Hideaki Umeyama

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. Proteins 2013; 81:1980–1987.

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

BackgroundNon-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.ResultsIntegrated 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.ConclusionsWe 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

BackgroundAberrant 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.ResultsThe 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.ConclusionsWe 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.

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

BackgroundFeature 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.ResultsTwo 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.ConclusionsOur 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.

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

BackgroundNon-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.MethodsThis 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.ResultsThe Wnt/β-catenin signalling pathway was extensively enriched among 32 genes identified by feature extraction. Among the genes identified, SFRP1 was specifically indicated to target β-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.ConclusionsThe meta-analysis of reprogrammed NSCLC cell lines identified SFRP1 as a promising target of epigenetic therapy for NSCLC.

Heuristic principal component analysis-based unsupervised feature extraction and its application to gene expression analysis of amyotrophic lateral sclerosis data sets

We applied principal component analysis (PCA)-based unsupervised feature extraction (FE) to amyotrophic lateral sclerosis (ALS) gene expression profiles. ALS is a debilitating neurodegenerative disorder with no effective therapy. The relevant gene expression profiles contained a small number of samples (from a few to tens) with a large number of features (several tens of thousands). Although it is important to recognize critical genes from gene expression profiles, a small-sample-large-feature situation makes FE difficult. In PCA-based unsupervised FE, features rather than samples are embedded into a low dimensional space, and critical genes are identified as outliers that are supposed to obey group-oriented behavior. The 29 candidate genes identified as critical for ALS by this methodology turned out to be biologically feasible based on comparisons with numerous previous studies. Together, they formed a collected gene regulation/protein binding network within which the known, but not explicitly identified in this study, three ALS-causing genes, SOD1, TDP-43, and SETX, could be naturally placed/embedded. Among the 29 genes, the translated chemokine receptor CCR6 protein was considered to be a potential therapy target and its antagonists/agonists were identified using the in silico drug discovery software ChooseLD. The ten top-ranked antagonists/agonists shared structures with many compounds that were previously known to bind to various proteins.

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.

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 inxa0vitro. 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.

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.