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PubMed2004

Mono-nucleotide repeats (MNRs): a neglected polymorphism for generating high density genetic maps in silico.

Short, tandemly repeated DNA motifs, termed SSRs (simple sequence repeats) are widely distributed throughout eukaryotic genomes and exhibit a high degree of polymorphism. The availability of size-based methods for genotyping SSRs has made them the markers of choice for genetic linkage studies in all higher eukaryotes. These genotyping methods are not efficiently applicable to mononucleotide repeats (MNRs). Consequently, MNRs, although highly frequent in the genome, have generally been ignored as genetic markers. In contrast to single nucleotide polymorphisms (SNPs), SSRs can be identified in silico once the genomic sequence or segment of interest is available, without requiring any additional information. This makes possible ad-hoc saturation of a target chromosomal region with informative markers. In this context, MNRs appear to have much to offer by increasing the degree of marker saturation that can be obtained. By using the human genome sequence as a model, computational analysis demonstrates that MNRs in the size of 9-15 bp are highly abundant, with an average appearance every 2.9 kb, exceeding di- and tri-nucleotide SSRs frequencies by two- and five-fold, respectively. In order to enable practical, high throughput MNR genotyping, a rapid method was developed, based on sizing of fluorescent-labeled primer extension products. Genotyping of 16 arbitrarily chosen non-coding MNR sites along human chromosome 22 revealed that almost two-thirds (63%) of them were polymorphic, having 2-5 alleles per locus, with 20% of the polymorphic MNRs having more than two alleles. Thus, MNRs have potential for in silico saturation of sequenced eukaryote genomes with informative genetic markers.

Chromosome Mapping↗
PubMed2006

Fine mapping and in silico isolation of the EUI1 gene controlling upper internode elongation in rice.

Upper internode elongation in rice is an important agronomic trait. Well-known mutants with an elongated uppermost internode (eui) are important germplasms for developing unsheathed-panicle male-sterile lines in hybrid rice breeding. We finely mapped the eui1 gene and identified its candidate gene using in silico analysis based on previous research work and rice genomic sequence data. The rice eui1 gene was mapped to two overlapping BAC clones, OSJNBa0095J22 and OSJNBb0099O15, between the markers AC40 and AC46, that were 0.64 cM apart and spanned approximately 152 kb. A simple sequence repeat (SSR) marker AC41 that cosegregated with eui1 was located in an intron of a putative cytochrome P450-related gene. In silico analysis suggested that this encoded the cytochrome CYP714D1. Allelic sequencing confirmed that EUI1 corresponded to this P450 gene. A gamma ray-induced eui1 mutant carried a deletion in exon II of the EUI1 gene, and resulted in a frame-shift deletion that produced a truncated polypeptide. We conclude that the EUI1 gene controlling the upper internode elongation in rice is 9,804 bp long, and comprises two exons and one intron. The length of the cDNA is 1,931 bp containing a 1,734 bp ORF, a 110 bp 5'-UTR and a 87 bp 3'-UTR. The ORF encodes an unknown 577 amino acid functional protein, that appears to be a member of the cytochrome P450 family.

Chromosome Mapping↗
PubMed2005

Identification of endogenous surrogate ligands for human P2Y12 receptors by in silico and in vitro methods.

Endogenous ligands acting on a human P2Y12 receptor, one of the G-protein coupled receptors, were searched by in silico screening against our own database, which contains more than 500 animal metabolites. The in silico screening using the docking software AutoDock resulted in selection of cysteinylleukotrienes (CysLTs) and 5-phosphoribosyl 1-pyrophosphate (PRPP), with high free energy changes, in addition to the known P2Y12 ligands such as 2MeSADP and ADP. These candidates were subjected to an in vitro Ca2+ assay using the CHO cells stably expressing P2Y12-G16alpha fusion proteins. We found that CysLTE4 and PRPP acted on the P2Y12 receptor as agonists with the EC50 values of 1.3 and 7.8 nM, respectively. Furthermore, we analyzed the phylogenetic relationship of the P2Y, P2Y-like, and CysLT receptors based on sequence alignment followed by evolutionary analyses. The analyses showed that the P2Y12, P2Y13, P2Y14, GPR87, CysLT-1, and CysLT-2 receptors formed a P2Y-related receptor subfamily with common sequence motifs in the transmembrane regions.

Amino Acid Motifs↗
PubMed2005

Novel matrix metalloproteinase inhibitors: generation of lead compounds by the in silico fragment-based approach.

Generation of structurally new matrix metalloproteinase inhibitors was successfully carried out using an in silico technique. In order to identify the small fragment interacting with residues in the S1' pocket of MMP-1 through hydrogen bonds, we performed in silico screening using the LUDI program. As a result, acetyl-L-alanyl-(N-methyl)amide (Ac-L-Ala-NHMe) was selected to link with another fragment, hydroxamic acid that interacted with catalytic zinc. By this approach, the L-glutamic acid derivative 2b was discovered to be a new type of matrix metalloproteinase inhibitor. Further transformation to reduce its peptidic nature and improve activity yielded nonpeptidic lead compounds as inhibitors of MMP-1, -2, -3, and -9.

Magnetic Resonance Spectroscopy↗
PubMed2006

KCNE2, a down-regulated gene identified by in silico analysis, suppressed proliferation of gastric cancer cells.

It is important to identify the differentially expressed gene in gastric cancer for elucidating the molecular mechanisms of tumorigenesis of stomach. Here, 38 genes differentially expressed genes between gastric cancer and normal gastric mucosa by in silico approaches. A potassium channel protein KCNE2, identified as a down-regulated gene in gastric cancer, was chosen for further study. We investigated the expression of KCNE2 in gastric cancer tissues and cell lines and examined the effect of KCNE2 on proliferation of gastric cancer. The expression of KCNE2 was markedly down-regulated in gastric cancer tissues and cell lines. Forced overexpression of KCNE2 suppressed the growth of SGC7901 cells and cell cycle progression significantly, which might be related to the down-regulation of Cyclin D1. KCNE2 also inhibited SGC7901 cell growth in soft agar and its tumorigenicity in nude mice. Taken together, our work showed that in silico analysis approaches could be used to identify cancer-related genes effectively. KCNE2, as a novel down-regulated gene in gastric cancer, suppressed cell proliferation and tumorigenesis of stomach.

Animals↗
PubMed2006

In silico prediction of clinical efficacy.

Drug development is a high risk and costly process, and the ability to predict clinical efficacy in silico (in a computer) can save the pharmaceutical industry time and resources. Additionally, such an approach will result in more targeted, personalized therapies. To date, a number of in silico strategies have been developed to provide better information about the human response to novel therapies earlier in the drug development process. Some of the most prominent include physiological modeling of disease and disease processes, analytical tools for population pharmacodynamics, tools for the analysis of genomic expression data, Monte Carlo simulation technologies, and predictive biosimulation. These strategies are likely to contribute significantly to reducing the failure rate of drugs entering clinical trials.

Clinical Trials as Topic↗
PubMed2005

Genome semantics, in silico multicellular systems and the Central Dogma.

Genomes with their complexity and size present what appears to be an impossible challenge. Scientists speak in terms of decades or even centuries before we will understand how genomes and their hosts the cell and the city of cells that make up the multicellular context function. We believe that there will be surprisingly quick progress made in our understanding of genomes. The key is to stop taking the Central Dogma as the only direction in which genome research can scale the semantics of genomes. Instead a top-down approach coupled with a bottom-up approach may snare the unwieldy beast and make sense of genomes. The method we propose is to take in silico biology seriously. By developing in silico models of genomes cells and multicellular systems, we position ourselves to develop a theory of meaning for artificial genomes. Then using that develop a natural semantics of genomes.

Cells↗
PubMed2005

Evaluation of an integrated in vitro-in silico PBPK (physiologically based pharmacokinetic) model to provide estimates of human bioavailability.

PK express module is a physiologically based model of first pass metabolism, which integrates in vitro data with an in silico physiologically based pharmacokinetic (PBPK) model to predict human bioavailability (F(H)). There are three required inputs: FDp (Fraction dose absorbed, final parameter from iDEA absorption module), protein binding (fu) and disappearance kinetics in human hepatocytes. Caco-2 permeability, aqueous solubility (at multiple pH's), estimated dose and chemical structure are inputs required for the estimation of FDp (Norris et al., 2000; Stoner et al., 2004) and were determined for all compounds in our laboratory or obtained from literature. Protein binding data was collected from literature references and/or Pfizer database. Human hepatocyte data was generated in-house using an automated human hepatocyte method (using Tecan Genesis Workstation) as described previously (). Sixteen compounds (commercial and Pfizer compounds) were chosen to evaluate the PK express model and the bioavailability predicted from the module was compared with known clinical endpoints. For majority of the 16 compounds (approximately 80%), the PK express model F(H) values were comparable to the known human bioavailability (F(H)) (within 23.7 units of the known human (true) F, except for PF 3, PF 4, PF 6). In conclusion, the PK express model integrates a number of key readily available discovery parameters and provides estimates of human performance by integrating in silico and experimental variables built on a physiological based pharmacokinetic model. Information from this model in conjunction with other ADME data (e.g., P450 inhibition) will enable progression of most promising compounds for further in vivo PK and/or efficacy studies.

Biological Availability↗
PubMed2005

Development of a software tool for in silico simulation of Escherichia coli using a visual programming environment.

This study describes the development of a software tool, EcoSim, to assist users in implementing quantitative in silico simulation easily. It consists of four parts: extracellular environment and constraints setting mode, table for optimal metabolic flux distribution and chart for changes of substrate concentration, dynamic flux distribution viewer and dynamic hierarchical regulatory network viewer. Representation of a hierarchical regulatory network was constructed with defined modeling symbols and weight in the central Escherichia coli metabolism. All programming procedures for EcoSim were accomplished in a visual programming environment (LabVIEW). To illustrate quantitative in silico simulation with EcoSim, this program was performed on E. coli using glucose and acetate as carbon sources. The simulation results were in agreement with the experimental data obtained from the literature. EcoSim can be used to assist biologists and engineers in predicting and interpreting dynamic behaviors of E. coli under a variety of environmental conditions.

Acetates↗
PubMed2006

Biochemical profiling in silico--predicting substrate specificities of large enzyme families.

A general high-throughput method for in silico biochemical profiling of enzyme families has been developed based on covalent docking of potential substrates into the binding sites of target enzymes. The method has been tested by systematically docking transition state--analogous intermediates of 12 substrates into the binding sites of 20 alpha/beta hydrolases from 15 homologous families. To evaluate the effect of side chain orientations to the docking results, 137 crystal structures were included in the analysis. A good substrate must fulfil two criteria: it must bind in a productive geometry with four hydrogen bonds between the substrate and the catalytic histidine and the oxyanion hole, and a high affinity of the enzyme-substrate complex as predicted by a high docking score. The modelling results in general reproduce experimental data on substrate specificity and stereoselectivity: the differences in substrate specificity of cholinesterases toward acetyl- and butyrylcholine, the changes of activity of lipases and esterases upon the size of the acid moieties, activity of lipases and esterases toward tertiary alcohols, and the stereopreference of lipases and esterases toward chiral secondary alcohols. Rigidity of the docking procedure was the major reason for false positive and false negative predictions, as the geometry of the complex and docking score may sensitively depend on the orientation of individual side chains. Therefore, appropriate structures have to be identified. In silico biochemical profiling provides a time efficient and cost saving protocol for virtual screening to identify the potential substrates of the members of large enzyme family from a library of molecules.

Binding Sites↗
PubMed2006

In silico Northern blot, an automated method to determine expression patterns from EST databases, reveals tissue specificity of murine 17beta-hydroxysteroid dehydrogenase type 11.

In this study, we developed an automated in silico Northern blot (ISNB) for analysis of gene expression patterns from EST databases. This kind of analysis can facilitate initial enzyme characterization by providing tissue distribution patterns. For proof of principle, we analyzed the expression pattern of well-characterized murine 17beta-hydroxysteroid dehydrogenases type 1 and 4. Less characterized murine 17beta-hydroxysteroid dehydrogenase type 11 (Dhrs 8) also was included and processed bioinformatically (ISNB) and further analyzed by Northern blot. The 17beta-hydroxysteroid dehydrogenase type 11 showed the same wide expression pattern by in silico and by wet laboratory approaches. The data point to its involvement of the enzyme in lipid metabolism. The quality of the ISNB relies on the quality of EST-databases. However, our approach is an easy and versatile tool of potentially universal application.

17-Hydroxysteroid Dehydrogenases↗
PubMed2005

Validation of in silico prediction by in vitro immunoserological results of fine epitope mapping on citrate synthase specific autoantibodies.

In silico antibody-antigen binding predictions are generally employed in research to rationalize epitope development. These techniques are widely spread despite their technical limitations. To validate the results of these bioinformatic calculations evidence based comparative in vitro studies are necessary. We have used a well-conserved mitochondrial inner membrane antigen-citrate synthase to develop a model for comparative analysis of the predicted and the immunoserologically verified epitopes of circulating autoantibodies. Epitopes were predicted using accepted tools: the GCG Wisconsin package and TEPITOPE 2000. An overlapping multipin ELISA assay--covering 49% of the citrate synthase molecule--was developed to map autoantibody epitopes of individuals (healthy, systemic autoimmune, and heart transplanted) in different immunopathological conditions. From the 40 synthesized decapeptides 34 were predicted in silico and 27 were validated in vitro. Thirty-two percent of epitopes were recognized by majority of sera 47% by at least one sera. False positive predictions were 21%. There was major difference in the recognized epitope pattern under different immunopathological conditions. Our results suggest that special databases are needed for training and weighing prediction methods by clinically well-characterized samples, due to the differences in the immune response under different health status. The development of these special algorithms needs a new approach. A high number of samples under these special immunological conditions are to be mapped and then used for the "fine tuning" of different prediction algorithms.

Amino Acid Sequence↗
PubMed2006

An in vitro and in silico study on the flavonoid-mediated modulation of the transport of 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP) through Caco-2 monolayers.

The present study describes the effect of different flavonoids on the absorption of the pro-carcinogen PhIP through Caco-2 monolayers and the development of an in silico model describing this process taking into account passive diffusion and active transport of PhIP. Various flavonoids stimulated the apical to basolateral PhIP transport. Using the in silico model for flavone, kaempferol and chrysoeriol, the apparent Ki value for inhibition of the active transport to the apical side was estimated to be below 53 muM and for morin, robinetin and taxifolin between 164 and 268 microM. For myricetin, luteolin, naringenin and quercetin, the apparent Ki values were determined more accurately and amounted to 37.3, 12.2, 11.7 and 5.6 microM respectively. Additional experiments revealed that the apical to basolateral PhIP transport was also increased in the presence of a typical BCRP or MRP inhibitor with apparent Ki values in the same range as those of the flavonoids. This observation together with the fact that flavonoids are known to be inhibitors of MRPs and BCRP, corroborates that inhibition of these apical membrane transporters is involved in the flavonoid-mediated increased apical to basolateral PhIP transport. Based on the apparent Ki values obtained, it is concluded that the flavonols, at the levels present in the regular Western diet, are capable of stimulating the transport of PhIP through Caco-2 monolayers from the apical to the basolateral compartment. This points to flavonoid-mediated stimulation of the bioavailability of PhIP and, thus, a possible adverse effect of these supposed beneficial food ingredients.

ATP Binding Cassette Transporter, Subfamily B, Mem↗
PubMed2004

In silico tools to aid risk assessment of endocrine disrupting chemicals.

In silico or computational tools could be used more effectively in endocrine disruptor risk assessment for prescreening potential endocrine disruptors, improving experimental in vitro screening assay design and facilitating more thorough data analyses. The in silico tools reviewed here are three-fold and include the use of: (1) nuclear receptor (NR) crystal structures and homology models to examine potential modes of ligand binding by different representative compounds; (2) multivariate principal component analyses (PCA) techniques to select best predicted cell lines for endocrine disrupting chemicals (EDC) risk assessment purposes; (3) NR quantitative structure-activity relationships (QSARs) that can be constructed from varied biological data sources, using multivariate partial least squares (PLS) techniques and specific descriptors. The cytosolic and NR examples discussed here include the Ah receptor, (AhR), the human oestrogen receptor alpha (hERalpha) and the human pregnane X receptor (PXR). The varied biological data sets can be compared to give a more integrated dimension to receptor cross talk mechanisms, with further support from molecular modelling studies.

Animals↗
PubMed2006

In silico search of putative adverse drug reaction related proteins as a potential tool for facilitating drug adverse effect prediction.

Adverse drug reaction (ADR) is a significant issue in drug development and post-market applications. Different experimental and computational approaches need to be explored for predicting ADRs due to the complexity of their molecular mechanisms. One approach for predicting ADRs of a drug is to search for its interaction with ADR-related proteins (ADRRPs). In this work, this approach is tested on 11 marketed anti-HIV drugs covering protease inhibitors (PIs), nucleoside reverse transcriptase inhibitors (NRTIs), and non-nucleoside reverse transcriptase inhibitors (NNRTIs). An in silico drug target search method, INVDOCK, is used for searching the ADRRPs of each of these drugs. The corresponding ADRs of the predicted ADRRPs of each of these drugs are compared to clinically observed ADRs reported in the literature. It is found that 86-89% of the INVDOCK predicted ADRs of these drugs are consistent with the literature reported ADRs, and about 67-100% of the literature-reported ADRs of these drugs to various degrees is agreed with INVDOCK predictions. These results suggest that it is feasible to explore in silico ADRRP search methods for facilitating drug toxicity prediction.

Adverse Drug Reaction Reporting Systems↗
PubMed2005

An analysis of genetic toxicity, reproductive and developmental toxicity, and carcinogenicity data: II. Identification of genotoxicants, reprotoxicants, and carcinogens using in silico methods.

This study examined a novel method to identify carcinogens that employed expanded data sets composed of in silico data pooled with actual experimental genetic toxicity (genetox) and reproductive and developmental toxicity (reprotox) data. We constructed 21 modules using the MC4PC program including 13 of 14 (11 genetox and 3 reprotox) tests that we found correlated with results of rodent carcinogenicity bioassays (rcbioassays) [Matthews, E.J., Kruhlak, N.L., Cimino, M.C., Benz, R.D., Contrera, J.F., 2005b. An analysis of genetic toxicity, reproductive and developmental toxicity, and carcinogenicity data: I. Identification of carcinogens using surrogate endpoints. Regul. Toxicol. Pharmacol.]. Each of the 21 modules was evaluated by cross-validation experiments and those with high specificity (SP) and positive predictivity (PPV) were used to predict activities of the 1442 chemicals tested for carcinogenicity for which actual genetox or reprotox data were missing. The expanded data sets had approximately 70% in silico data pooled with approximately 30% experimental data. Based upon SP and PPV, the expanded data sets showed good correlation with carcinogenicity testing results and had correlation indicator (CI, the average of SP and PPV) values of 75.5-88.7%. Conversely, expanded data sets for 9 non-correlated test endpoints were shown not to correlate with carcinogenicity results (CI values <75%). Results also showed that when Salmonella mutagenic carcinogens were removed from the 12 correlated, expanded data sets, only 7 endpoints showed added value by detecting significantly more additional carcinogens than non-carcinogens.

Animals↗
PubMed2003

In silico identification of components of the Toll-like receptor (TLR) signaling pathway in clustered chicken expressed sequence tags (ESTs).

We have described a bioinformatic approach that involves the clustering of expressed sequence tags (ESTs) to reveal homologs of the Toll-like receptor (TLR) pathway in the chicken. Homology searching of proteins, predicted to be encoded by these EST clusters, resulted in the in silico identification of full-length sequences for Toll-interacting protein (Tollip), IL-1 receptor-associated kinase 4 (IRAK-4), myeloid differentiation factor 88 adapter-like (Mal), TGF beta-activated kinase 1 binding protein 1 (TAB1). We also determined partial sequence information for myeloid differentiation factor 88 (MyD88), two novel TLRs, TNF receptor-associated factor 6 (TRAF6), TGF beta-activated kinase 1 (TAK1), TAB2, inhibitor of nuclear factor kappa B kinase alpha (IKK alpha) and IKK beta. This bioinformatics study has confirmed the evolutionary conservation of the TLR pathway in chicken and demonstrated its essential homology to the TLR pathway in mammals. We have identified in silico the full-length sequence for liver-expressed antimicrobial peptide 2 (LEAP-2). This is the first time a non-mammalian LEAP-2 has been described.

Amino Acid Sequence↗
PubMed2001

In silico research in drug discovery.

Target and lead discovery constitute the main components of today's early pharmaceutical research. The aim of target discovery is the identification and validation of suitable drug targets for therapeutic intervention, whereas lead discovery identifies novel chemical molecules that act on those targets. With the near completion of the human genome sequencing, bioinformatics has established itself as an essential tool in target discovery and the in silico analysis of gene expression and gene function are now an integral part of it, facilitating the selection of the most relevant targets for a disease under study. In lead discovery, advances in chemoinformatics have led to the design of compound libraries in silico that can be screened virtually. Moreover, computational methods are being developed to predict the drug-likeness of compounds. Thus, drug discovery is already on the road towards electronic R&D.

Computational Biology↗