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PubMed2002

In silico approaches to mechanistic and predictive toxicology: an introduction to bioinformatics for toxicologists.

Bioinformatics, or in silico biology, is a rapidly growing field that encompasses the theory and application of computational approaches to model, predict, and explain biological function at the molecular level. This information rich field requires new skills and new understanding of genome-scale studies in order to take advantage of the rapidly increasing amount of sequence, expression, and structure information in public and private databases. Toxicologists are poised to take advantage of the large public databases in an effort to decipher the molecular basis of toxicity. With the advent of high-throughput sequencing and computational methodologies, expressed sequences can be rapidly detected and quantitated in target tissues by database searching. Novel genes can also be isolated in silico, while their function can be predicted and characterized by virtue of sequence homology to other known proteins. Genomic DNA sequence data can be exploited to predict target genes and their modes of regulation, as well as identify susceptible genotypes based on single nucleotide polymorphism data. In addition, highly parallel gene expression profiling technologies will allow toxicologists to mine large databases of gene expression data to discover molecular biomarkers and other diagnostic and prognostic genes or expression profiles. This review serves to introduce to toxicologists the concepts of in silico biology most relevant to mechanistic and predictive toxicology, while highlighting the applicability of in silico methods using select examples.

Cluster Analysis↗
PubMed2004

Systematic identification in silico of covalently bound cell wall proteins and analysis of protein-polysaccharide linkages of the human pathogen Candida glabrata.

Candida glabrata is an important cause of systemic candidiasis in humans. This paper reports a systematic analysis of the putative glycosylphosphatidylinositol-modified (GPI) proteins of C. glabrata, a large part of which are covalently bound to the cell wall glucan network and the remainder of which are retained in the plasma membrane, and of cell wall proteins (CWPs) which are covalently bound in a mild-alkali-sensitive manner. In silico genomic analysis revealed 106 putative GPI proteins. Fifty-one of these GPI proteins could be categorized as adhesive proteins, potentially implicated in fungus-host interactions or biofilm formation during the development of fungal infections. Eleven proteins belonged to well-known GPI protein families of glycoside hydrolases, probably involved in cell wall expansion and remodelling during growth. Other identified GPI proteins included phospholipases, aspartic proteases, homologues of ScEcm33p and ScKre1p, and structural CWPs. Interestingly, the GPI algorithm predicted three orthologues of an abundant CWP in S. cerevisiae, Cwp1p, which is absent in Candida albicans. To evaluate the in silico predictions, isolated cell walls were extracted using HF-pyridine, which specifically cleaves phosphodiester bonds, to release GPI-CWPs. Immunological analysis of the extract using one-dimensional SDS-PAGE and anti-ScCwp1p antiserum indicated the presence of a Cwp1p homologue in C. glabrata cell walls. Further analysis by two-dimensional gel electrophoresis and electrospray ionization tandem mass spectrometry (ESI-MS/MS) confirmed the presence of two of the predicted Cwp1p proteins, Cwp1.1p and Cwp1.2p. Crh1p, a putative 1,3-beta-glucan remodelling enzyme, was also identified. In silico genomic analysis further revealed five putative Pir proteins (Pir1-5p) and five members of the Bgl2 glycoside hydrolase family 17, belonging to a class of putative CWPs that can be extracted with NaOH. Immunological analysis of mild-alkali-extracted CWPs showed the presence of a ScPir2p homologue. Together, these experimental data and in silico predictions represent the first systematic analysis of the C. glabrata cell wall proteome.

Amino Acid Sequence↗
PubMed2002

In silico model-driven assessment of the effects of single nucleotide polymorphisms (SNPs) on human red blood cell metabolism.

The completion of the human genome project and the construction of single nucleotide polymorphism (SNP) maps have lead to significant efforts to find SNPs that can be linked to pathophysiology. In silico models of complete biochemical reaction networks relate a cell's individual reactions to the function of the entire network. Sequence variations can in turn be related to kinetic properties of individual enzymes, thus allowing an in silico model-driven assessment of the effects of defined SNPs on overall cellular functions. This process is applied to defined SNPs in two key enzymes of human red blood cell metabolism: glucose-6-phosphate dehydrogenase and pyruvate kinase. The results demonstrate the utility of in silico models in providing insight into differences between red cell function in patients with chronic and nonchronic anemia. In silico models of complex cellular processes are thus likely to aid in defining and understanding key SNPs in human pathophysiology.

Computational Biology↗
PubMed1993

Evaluation of three silicoating methods for resin-bonded prostheses.

Three different methods of increasing the bond strength of resin to metal were tested: the original Silicoater technique, the Silicoater MD technique, and the Rocatec system. Metals used for the resin-bonded prostheses were gold, a cobalt-chromium (Co-Cr) alloy, and titanium. Silicoating increased the bond strength to sandblasted specimens. The original Silicoater technique produced the highest bond strengths, especially when used with the Co-Cr alloy and titanium. The Silicoater MD technique showed the lowest bond strengths for all of the test metals. Storage for 7 days with protective film on the silicoated surface did not affect the bond strength, as compared with specimens bonded immediately. The retention of the resin to the gold specimens (Rocatec system) decreased after thermocycling, but retention to the Co-Cr alloy and titanium was not affected. Various resin cements were tested and showed the same retention with the exception of Microfill Pontic, which gave a weaker bond. Scanning electron microscope (SEM) and energy-dispersive X-ray analysis (EDAX) of the specimens before and after the measurements gave no clear explanation of the differences in bond strengths between the test metals. However, it was concluded that silicoating of the metal surfaces contributed to the retention of the resin by chemical action, as no differences in adaptation of the resin to the metal were seen between specimens pretreated by sandblasting and those which were also silicoated.

Analysis of Variance↗
PubMed2001

Clinical use of an intraoral silicoating technique.

UNLABELLED: A ceramic fracture rate of metal-ceramic fixed prosthodontics of up to almost 9% calls for an efficient and effective intraoral repair system. For the repair of fractured veneering material, an intraoral silicoating technique was introduced. In contrast to other repair techniques, the silicoating can successfully be used on a variety of dental restorative materials and, therefore, offers additional applications in the field of the intraoral adhesive technique. This silicoating technique, consisting of a chairside airabrasion device filled with a specific silica-coated air-abrasion medium, has been used intraorally by the authors since 1993 for clinical purposes. The clinical procedure is described for different applications in the field of the adhesive technique: intraoral repair, preconditioning of core buildups, implant abutments, and surfaces of fixed prosthodontics for the bonding of orthodontic brackets or periodontal splints. CLINICAL SIGNIFICANCE: The intraoral silicoating technique promotes reliable adhesion to the major part of metal alloy, composite, and ceramic surfaces of fixed prosthodontics. Preliminary clinical results of intraoral repairs conducted with this technique have shown that it presents a medium-term alternative to the replacement of the restoration. Also, the preliminary results of the clinical applications in other fields of adhesive dentistry are promising.

Dental Abutments↗
PubMed2003

Description and interpretation of adaptive evolution of Escherichia coli K-12 MG1655 by using a genome-scale in silico metabolic model.

Genome-scale in silico metabolic networks of Escherichia coli have been reconstructed. By using a constraint-based in silico model of a reconstructed network, the range of phenotypes exhibited by E. coli under different growth conditions can be computed, and optimal growth phenotypes can be predicted. We hypothesized that the end point of adaptive evolution of E. coli could be accurately described a priori by our in silico model since adaptive evolution should lead to an optimal phenotype. Adaptive evolution of E. coli during prolonged exponential growth was performed with M9 minimal medium supplemented with 2 g of alpha-ketoglutarate per liter, 2 g of lactate per liter, or 2 g of pyruvate per liter at both 30 and 37 degrees C, which produced seven distinct strains. The growth rates, substrate uptake rates, oxygen uptake rates, by-product secretion patterns, and growth rates on alternative substrates were measured for each strain as a function of evolutionary time. Three major conclusions were drawn from the experimental results. First, adaptive evolution leads to a phenotype characterized by maximized growth rates that may not correspond to the highest biomass yield. Second, metabolic phenotypes resulting from adaptive evolution can be described and predicted computationally. Third, adaptive evolution on a single substrate leads to changes in growth characteristics on other substrates that could signify parallel or opposing growth objectives. Together, the results show that genome-scale in silico metabolic models can describe the end point of adaptive evolution a priori and can be used to gain insight into the adaptive evolutionary process for E. coli.

Adaptation, Physiological↗
PubMed1987

Adhesion of resins to Ag-Pd alloys by means of the silicoating technique.

The purpose of the investigation was to study the effect of water storage on the bond strengths between silanized, silicoated Ag-Pd alloys and veneered composites, in comparison with the bond strengths of systems with conventional retention beads. Furthermore, the mechanism of the bonding was examined. The bond strength of silanized, silicoated dry specimens and similar specimens stored in water was measured by four-point bending. Water storage for 90 days at 37 degrees C reduced the bond strength by approximately 30% to about 15-20 MPa. Mechanical retention beads caused bond strengths of approximately 16-18 MPa which were unaffected by water storage. SEM and microprobe investigations showed that sandblasting with AI2O3 prior to silanization caused substantial numbers of cracks and porosities in the surface layer of the alloy, partly filled with Al2O3. Some particles of silicon oxide in these surface defects were produced by the flame-spraying of the so-called silicoating technique. Further painting of the surface with a silane adhesion primer provided chemical bonding to the composite at the densely spaced Si-O-H-containing silica particles. Many cracks were observed in the interfaces between these particles; thus, water is likely to penetrate the interface with time. The bond strength is most likely reduced by reaction between water and the composite/Si-O structure. The silicon oxide particles are probably attached to the alloy substrate by mechanical retention. Without sandblasting, no bonding was obtained by means of the silicoating technique.

Acrylic Resins↗
PubMed2000

Metabolic flux balance analysis and the in silico analysis of Escherichia coli K-12 gene deletions.

BACKGROUND: Genome sequencing and bioinformatics are producing detailed lists of the molecular components contained in many prokaryotic organisms. From this 'parts catalogue' of a microbial cell, in silico representations of integrated metabolic functions can be constructed and analyzed using flux balance analysis (FBA). FBA is particularly well-suited to study metabolic networks based on genomic, biochemical, and strain specific information. RESULTS: Herein, we have utilized FBA to interpret and analyze the metabolic capabilities of Escherichia coli. We have computationally mapped the metabolic capabilities of E. coli using FBA and examined the optimal utilization of the E. coli metabolic pathways as a function of environmental variables. We have used an in silico analysis to identify seven gene products of central metabolism (glycolysis, pentose phosphate pathway, TCA cycle, electron transport system) essential for aerobic growth of E. coli on glucose minimal media, and 15 gene products essential for anaerobic growth on glucose minimal media. The in silico tpi-, zwf, and pta- mutant strains were examined in more detail by mapping the capabilities of these in silico isogenic strains. CONCLUSIONS: We found that computational models of E. coli metabolism based on physicochemical constraints can be used to interpret mutant behavior. These in silica results lead to a further understanding of the complex genotype-phenotype relation.

Computational Biology↗
PubMed2007

In silico panning for a non-competitive peptide inhibitor.

BACKGROUND: Peptide ligands have tremendous therapeutic potential as efficacious drugs. Currently, more than 40 peptides are available in the market for a drug. However, since costly and time-consuming synthesis procedures represent a problem for high-throughput screening, novel procedures to reduce the time and labor involved in screening peptide ligands are required. We propose the novel approach of 'in silico panning' which consists of a two-stage screening, involving affinity selection by docking simulation and evolution of the peptide ligand using genetic algorithms (GAs). In silico panning was successfully applied to the selection of peptide inhibitor for water-soluble quinoprotein glucose dehydrogenase (PQQGDH). RESULTS: The evolution of peptide ligands for a target enzyme was achieved by combining a docking simulation with evolution of the peptide ligand using genetic algorithms (GAs), which mimic Darwinian evolution. Designation of the target area as next to the substrate-binding site of the enzyme in the docking simulation enabled the selection of a non-competitive inhibitor. In all, four rounds of selection were carried out on the computer; the distribution of the docking energy decreased gradually for each generation and improvements in the docking energy were observed over the four rounds of selection. One of the top three selected peptides with the lowest docking energy, 'SERG' showed an inhibitory effect with Ki value of 20 microM. PQQGDH activity, in terms of the Vmax value, was 3-fold lower than that of the wild-type enzyme in the presence of this peptide. The mechanism of the SERG blockage of the enzyme was identified as non-competitive inhibition. We confirmed the specific binding of the peptide, and its equilibrium dissociation constant (KD) value was calculated as 60 microM by surface plasmon resonance (SPR) analysis. CONCLUSION: We demonstrate an effective methodology of in silico panning for the selection of a non-competitive peptide inhibitor from small virtual peptide library. This study is the first to demonstrate the usefulness of in silico evolution using experimental data. Our study highlights the usefulness of this strategy for structure-based screening of enzyme inhibitors.

Acinetobacter calcoaceticus↗
PubMed2002

In silico and in situ characterization of the zebrafish (Danio rerio) gnrh3 (sGnRH) gene.

BACKGROUND: Gonadotropin releasing hormone (GnRH) is responsible for stimulation of gonadotropic hormone (GtH) in the hypothalamus-pituitary-gonadal axis (HPG). The regulatory mechanisms responsible for brain specificity make the promoter attractive for in silico analysis and reporter gene studies in zebrafish (Danio rerio). RESULTS: We have characterized a zebrafish [Trp7, Leu8] or salmon (s) GnRH variant, gnrh3. The gene includes a 1.6 Kb upstream regulatory region and displays the conserved structure of 4 exons and 3 introns, as seen in other species. An in silico defined enhancer at -976 in the zebrafish promoter, containing adjacent binding sites for Oct-1, CREB and Sp1, was predicted in 2 mammalian and 5 teleost GnRH promoters. Reporter gene studies confirmed the importance of this enhancer for cell specific expression in zebrafish. Interestingly the promoter of human GnRH-I, known as mammalian GnRH (mGnRH), was shown capable of driving cell specific reporter gene expression in transgenic zebrafish. CONCLUSIONS: The characterized zebrafish Gnrh3 decapeptide exhibits complete homology to the Atlantic salmon (Salmo salar) GnRH-III variant. In silico analysis of mammalian and teleost GnRH promoters revealed a conserved enhancer possessing binding sites for Oct-1, CREB and Sp1. Transgenic and transient reporter gene expression in zebrafish larvae, confirmed the importance of the in silico defined zebrafish enhancer at -976. The capability of the human GnRH-I promoter of directing cell specific reporter gene expression in zebrafish supports orthology between GnRH-I and GnRH-III.

Journal Article↗
PubMed2006

A flexible approach for optimising in silico ADME/Tox characterisation of lead candidates.

Over the years, multiple in silico solutions have been developed for the early characterisation of lead candidates at early stages of the drug development process. Despite the nascent promise this technology holds for the pharmaceutical and biotech industries, in many cases, inherent limitations in many of these computational technologies still hinders the prediction performance of absorption, distribution, metabolism and excretion (ADME), and toxicological (Tox) properties. However, as the result of recent developments in this arena and key technology collaborations, Bio-Rad Laboratories, Inc. has made some breakthroughs with their in silico ADME/Tox prediction and lead optimisation solutions. The company's KnowItA11 ADME/Tox system, when used in conjunction with Equbits' Foresight support vector machine platform and other best-of-breed partnering technologies, provides an intelligent and flexible approach to in silico modelling that helps to overcome these difficulties. The system ultimately does this by offering various approaches and technologies that can lead researchers toward improvement in results and overall greater confidence in the in silico approach as a whole. In this technology evaluation, several examples and case studies on mutagenicity and hERG-channel blocking illustrate how researchers can take advantage of this system from compound characterisation to knowledge extraction to achieve better and faster results in their research process.

Animals↗
PubMed2006

An integrated in silico gene mapping strategy in inbred mice.

In recent years in silico analysis of common laboratory mice has been introduced and subsequently applied, in slightly different ways, as a methodology for gene mapping. Previously we have demonstrated some limitation of the methodology due to sporadic genetic correlations across the genome. Here, we revisit the three main aspects that affect in silico analysis. First, we report on the use of marker maps: we compared our existing 20,000 SNP map to the newly released 140,000 SNP map. Second, we investigated the effect of varying strain numbers on power to map QTL. Third, we introduced a novel statistical approach: a cladistic analysis, which is well suited for mouse genetics and has increased flexibility over existing in silico approaches. We have found that in our examples of complex traits, in silico analysis by itself does fail to uniquely identify quantitative trait gene (QTG)-containing regions. However, when combined with additional information, it may significantly help to prioritize candidate genes. We therefore recommend using an integrated work flow that uses other genomic information such as linkage regions, regions of shared ancestry, and gene expression information to obtain a list of candidate genes from the genome.

Algorithms↗
PubMed2002

NMR and in silico screening.

NMR-based screening and virtual, or in silico, screening can be highly complementary and synergistic. NMR-based screening is a rapid and reliable method for validating hits that come from in silico screens. In addition, ligand-binding data derived from NMR-based screens can focus and direct subsequent in silico screening. We will first give a short overview of existing NMR and in silico screening methods, discuss the drawbacks associated with each, and finally present applications that highlight the combination of the two technologies.

Algorithms↗
PubMed1996

Shear bond strength of a composite resin to differently silicoated base metal alloys.

This study was undertaken to determine the shear bond strength of Charisma composite resin to both thermal and pyrogenic silicoated base metal alloys namely, Duracast MS, Rexillium V and Wironit extra-hard. The bonded specimens were subjected to three stress treatments prior to shear bond testing. The results indicated that both silicoating techniques significantly improved shear bond strengths to sandblasted alloys, whereas wet heat and thermocycling treatments reduced the bond strengths, except for pyrogenic silicoated Wironit. Thermocycling was more detrimental to thermal than to pyrogenic silicoated Rexillium nad Wironit specimens.

Composite Resins↗
PubMed2024

Exploring novel MYH7 gene variants using in silico analyses in Korean patients with cardiomyopathy.

BACKGROUND: Pathogenic variants of MYH7, which encodes the beta-myosin heavy chain protein, are major causes of dilated and hypertrophic cardiomyopathy. METHODS: In this study, we used whole-genome sequencing data to identify MYH7 variants in 397 patients with various cardiomyopathy subtypes who were participating in the National Project of Bio Big Data pilot study in Korea. We also performed in silico analyses to predict the pathogenicity of the novel variants, comparing them to known pathogenic missense variants. RESULTS: We identified 27 MYH7 variants in 41 unrelated patients with cardiomyopathy, consisting of 20 previously known pathogenic/likely pathogenic variants, 2 variants of uncertain significance, and 5 novel variants. Notably, the pathogenic variants predominantly clustered within the myosin motor domain of MYH7. We confirmed that the novel identified variants could be pathogenic, as indicated by high prediction scores in the in silico analyses, including SIFT, Mutation Assessor, PROVEAN, PolyPhen-2, CADD, REVEL, MetaLR, MetaRNN, and MetaSVM. Furthermore, we assessed their damaging effects on protein dynamics and stability using DynaMut2 and Missense3D tools. CONCLUSIONS: Overall, our study identified the distribution of MYH7 variants among patients with cardiomyopathy in Korea, offering new insights for improved diagnosis by enriching the data on the pathogenicity of novel variants using in silico tools and evaluating the function and structural stability of the MYH7 protein.

Humans↗
PubMed2006

In silico assessment of gene function involved in cysteine biosynthesis in Arabidopsis: expression analysis of multiple isoforms of serine acetyltransferase.

In plants, the inorganic sulfur is first fixed into cysteine by the cysteine biosynthetic pathway. This biosynthetic pathway of cysteine involves several enzymatic reactions. In Arabidopsis thaliana, multiple isoforms seem to participate in each enzymatic step for cysteine biosynthesis. To obtain more insights on the specific role of each isoform involved in the cysteine biosynthesis, in silico analysis of these isoforms using Arabidopsis expressed sequence tags (EST) database was carried out. This EST database analysis revealed distinct population distribution of ESTs among multiple isoforms, suggesting that each isoform has its particular expression pattern, presumably associated with its specific role in cysteine biosynthesis. As another in silico analysis, co-expression analysis of genes involved in sulfur metabolism in Arabidopsis was performed using a public transcriptome database of DNA microarrays. This co-expression analysis also suggested specific function and co-regulation of some isoform genes for cysteine biosynthesis by consideration on the clustering of co-expressed genes. From the results of sensitivity to feedback regulation, subcellular localization and expression of mRNA analyses, each serine acetyltransferase (SATase) isoform seems to have its specific role for cysteine biosynthesis. Similar expression patterns were observed between the experimental results of expression data for SATase isoforms and the in silico results of "digital northern" analysis using EST database.

Arabidopsis↗
PubMed2006

In silico modeling of non-linear drug absorption for the P-gp substrate talinolol and of consequences for the resulting pharmacodynamic effect.

PURPOSE: The aim of the present work was to demonstrate P-glycoprotein's involvement in the non-linear talinolol pharmacokinetics using an advanced compartment and transit model (ACAT) and to compare the results predicted from the model to the finding of a phase I dose escalation study with oral talinolol doses increasing from 25 to 400 mg. MATERIALS AND METHODS: Besides minimum input parameters for the compound (pKa(s), solubility at one or more pH's, Peff, doses, formulation, diffusivity), physiological and pharmacokinetic properties, transporter data are included in these predictions. The simulations assumed higher expression levels in lower gastrointestinal regions, in particular in the colon, which is in accordance with the results of intestinal rat perfusion studies and intestinal distribution data from rats, catfishes, micropigs and humans reported in the literature. Optimized values for P-glycoprotein (P-gp) Km and Vmax were used for the final simulation results and for a stochastic virtual trial with 12 patients. RESULTS: Talinolol, a P-gp substrate, exhibits non-linear dose AUC relationship after administration of 25, 50, 100 and 400 mg immediate-release tablets. This dose dependency is due to a decrease of efflux transport caused by saturation of P-gp by talinolol. It was found that oral bioavailability increases after administration of higher doses of talinolol. The predicted bioavailability of the p.o. 25, 50, 100 and 400 mg doses of talinolol was 64, 76, 85, 94%, respectively. Pharmacokinetic parameters (AUC, Cmax) from in silico simulations are within acceptable range comparing with data, observed in vivo. However, the in vitro value of Km for talinolol's interactions with P-gp could not be used in the simulation and still reproduce the observed non-linear dose dependence. For each of the four doses, GastroPlus was used to model pharmacodynamic (PD) response and to optimize the values of CLe, Emax, and EC5o with the effect compartment linked indirectly to the central compartment. For all simulations, EC50 was 114 nM and E0 was 83 bpm. CONCLUSION: Comparison between the results of the in vivo study and the in silico simulations determined the quality and reliability of the in silico predictions and demonstrate the simulation of dose dependent absorption. In contrast to previous simulation work for the non-linear dose dependence of interaction with intestinal transporters or enterocyte metabolism, optimized Km and Vmax values were required to reproduce the clinically observed non-linear dose dependence. The model developed may be useful in the prediction of absorption of other P-gp substrates including pharmacodynamic consequences.

ATP Binding Cassette Transporter, Subfamily B, Mem↗
PubMed2006

Streamlining lead discovery by aligning in silico and high-throughput screening.

Lead discovery in the pharmaceutical environment is largely an industrial-scale process in which it is typical to screen 1-5 million compounds in a matter of weeks using High Throughput Screening (HTS). This process is a very costly endeavor. Typically a HTS campaign of 1 million compounds will cost anywhere from $500000 to $1000000. There is consequently a great deal of pressure to maximize the return on investment by finding fast and more effective ways to screen. A panacea that has emerged over the past few years to help address this issue is in silico screening. In silico screening is now incorporated in all areas of lead discovery; from target identification and library design, to hit analysis and compound profiling. However, as lead discovery has evolved over the past few years, so has the role of in silico screening.

Bayes Theorem↗