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Results for “In silico”

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PubMed2004

In silico evaluation of molecular probes for detection and identification of Ralstonia solanacearum and Clavibacter michiganensis subsp. sepedonicus.

Ralstonia solanacerum and Clavibacter michiganensis subsp. sepedonicus are the two most relevant bacterial pathogens of potato for which a large number of molecular diagnostic methods using specific DNA sequences have been developed. About one hundred oligonucleotides have been described and thoroughly tested experimentally. After having compiled and evaluated all these primers and probes in silico to check their specificity, many discrepancies were found. A detailed analysis permitted the recognition of different possible reasons for such discrepancies: sequencing errors in public sequences, wrong supposed specificity (sometimes due to more recent sequences than the oligonucleotides being evaluated) or even typing errors in the oligonucleotides. Although this study is an exercise about in silico evaluation using two potato bacterial pathogens as a model, the conclusions reflect not only information useful for phytopathologists but, in a broader scope, draw the main situations that can be found during an evaluation of probes, which can be surely found in other scenarios.

Actinomycetales↗

PubMed2006

Fuzzy-oil-drop hydrophobic force field--a model to represent late-stage folding (in silico) of lysozyme.

A model of hydrophobic collapse (in silico), which is generally considered to be the driving force for protein folding, is presented in this work. The model introduces the external field in the form of a fuzzy-oil-drop assumed to represent the environment. The drop is expressed in the form of a three-dimensional Gauss function. The usual probability value is assumed to represent the hydrophobicity distribution in the three-dimensional space of the virtual environment. The differences between this idealized hydrophobicity distribution and the one represented by the folded polypeptide chain is the parameter to be minimized in the structure optimization procedure. The size of fuzzy-oil-drop is critical for the folding process. A strong correlation between protein length and the dimension of the native and early-stage molecular form was found on the basis of single-domain proteins analysis. A previously presented early-stage folding (in silico) model was used to create the starting structure for the procedure of late-stage folding of lysozyme. The results of simulation were found to be promising, although additional improvements for the formation of beta-structure and disulfide bonds as well as the participation of natural ligand in folding process seem to be necessary.

Computer Simulation↗

PubMed2005

An in silico ensemble method for lead discovery: decision forest.

Recent progress in combinatorial chemistry and parallel synthesis has radically changed the approach to drug discovery in the pharmaceutical industry. At present, thousands of compounds can be made in a short period, creating a need for fast and effective in silico methods to select the most promising lead candidates. Decision forest is a novel pattern recognition method, which combines the results of multiple distinct but comparable decision tree models to reach a consensus prediction. In this article, a decision forest model was developed using a structurally diverse training data set containing 232 compounds whose estrogen receptor binding activity was tested at the U.S. Food and Drug Administration (FDA)'s National Center for Toxicological Research (NCTR). The model was subsequently validated using a test data set of 463 compounds selected from the literature, and then applied to a large data set with 57,145 compounds as a screening example. The results show that the decision forest method is a fast, reliable and effective in silico approach, which could be useful in drug discovery.

Decision Trees↗

PubMed2006

Adapters, shims, and glue--service interoperability for in silico experiments.

MOTIVATION: Computationally, in silico experiments in biology are workflows describing the collaboration of people, data and methods. The Grid and Web services are proposed to be the next generation infrastructure supporting the deployment of bioinformatics workflows. But the growing number of autonomous and heterogeneous services pose challenges to the used middleware w.r.t. composition, i.e. discovery and interoperability of services required within in silico experiments. In the IRIS project, we handle the problem of service interoperability by a semi-automatic procedure for identifying and placing customizable adapters into workflows built by service composition. RESULTS: We show the effectiveness and robustness of the software-aided composition procedure by a case study in the field of life science. In this study we combine different database services with different analysis services with the objective of discovering required adapters. Our experiments show that we can identify relevant adapters with high precision and recall.

Computational Biology↗

PubMed2004

In silico design of clinical trials: a method coming of age.

OBJECTIVE: To determine the feasibility and potential usefulness of mathematical models in evaluating immunomodulatory strategies in clinical trials of severe sepsis. DESIGN: Mathematical modeling of immunomodulation in simulated patients. SETTING: Computer laboratory. MEASUREMENTS AND MAIN RESULTS: We introduce and evaluate the concept of conducting a randomized clinical trial in silico based on simulated patients generated from a mechanistic mathematical model of bacterial infection, the acute inflammatory response, global tissue dysfunction, and a therapeutic intervention. Trial populations are constructed to reflect heterogeneity in bacterial load and virulence as well as propensity to mount and modulate an inflammatory response. We constructed a cohort of 1,000 trial patients submitted to therapy with one of three different doses of a neutralizing antibody directed against tumor necrosis factor (anti-TNF) for 6, 24, or 48 hrs. We present cytokine profiles over time and expected outcome for each cohort. We identify subgroups with high propensity for being helped or harmed by the proposed intervention and identify early serum markers for each of those subgroups. The mathematical simulation confirms the inability of simple markers to predict outcome of sepsis. The simulation clearly separates cases with favorable and unfavorable outcome on the basis of global tissue dysfunction. Control survival was 62.9% at 1 wk. Depending on dose and duration of treatment, survival ranged from 57.1% to 80.8%. Higher doses of anti-TNF, although effective, also result in considerable harm to patients. A statistical analysis based on a simulated cohort identified markers of favorable or adverse response to anti-TNF treatment. CONCLUSIONS: A mathematical simulation of anti-TNF therapy identified clear windows of opportunity for this intervention as well as populations that can be harmed by anti-TNF therapy. The construction of an in silico clinical trial could provide profound insight into the design of clinical trials of immunomodulatory therapies, ranging from optimal patient selection to individualized dosage and duration of proposed therapeutic interventions.

Antibodies, Monoclonal↗

PubMed2003

Importance of input perturbations and stochastic gene expression in the reverse engineering of genetic regulatory networks: insights from an identifiability analysis of an in silico network.

Gene expression profiles are an increasingly common data source that can yield insights into the functions of cells at a system-wide level. The present work considers the limitations in information content of gene expression data for reverse engineering regulatory networks. An in silico genetic regulatory network was constructed for this purpose. Using the in silico network, a formal identifiability analysis was performed that considered the accuracy with which the parameters in the network could be estimated using gene expression data and prior structural knowledge (which transcription factors regulate which genes) as a function of the input perturbation and stochastic gene expression. The analysis yielded experimentally relevant results. It was observed that, in addition to prior structural knowledge, prior knowledge of kinetic parameters, particularly mRNA degradation rate constants, was necessary for the network to be identifiable. Also, with the exception of cases where the noise due to stochastic gene expression was high, complex perturbations were more favorable for identifying the network than simple ones. Although the results may be specific to the network considered, the present study provides a framework for posing similar questions in other systems.

Computational Biology↗

PubMed2003

Biosynthesis of camptothecin. In silico and in vivo tracer study from [1-13C]glucose.

Camptothecin derivatives are clinically used antitumor alkaloids that belong to monoterpenoid indole alkaloids. In this study, we investigated the biosynthetic pathway of camptothecin from [1-13C]glucose (Glc) by in silico and in vivo studies. The in silico study measured the incorporation of Glc into alkaloids using the Atomic Reconstruction of Metabolism software and predicted the labeling patterns of successive metabolites from [1-13C]Glc. The in vivo study followed incorporation of [1-13C]Glc into camptothecin with hairy roots of Ophiorrhiza pumila by 13C nuclear magnetic resonance spectroscopy. The 13C-labeling pattern of camptothecin isolated from the hairy roots clearly showed that the monoterpene-secologanin moiety was synthesized via the 2C-methyl-D-erythritol 4-phosphate pathway, not via the mevalonate pathway. This conclusion was supported by differential inhibition of camptothecin accumulation by the pathway-specific inhibitors (fosmidomycin and lovastatin). The quinoline moiety from tryptophan was also labeled as predicted by the Atomic Reconstruction of Metabolism program via the shikimate pathway. These results indicate that camptothecin is formed by the combination of the 2C-methyl-D-erythritol 4-phosphate pathway and the shikimate pathway. This study provides the innovative example for how a computer-aided comprehensive metabolic analysis will refine the experimental design to obtain more precise biological information.

Camptothecin↗

PubMed2004

Control of state transitions in an in silico model of epilepsy using small perturbations.

We propose the use of artificial neural networks in an in silico epilepsy model of biological neural networks: 1) to predict the onset of state transitions from higher complexities, possibly chaotic to lower complexity possibly rhythmic activities; and 2) to restore the original higher complexity activity. A coupled nonlinear oscillators model (Bardakjian and Diamant, 1994) was used to represent the spontaneous seizure-like oscillations of CA3 hippocampal neurons (Bardakjian and Aschebrenner-Scheibe, 1995) to illustrate the prediction and control schemes of these state transition onsets. Our prediction scheme consists of a recurrent neural network having Gaussian nonlinearities. When the onset of lower complexity activity is predicted in the in silico model, then our control scheme consists of applying a small perturbation to a system variable (i.e., the transmembrane voltage) when it is sufficiently close to the unstable higher complexity manifold. The system state can be restored back to its higher complexity mode utilizing the forces of the system's vector field.

Action Potentials↗

PubMed2006

New algorithms and an in silico benchmark for computational enzyme design.

The creation of novel enzymes capable of catalyzing any desired chemical reaction is a grand challenge for computational protein design. Here we describe two new algorithms for enzyme design that employ hashing techniques to allow searching through large numbers of protein scaffolds for optimal catalytic site placement. We also describe an in silico benchmark, based on the recapitulation of the active sites of native enzymes, that allows rapid evaluation and testing of enzyme design methodologies. In the benchmark test, which consists of designing sites for each of 10 different chemical reactions in backbone scaffolds derived from 10 enzymes catalyzing the reactions, the new methods succeed in identifying the native site in the native scaffold and ranking it within the top five designs for six of the 10 reactions. The new methods can be directly applied to the design of new enzymes, and the benchmark provides a powerful in silico test for guiding improvements in computational enzyme design.

Algorithms↗

PubMed2004

High throughput T-DNA insertion mutagenesis in rice: a first step towards in silico reverse genetics.

A library of 29,482 T-DNA enhancer trap lines has been generated in rice cv. Nipponbare. The regions flanking the T-DNA left border from the first 12,707 primary transformants were systematically isolated by adapter anchor PCR and sequenced. A survey of the 7480 genomic sequences larger than 30 bp (average length 250 bp), representing 56.4% of the total readable sequences and matching the rice bacterial artificial chromosome/phage artificial chromosome (BAC/PAC) sequences assembled in pseudomolecules allowed the assigning of 6645 (88.8%) T-DNA insertion sites to at least one position in the rice genome of cv. Nipponbare. T-DNA insertions appear to be rather randomly distributed over the 12 rice chromosomes, with a slightly higher insertion frequency in chromosomes 1, 2, 3 and 6. The distribution of 723 independent T-DNA insertions along the chromosome 1 pseudomolecule did not differ significantly from that of the predicted coding sequences in exhibiting a lower insertion density around the centromere region and a higher density in the subtelomeric regions where the gene density is higher. Further establishment of density graphs of T-DNA inserts along the recently released 12 rice pseudomolecules confirmed this non-uniform chromosome distribution. T-DNA appeared less prone to hot spots and cold spots of integration when compared with those revealed by a concurrent assignment of the Tos17 retrotransposon flanking sequences deposited in the National Center for Biotechnology Information (NCBI). T-DNA inserts rarely integrated into repetitive sequences. Based on the predicted gene annotation of chromosome 1, preferential insertion within the first 250 bp from the putative ATG start codon has been observed. Using 4 kb of sequences surrounding the insertion points, 62% of the sequences showed significant similarity to gene encoding known proteins (E-value < 1.00 e(-05)). To illustrate the in silico reverse genetic approach, identification of 83 T-DNA insertions within genes coding for transcription factors (TF) is presented. Based both on the estimated number of members of several large TF gene families (e.g. Myb, WRKY, HD-ZIP, Zinc-finger) and on the frequency of insertions in chromosome 1 predicted genes, we could extrapolate that 7-10% of the rice gene complement is already tagged by T-DNA insertion in the 6116 independent transformant population. This large resource is of high significance while assisting studies unravelling gene function in rice and cereals, notably through in silico reverse genetics.

Base Sequence↗

PubMed2005

In silico predictions of drug solubility and permeability: two rate-limiting barriers to oral drug absorption.

Aqueous drug solubility and intestinal drug permeability are two of the most important factors influencing drug absorption. If the developability of a drug is to be included in the lead optimization, new experimental and computational models of solubility and permeability are needed. These models must have the capacity to handle a large amount of data. Nowadays, epithelial cell culture models such as Caco-2 are routinely used to assess intestinal drug permeability and transport in drug discovery settings. The permeability values obtained from the Caco-2 cell monolayers have been traditionally used to devise in silico models for the prediction of drug absorption. In this paper, the use of molecular surface areas as descriptors of permeability and solubility will be reviewed. Moreover, a virtual filter for the prediction of oral drug developability based on the successful combination of in vitro and in silico models of drug permeability and aqueous drug solubility will be discussed.

Administration, Oral↗

PubMed2025

In silico prediction method for plant Nucleotide-binding leucine-rich repeat- and pathogen effector interactions.

Plant Nucleotide-binding leucine-rich repeat (NLR) proteins play a crucial role in effector recognition and activation of Effector triggered immunity following pathogen infection. Genome sequencing advancements have led to the identification of a myriad of NLRs in numerous agriculturally important plant species. However, deciphering which NLRs recognize specific pathogen effectors remains challenging. Predicting NLR-effector interactions in silico will provide a more targeted approach for experimental validation, critical for elucidating function, and advancing our understanding of NLR-triggered immunity. In this study, NLR-effector protein complex structures were predicted using AlphaFold2-Multimer for all experimentally validated NLR-effector interactions reported in literature. Binding affinities- and energies were predicted using 97 machine learning models from Area-Affinity. We show that AlphaFold2-Multimer predicted structures have acceptable accuracy and can be used to investigate NLR-effector interactions in silico. Binding affinities for 58 NLR-effector complexes ranged between -8.5 and -10.6 log(K), and binding energies between -11.8 and -14.4 kcal/mol-1, depending on the Area-Affinity model used. For 2427 "forced" NLR-effector complexes, these estimates showed larger variability, enabling identification of novel NLR-effector interactions with 99% accuracy using an Ensemble machine learning model. The narrow range of binding energies- and affinities for "true" interactions suggest a specific change in Gibbs free energy, and thus conformational change, is required for NLR activation. This is the first study to provide a method for predicting NLR-effector interactions, applicable to all pathosystems. Finally, the NLR-Effector Interaction Classification (NEIC) resource can streamline research efforts by identifying NLRs important for plant-pathogen resistance, advancing our understanding of plant immunity.

Plant Proteins↗

PubMed2006

In silico and in vitro screening for inhibition of cytochrome P450 CYP3A4 by comedications commonly used by patients with cancer.

Cytochrome P450 3A4 (CYP3A4) is the major enzyme responsible for phase I drug metabolism of many anticancer agents. It is also a major route for metabolism of many drugs used by patients to treat the symptoms caused by cancer and its treatment as well as their other illnesses, for example, cardiovascular disease. To assess the ability to inhibit CYP3A4 of drugs most commonly used by our patients during cancer therapy, we have made in silico predictions based on the crystal structures of CYP3A4. From this set of 33 common comedicated drugs, 10 were predicted to be inhibitors of CYP3A4, with the antidiarrheal drug loperamide predicted to be the most potent. There was significant correlation (r(2) = 0.75-0.66) between predicted affinity and our measured IC(50) values, and loperamide was confirmed as a potent inhibitor (IC(50) of 0.050 +/- 0.006 microM). Active site docking studies predicted an orientation of loperamide consistent with formation of the major (N-demethylated) metabolite, where it interacts with the phenylalanine cluster and Arg-212 and Glu-374; experimental evidence for the latter interaction comes from the approximately 12-fold increase in K(M) for loperamide observed for the Glu-374-Gln mutant. The commonly prescribed drugs loperamide, amitriptyline, diltiazem, domperidone, lansoprazole, omeprazole, and simvastatin were identified by our in silico and in vitro screens as relatively potent inhibitors of CYP3A4 that have the potential to interact with cytotoxic agents to cause adverse effects, highlighting the likelihood of drug-drug interactions affecting chemotherapy treatment.

Antidiarrheals↗

PubMed2006

Absorption and metabolism of Astragali radix decoction: in silico, in vitro, and a case study in vivo.

To profile absorption of Astragali Radix decoction and identify its orally absorbable constituents and their metabolites, four complementary in silico, in vitro, and in vivo methods, i.e., a computational chemistry prediction method, a Caco-2 cell monolayer model experiment, an improved rat everted gut sac experiment, and a healthy human volunteer experiment, were used. According to the in silico computation result, 26 compounds of Astragali Radix could be regarded as orally available compounds, including 12 flavonoids. In the in vitro and in vivo experiments, 21 compounds were tentatively identified by high-performance liquid chromatography-diode array detection-electrospray ion trap tandem mass spectrometry data, which involved calycosin, formononetin, (6aR,11aR)-3-hydroxy-9,10-dimethoxypterocarpan, 7,2'-dihydroxy-3',4'-dimethoxyisoflavan, calycosin-7-O-beta-D-glucoside, formononetin-7-O-beta-D-glucoside, 7,2'-dihydroxy-3',4'-dimethoxyisoflavan-7-O-beta-D-glucoside-6''-O-malonate, (6aR,11aR)-3-hydroxy-9,10-dimethoxypterocarpan-3-O-beta-D-glucoside, and phase II metabolites calycosin-7-O-beta-D-glucuronide, formononetin-7-O-beta-D-glucuronide, (6aR,11aR)-3-hydroxy-9,10-dimethoxypterocarpan-3-O-beta-D-glucuronide, 7,2'-dihydroxy-3',4'-dimethoxyisoflavan-7-O-beta-D-glucuronide, and calycosin sulfate. Calycosin and formononetin were proved absorbable by four methods; (6aR,11aR)-3-hydroxy-9,10-dimethoxypterocarpan and 7,2'-dihydroxy-3',4'-dimethoxyisoflavan were proved absorbable by three methods; formononetin-7-O-beta-D-glucoside and (6aR,11aR)-3-hydroxy-9,10-dimethoxypterocarpan-3-O-beta-D-glucoside were proved absorbable by two methods. The existence of calycosin-7-O-beta-D-glucuronide, formononetin-7-O-beta-D-glucuronide, (6aR,11aR)-3-hydroxy-9,10-dimethoxypterocarpan-3-O-beta-D-glucuronide, 7,2'-dihydroxy-3',4'-dimethoxyisoflavan-7-O-beta-D-glucuronide, and calycosin sulfate was proved by two or three methods. We found that besides isoflavones, pterocarpans and isoflavans also could be metabolized by the intestine during absorption, and the major metabolites were glucuronides. In conclusion, the present study demonstrated that the flavonoids in Astragali Radix decoction, including isoflavones, pterocarpans, and isoflavans, could be absorbed and metabolized by the intestine. These absorbable compounds, which were reported to have various bioactivities related to the curative effects of Astragali Radix decoction, could be regarded as an important component of the effective constituents of Astragali Radix decoction.

Adult↗

PubMed2005

Expanded metabolic reconstruction of Helicobacter pylori (iIT341 GSM/GPR): an in silico genome-scale characterization of single- and double-deletion mutants.

Helicobacter pylori is a human gastric pathogen infecting almost half of the world population. Herein, we present an updated version of the metabolic reconstruction of H. pylori strain 26695 based on the revised genome annotation and new experimental data. This reconstruction, iIT341 GSM/GPR, represents a detailed review of the current literature about H. pylori as it integrates biochemical and genomic data in a comprehensive framework. In total, it accounts for 341 metabolic genes, 476 intracellular reactions, 78 exchange reactions, and 485 metabolites. Novel features of iIT341 GSM/GPR include (i) gene-protein-reaction associations, (ii) elementally and charge-balanced reactions, (iii) more accurate descriptions of isoprenoid and lipopolysaccharide metabolism, and (iv) quantitative assessments of the supporting data for each reaction. This metabolic reconstruction was used to carry out in silico deletion studies to identify essential and conditionally essential genes in H. pylori. A total of 128 essential and 75 conditionally essential metabolic genes were identified. Predicted growth phenotypes of single knockouts were validated using published experimental data. In addition, in silico double-deletion studies identified a total of 47 synthetic lethal mutants involving 67 different metabolic genes in rich medium.

Biotin↗

PubMed2026

Probiogenomic analysis of functional potential and safety of L. plantarum 8p-a3 and DMC-S1 strains: in silico vs in vitro and in vivo data.

The molecular basis of the beneficial effects and the causes of the negative effects of probiotics are not entirely clear. Clarifying these issues is important for understanding the biology and assessing the safety of the microbes. Omics technologies have opened up new resources for obtaining relevant knowledge. Here, for the first time, we present the results of a comparative analysis of the functional potential and safety of two L. plantarum strains: the approved probiotic 8p-a3 and the Drosophila intestinal resident, which exhibit opposite effects on D. melanogaster as the model host organism. Through genomic analysis, extracellular vesicle studies, and in vitro and in vivo assays, we have identified the common and specific characteristics of the strains. The strains proved to be similar in a set of genes that determine benefits to the host organism, as well as in the presence of some risk factors. Significant differences between the strains are related to genes responsible for adhesion, sialic acid metabolism, mucin degradation, antimicrobial peptides, tannin resistance, and immunomodulation. In silico data correlated with in vitro and in vivo data, with the exception of antimicrobial sensitivity. Pronounced differences between the strains were found in terms of the composition and biological effects of their vesicles. In vivo data on the effects of the strains correlate with the corresponding data of their vesicles in the fruit fly model. The results obtained open up new facets in L. plantarum strains relevant for evaluating the functionality and safety of probiotics.IMPORTANCEUsing a probiogenomic approach, common and specific features regarding functionality and safety were identified in the strains (the approved probiotic strain L. plantarum 8p-a3 and the Drosophila intestinal bacterium L. plantarum DMC-S1), which exhibit opposite effects on the model host organism (D. melanogaster). The genomic analysis was supplemented by the analysis of extracellular vesicles of the strains. Comparative analysis of in silico data in combination with in vitro and in vivo studies was performed, and unexpected capabilities of the strains were discovered. Novel factors, essential for evaluating the safety of probiotics, were identified. New facets in the interplay of probiotic bacterium with host organism have been revealed.

Animals↗

PubMed2005

In silico phenotypic screening method of mutants based on statistical modeling of genetically mixed samples.

In comprehensive functional genomics projects, systematic analysis of phenotypes is vital. However, conventional phenotypic screening is done mainly by imprecise visual observation of qualitative traits, and, therefore, in silico screening techniques for quantitative traits are required. In this report, we propose in silico phenotypic screening method that utilizes a Gaussian mixture model for the trait distribution in the offspring of a mutagenized line and the likelihood ratio test between the estimated Gaussian mixture model and the wild-type single Gaussian model. In order to evaluate the proposed method, we performed a screening experiment using real trait data of Arabidopsis. In this experiment, the proposed screening method properly distinguished the mutant line from the wild-type line. Furthermore, we conducted power analysis of the proposed method and two conventional methods under various simulated conditions of sample size and distribution of trait frequency. The result of the power analysis confirmed the effectiveness of the proposed method compared to the conventional methods.

Algorithms↗

PubMed2004

Predicting human drug glucuronidation parameters: application of in vitro and in silico modeling approaches.

Cytochrome P450 (CYP) and UDP-glucuronosyltransferase (UGT), which both exist as enzyme "superfamilies," are together responsible for the metabolism of most hepatically cleared drugs. There is currently intense interest in the development of techniques that permit identification of the CYP and UGT isoform(s) involved in the metabolism of a newly discovered drug, and hence prediction of factors likely to alter elimination in vivo. In addition, the quantitative scaling of kinetic parameters for a metabolic pathway assumes importance for identifying newly discovered drugs with undesirable in vivo pharmacokinetic properties. Although qualitative and quantitative in vitro-in vivo correlation based on data generated using human liver tissue or recombinant enzymes have been applied successfully to many drugs eliminated by CYP, these strategies have proved less definitive for glucuronidated compounds. Computational (in silico) modeling techniques that potentially provide a facile and economic alternative to the in vitro methods are now emerging. This review assesses the utility of in vitro and in silico approaches for the qualitative and quantitative prediction of drug glucuronidation parameters and the challenges facing the development of generalizable models.

Cytochrome P-450 Enzyme System↗