the AKosSamples database, twelve candidates that can inhibit RT were also identified, two of which seem to have the potential to control HBV replication in vitro.

TINAGL1 and B3GALNT1 are potential therapy target genes to suppress metastasis in non-small cell lung cancer.

BACKGROUND: Non-small cell lung cancer (NSCLC) remains lethal despite the development of numerous drug therapy technologies. About 85% to 90% of lung cancers are NSCLC and the 5-year survival rate is at best still below 50%. Thus, it is important to find drugable target genes for NSCLC to develop an effective therapy for NSCLC. RESULTS: Integrated analysis of publically available gene expression and promoter methylation patterns of two highly aggressive NSCLC cell lines generated by in vivo selection was performed. We selected eleven critical genes that may mediate metastasis using recently proposed principal component analysis based unsupervised feature extraction. The eleven selected genes were significantly related to cancer diagnosis. The tertiary protein structure of the selected genes was inferred by Full Automatic Modeling System, a profile-based protein structure inference software, to determine protein functions and to specify genes that could be potential drug targets. CONCLUSIONS: We identified eleven potentially critical genes that may mediate NSCLC metastasis using bioinformatic analysis of publically available data sets. These genes are potential target genes for the therapy of NSCLC. Among the eleven genes, TINAGL1 and B3GALNT1 are possible candidates for drug compounds that inhibit their gene expression.

Genes associated with genotype-specific DNA methylation in squamous cell carcinoma as candidate drug targets.

BACKGROUND: Aberrant DNA methylation is often associated with cancers. Thus, screening genes with cancer-associated aberrant DNA methylation is a useful method to identify candidate cancer-causing genes. Aberrant DNA methylation is also genotype dependent. Thus, the selection of genes with genotype-specific aberrant DNA methylation in cancers is potentially important for tailor-made medicine. The selected genes are important candidate drug targets. RESULTS: The recently proposed principal component analysis based selection of genes with aberrant DNA methylation was applied to genotype and DNA methylation patterns in squamous cell carcinoma measured using single nucleotide polymorphism (SNP) arrays. SNPs that are frequently found in cancers are usually highly methylated, and the genes that were selected using this method were reported previously to be related to cancers. Thus, genes with genotype-specific DNA methylation patterns will be good therapeutic candidates. The tertiary structures of the proteins encoded by the selected genes were successfully inferred using two profile-based protein structure servers, FAMS and Phyre2. Candidate drugs for three of these proteins, tyrosine kinase receptor (ALK), EGLN3 protein, and NUAK family SNF1-like kinase 1 (NUAK1), were identified by ChooseLD. CONCLUSIONS: We detected genes with genotype-specific DNA methylation in squamous cell carcinoma that are candidate drug targets. Using in silico drug discovery, we successfully identified several candidate drugs for the ALK, EGLN3 and NUAK1 genes that displayed genotype-specific DNA methylation.

Bioinformatic screening of autoimmune disease genes and protein structure prediction with FAMS for drug discovery.

Autoimmune diseases are often intractable because their causes are unknown. Identifying which genes contribute to these diseases may allow us to understand the pathogenesis, but it is difficult to determine which genes contribute to disease. Recently, epigenetic information has been considered to activate/deactivate disease-related genes. Thus, it may also be useful to study epigenetic information that differs between healthy controls and patients with autoimmune disease. Among several types of epigenetic information, promoter methylation is believed to be one of the most important factors. Here, we propose that principal component analysis is useful to identify specific gene promoters that are differently methylated between the normal healthy controls and patients with autoimmune disease. Full Automatic Modeling System (FAMS) was used to predict the three-dimensional structures of selected proteins and successfully inferred relatively confident structures. Several possibilities of the application to the drug discovery based on obtained structures are discussed.

Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions.

Community-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side-chain sampling and backbone relaxation, evaluated packing, electrostatic, and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of both existing and new prediction methodologies.

A new method for induced fit docking (GENIUS) and its application to virtual screening of novel HCV NS3-4A protease inhibitors.

Hepatitis C virus (HCV) is an etiologic agent of chronic liver disease, and approximately 170 million people worldwide are infected with the virus. HCV NS3-4A serine protease is essential for the replication of this virus, and thus has been investigated as an attractive target for anti-HCV drugs. In this study, we developed our new induced-fit docking program (genius), and applied it to the discovery of a new class of NS3-4A protease inhibitors (IC(50)=1-10 µM including high selectivity index). The new inhibitors thus identified were modified, based on the docking models, and revealed preliminary structure-activity relationships. Moreover, the genius in silico screening performance was validated by using an enrichment factor. We believe our designed scaffold could contribute to the improvement of HCV chemotherapy.

New protein structure model evaluation methods that include a side-chain consensus score for the protein modeling.

Selecting the best quality model from a set of predicted structures is one of the most important aspects of protein structure prediction. We have developed model quality assessment programs that select high quality models which account for both the Calpha backbone and side-chain atom positions. The new methods are based on the consensus method with consideration of the side-chain environment of a protein structure and the secondary structure agreement. This Side-chain Environment Consensus (SEC) method is compared with the conventional consensus method, 3D-Jury (Ginalski K. et al., Bioinformatics, 19, 1015-1018 (2003)), which takes into account only the Calpha backbone atoms of the protein model. As the result, it was found that the SEC method selects the models with more accurate positioning of the side-chain atoms than the 3D-Jury method. When the SEC method was used in combination with the 3D-Jury method (3DJ+SEC), models were selected with improved quality both in the Calpha backbone and side-chain atom positions. Moreover, the CIRCLE (CCL) method (Terashi G. et al., Proteins, 69 (Suppl. 8), 98-107 (2007)) based on the 3D-1D profile score has been shown to select the best possible models that are the closest to the native structures from candidate models. Accordingly, the 3DJ+SEC+CCL method, in which CIRCLE is used after reducing the number of candidates by the 3DJ+SEC consensus method, was found to be very effective in selecting high quality models. Thus, the best method (the 3DJ+SEC+CCL method) includes the consensus approaches of the Calpha backbone and the side-chains, the secondary structure agreement and the 3D-1D profile score which corresponds to the free energy-like score in the residues of the protein model. In short, new algorithms are introduced in protein structure evaluation methods that are based on a side-chain consensus score. Additionally, in order to apply the 3DJ+SEC+CCL method and indicate the usefulness of this method, a model of human Cabin1, a protein associated with p53 function and cancer, is created using various internet modeling and alignment servers.