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PubMed2002

An integrative model for in-silico clinical-genomics discovery science.

Human Genome discovery research has set the pace for Post-Genomic Discovery Research. While post-genomic fields focused at the molecular level are intensively pursued, little effort is being deployed in the later stages of molecular medicine discovery research, such as clinical-genomics. The objective of this study is to demonstrate the relevance and significance of integrating mainstream clinical informatics decision support systems to current bioinformatics genomic discovery science. This paper is a feasibility study of an original model enabling novel "in-silico" clinical-genomic discovery science and that demonstrates its feasibility. This model is designed to mediate queries among clinical and genomic knowledge bases with relevant bioinformatic analytic tools (e.g. gene clustering). Briefly, trait-disease-gene relationships were successfully illustrated using QMR, OMIM, SNOMED-RT, GeneCluster and TreeView. The analyses were visualized as two-dimensional dendrograms of clinical observations clustered around genes. To our knowledge, this is the first study using knowledge bases of clinical decision support systems for genomic discovery. Although this study is a proof of principle, it provides a framework for the development of clinical decision-support-system driven, high-throughput clinical-genomic technologies which could potentially unveil significant high-level functions of genes.

Artificial Intelligence↗
PubMedDate not supplied

Can 'Bacterial-Metabolite-Likeness' model improve odds of 'in silico' antibiotic discovery?

'Inductive' QSAR descriptors have been used to develop the series of QSAR models enabling 'in silico' distinguishing between antimicrobial compounds, conventional drugs, and druglike substances. The constructed neural network-based models operating by 30 'inductive' parameters have been validated on an extensive set of 2686 chemical structures and resulted in up to 97% accurate separation of the three types of molecular activities. The demonstrated ability of 'inductive' parameters to adequately capture molecular features determining 'antibiotic-like' and 'druglike' potentials have been further utilized to construct a model of 'Bacterial-Metabolite-Likeness' (BML). The same 'inductive' descriptors have been used to train a neural network that could very accurately recognize substances involved into bacterial metabolism (that have been experimentally identified). When the developed model has been applied to the mixed set of antimicrobials, drugs, and druglike chemicals (not used for training the BML model), it exhibited a 2-5-fold recognition preference toward antimicrobial compounds compared to general drugs and an 18- to 45-fold preference when compared to a druglike substance (depending on the model stringency). These results illustrate immanent similarity between conventional antimicrobials and native bacterial metabolites and suggest that the developed BML model can be an effective classification tool for 'in silico' antibiotic studies.

Anti-Bacterial Agents↗
PubMed2004

PREDICT modeling and in-silico screening for G-protein coupled receptors.

G-protein coupled receptors (GPCRs) are a major group of drug targets for which only one x-ray structure is known (the nondrugable rhodopsin), limiting the application of structure-based drug discovery to GPCRs. In this paper we present the details of PREDICT, a new algorithmic approach for modeling the 3D structure of GPCRs without relying on homology to rhodopsin. PREDICT, which focuses on the transmembrane domain of GPCRs, starts from the primary sequence of the receptor, simultaneously optimizing multiple 'decoy' conformations of the protein in order to find its most stable structure, culminating in a virtual receptor-ligand complex. In this paper we present a comprehensive analysis of three PREDICT models for the dopamine D2, neurokinin NK1, and neuropeptide Y Y1 receptors. A shorter discussion of the CCR3 receptor model is also included. All models were found to be in good agreement with a large body of experimental data. The quality of the PREDICT models, at least for drug discovery purposes, was evaluated by their successful utilization in in-silico screening. Virtual screening using all three PREDICT models yielded enrichment factors 9-fold to 44-fold better than random screening. Namely, the PREDICT models can be used to identify active small-molecule ligands embedded in large compound libraries with an efficiency comparable to that obtained using crystal structures for non-GPCR targets.

Algorithms↗
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↗
PubMed2006

RET is constitutively activated by novel tandem mutations that alter the active site resulting in multiple endocrine neoplasia type 2B.

Constitutive activation of the RET receptor tyrosine kinase underlies the genesis and progression of multiple endocrine neoplasia type 2 (MEN 2), a dominantly inherited cancer predisposition. Importantly, although kinase activation represents a common theme in neoplasias, not all activating mutations are functionally equivalent. Consistent with this, we ascertained a patient with classical features of MEN 2B, but lacking either of the classical mutations in RET (M918T or A883F). Instead, the patient harbors a novel pair of germ line missense mutations in cis at codons 804 and 805. We evaluated the potential physiochemical effects of these substitutions in silico, predicting both to be moderately deleterious in isolation, but severely deleterious in combination. Consistent with this postulate, we show that the identified tandem mutations (V804M/E805K) are biologically active, transforming cells in culture and that their transforming capacity in combination is distinctly synergistic. Furthermore, the V804M/E805K tandem lesion confers resistance to the small molecule receptor tyrosine kinase inhibitor, PP1, suggesting a mode of action distinct from that known for classical MEN 2B mutations. To address this question, we used homology molecular modeling in silico to model the active site of RET. We predict that RET804 constitutes a critical gatekeeper residue that, when mutated in combination with RET805, induces a conformational change in the hinge region that locks the active site in a position permissive for ATP hydrolysis. Our findings have implications both in the clinic and in the successful development of novel kinase-targeted anticancer drugs.

Amino Acid Sequence↗
PubMed2004

Stochastic modeling of the phase-variable pap operon regulation in uropathogenic Escherichia coli.

Regulation of the pap operon in uropathogenic Escherichia coli is phase variable. This phase variation arises from competition between regulatory proteins at two sites within the regulatory region, GATC(dist) and GATC(prox). We have used the available literature data to design a stochastic model of the molecular interactions of pap regulation and expression during growth in a non-glucose environment at 37 degrees C. The resulting wild-type model is consistent with reported data. The wild-type model served as a basis for two "in silico" mutant models for investigating the role of key regulatory components, the GATC(dist) binding site and the PapI interaction with Lrp at the GATC(prox) site. Our results show that competition at GATC(dist) is required for phase variation, as previously reported. However, our results suggest that removal of competition at GATC(dist) does not affect initial state dependence. Additionally, the PapI involvement in Lrp translocation from GATC(prox) to GATC(dist) is required for the initial state dependence but not for phase variation. Our results also predict that pap expression is maximized at low growth rates and minimized at high growth rates. These predictions provide a basis for further experimental investigation.

DNA-Binding Proteins↗
PubMed2006

In silico experimentation with a model of hepatic mitochondrial folate metabolism.

BACKGROUND: In eukaryotes, folate metabolism is compartmentalized and occurs in both the cytosol and the mitochondria. The function of this compartmentalization and the great changes that occur in the mitochondrial compartment during embryonic development and in rapidly growing cancer cells are gradually becoming understood, though many aspects remain puzzling and controversial. APPROACH: We explore the properties of cytosolic and mitochondrial folate metabolism by experimenting with a mathematical model of hepatic one-carbon metabolism. The model is based on known biochemical properties of mitochondrial and cytosolic enzymes. We use the model to study questions about the relative roles of the cytosolic and mitochondrial folate cycles posed in the experimental literature. We investigate: the control of the direction of the mitochondrial and cytosolic serine hydroxymethyltransferase (SHMT) reactions, the role of the mitochondrial bifunctional enzyme, the role of the glycine cleavage system, the effects of variations in serine and glycine inputs, and the effects of methionine and protein loading. CONCLUSION: The model reproduces many experimental findings and gives new insights into the underlying properties of mitochondrial folate metabolism. Particularly interesting is the remarkable stability of formate production in the mitochondria in the face of large changes in serine and glycine input. The model shows that in the presence of the bifunctional enzyme (as in embryonic tissues and cancer cells), the mitochondria primarily support cytosolic purine and pyrimidine synthesis via the export of formate, while in adult tissues the mitochondria produce serine for gluconeogenesis.

Animals↗
PubMedDate not supplied

In silico prediction of blood brain barrier permeability: an Artificial Neural Network model.

This paper has two objectives: first to develop an in silico model for the prediction of blood brain barrier permeability of new chemical entities and second to find the role of active transport specific to the P-glycoprotein (P-gp) substrate probability in blood brain barrier permeability. An Artificial Neural Network (ANN) model has been developed to predict the ratios of the steady-state concentrations of drugs in the brain to those in the blood (logBB) from their molecular structural parameters. Seven descriptors including P-gp substrate probability have been used for model development. The developed model is able to capture a relationship between P-gp and logBB. The predictive ability of the ANN model has also been compared with earlier computational models.

ATP Binding Cassette Transporter, Subfamily B, Mem↗
PubMed2005

Binding of the Bacillus subtilis LexA protein to the SOS operator.

The Bacillus subtilis LexA protein represses the SOS response to DNA damage by binding as a dimer to the consensus operator sequence 5'-CGAACN(4)GTTCG-3'. To characterize the requirements for LexA binding to SOS operators, we determined the operator bases needed for site-specific binding as well as the LexA amino acids required for operator recognition. Using mobility shift assays to determine equilibrium constants for B.subtilis LexA binding to recA operator mutants, we found that several single base substitutions within the 14 bp recA operator sequence destabilized binding enough to abolish site-specific binding. Our results show that the AT base pairs at the third and fourth positions from the 5' end of a 7 bp half-site are essential and that the preferred binding site for a LexA dimer is 5'-CGAACATATGTTCG-3'. Binding studies with LexA mutants, in which the solvent accessible amino acid residues in the putative DNA binding domain were mutated, indicate that Arg-49 and His-46 are essential for binding and that Lys-53 and Ala-48 are also involved in operator recognition. Guided by our mutational analyses as well as hydroxyl radical footprinting studies of the dinC and recA operators we docked a computer model of B.subtilis LexA on the preferred operator sequence in silico. Our model suggests that binding by a LexA dimer involves bending of the DNA helix within the internal 4 bp of the operator.

Bacillus subtilis↗
PubMed2004

PK-PD integration and PK-PD modelling of nonsteroidal anti-inflammatory drugs: principles and applications in veterinary pharmacology.

Much useful information relevant to elucidation of mechanism of action of nonsteroidal anti-inflammatory drugs (NSAIDs) at the molecular level can be obtained from integrating pharmacokinetic (PK) and pharmacodynamic (PD) data, such data being obtained usually, although not necessarily, in separate studies. Integrating PK and PD data can also provide a basis for selecting clinically relevant dosing schedules for subsequent evaluation in disease models and clinical trials. The principles underlying and uses of PK-PD integration are illustrated in this review for phenylbutazone in the horse and cow, carprofen and meloxicam in the horse, carprofen and meloxicam in the cat and nimesulide in the dog. In the PK-PD modelling approach for NSAIDs, the PK and PD data are generated (usually though not necessarily) in vivo in the same investigation and then modelled in silico, usually using the integrated effect compartment or indirect response models. Drug effect is classically modelled with the sigmoidal E(max) (Hill) equation to derive PD parameters which define efficacy, potency and sensitivity. The PK-PD modelling approach for NSAIDs can be undertaken at the molecular level using surrogates of inhibition of cyclooxygenase (COX) isoforms (or indeed other enzymes e.g. 5-lipoxygenase). Examples are provided of the generation of PD parameters for several NSAIDs (carprofen, ketoprofen, vedaprofen, flunixin and tolfenamic acid) in species of veterinary interest (horse, calf, sheep and goat), which indicate that all drugs investigated except vedaprofen were non-selective for COX-1 and COX-2 in the four species investigated under the experimental conditions used, vedaprofen being a COX-1 selective NSAID. In these studies, plasma concentration was linked to COX inhibitory action in the biophase using an effect compartment model. Data for S-(+)-ketoprofen have been additionally subjected to inter-species modelling and allometric scaling of both PK and PD parameters. For several species values of four PK parameters were highly correlated with body weight, whilst values for PD parameters based on COX inhibition lacked allometric relationship with body weight. PK-PD modelling of NSAIDs has also been undertaken using clinical end-points and surrogates for clinical end-points in disease models. By measurement of clinically relevant indices in clinically relevant models, data generated for PD parameters have been used to set dosages and dose intervals for evaluation and confirmation in clinical trials. PK-PD modelling of NSAIDs is likely to prove superior to conventional dose titration studies for dosage schedule determination, as it sweeps the whole of the concentration-effect relationship for all animals and therefore permits determination of genuine PD parameters. It also introduces time as a second independent variable thus allowing prediction of dosage interval. Using indirect response models and clinically relevant indices, PD data have been determined for flunixin, phenylbutazone and meloxicam in the horse, nimesulide in the dog and meloxicam in the cat.

Animals↗
PubMed2006

Kinetic and chemical mechanisms of shikimate dehydrogenase from Mycobacterium tuberculosis.

Mycobacterium tuberculosis shikimate dehydrogenase (MtbSD) catalyzes the fourth reaction in the shikimate pathway, the NADPH-dependent reduction of 3-dehydroshikimate. To gather information on the kinetic mechanism, initial velocity patterns, product inhibition, and primary deuterium kinetic isotope effect studies were performed and the results suggested a steady-state ordered bi-bi kinetic mechanism. The magnitudes of both primary and solvent kinetic isotope effects indicated that the hydride transferred from NADPH and protons transferred from the solvent in the catalytic cycle are not significantly rate limiting in the overall reaction. Proton inventory analysis indicates that one proton gives rise to solvent isotope effects. Multiple isotope effect studies indicate that both hydride and proton transfers are concerted. The pH profiles revealed that acid/base chemistry takes place in catalysis and substrate binding. The MtbSD 3D model was obtained in silico by homology modeling. Kinetic and chemical mechanisms for MtbSD are proposed on the basis of experimental data.

Alcohol Oxidoreductases↗
PubMed2004

Lead identification for modulators of multidrug resistance based on in silico screening with a pharmacophoric feature model.

Considerable effort has been devoted to the characterization of P-glycoprotein - drug interaction in the past. Systematic quantitative structure-activity relationship (QSAR) studies identified both predictive physicochemical parameters and pharmacophoric substructures within homologous series of compounds. Comparative molecular field analysis (CoMFA) led to distinct 3D-QSAR models for propafenone and phenothiazine analogs. Recently, several pharmacophore models have been generated for diverse sets of ligands. Starting from a training set of 15 propafenone-type MDR-modulators, we established a chemical function-based pharmacophore model. The pharmacophoric features identified by this model were (i) one hydrogen bond acceptor, (ii) one hydrophobic area, (iii) two aromatic hydrophobic areas, and (iv) one positive ionizable group. In silico screening of the Derwent World Drug Index using the model led to identification of 28 compounds. Substances retrieved by database screening are diverse in structure and include dihydropyridines, chloroquine analogs, phenothiazines, and terfenadine. On the basis of its general applicability, the presented 3DQSAR model allows in silico screening of virtual compound libraries to identify new potential lead compounds.

ATP Binding Cassette Transporter, Subfamily B, Mem↗
PubMed2003

Large-scale evaluation of in silico gene deletions in Saccharomyces cerevisiae.

A large-scale in silico evaluation of gene deletions in Saccharomyces cerevisiae was conducted using a genome-scale reconstructed metabolic model. The effect of 599 single gene deletions on cell viability was simulated in silico and compared to published experimental results. In 526 cases (87.8%), the in silico results were in agreement with experimental observations when growth on synthetic complete medium was simulated. Viable phenotypes were correctly predicted in 89.4% (496 out of 555) and lethal phenotypes were correctly predicted in 68.2% (30 out of 44) of the cases considered. The in silico evaluation was solely based on the topological properties of the metabolic network which is based on well-established reaction stoichiometry. No interaction or regulatory information was accounted for in the in silico model. False predictions were analyzed on a case-by-case basis for four possible inadequacies of the in silico model: (1) incomplete media composition, (2) substitutable biomass components, (3) incomplete biochemical information, and (4) missing regulation. This analysis eliminated a number of false predictions and suggested a number of experimentally testable hypotheses. A genome-scale in silico model can thus be used to systematically reconcile existing data and fill in our knowledge gaps about an organism.

Computational Biology↗
PubMed2001

Metabolic modeling of microbial strains in silico.

The large volume of genome-scale data that is being produced and made available in databases on the World Wide Web is demanding the development of integrated mathematical models of cellular processes. The analysis of reconstructed metabolic networks as systems leads to the development of an in silico or computer representation of collections of cellular metabolic constituents, their interactions and their integrated function as a whole. The use of quantitative analysis methods to generate testable hypotheses and drive experimentation at a whole-genome level signals the advent of a systemic modeling approach to cellular and molecular biology.

Genome↗
PubMed2002

Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth.

Annotated genome sequences can be used to reconstruct whole-cell metabolic networks. These metabolic networks can be modelled and analysed (computed) to study complex biological functions. In particular, constraints-based in silico models have been used to calculate optimal growth rates on common carbon substrates, and the results were found to be consistent with experimental data under many but not all conditions. Optimal biological functions are acquired through an evolutionary process. Thus, incorrect predictions of in silico models based on optimal performance criteria may be due to incomplete adaptive evolution under the conditions examined. Escherichia coli K-12 MG1655 grows sub-optimally on glycerol as the sole carbon source. Here we show that when placed under growth selection pressure, the growth rate of E. coli on glycerol reproducibly evolved over 40 days, or about 700 generations, from a sub-optimal value to the optimal growth rate predicted from a whole-cell in silico model. These results open the possibility of using adaptive evolution of entire metabolic networks to realize metabolic states that have been determined a priori based on in silico analysis.

Acetic Acid↗
PubMed2025

Interrogating functional connectivity of in vitro neural glia tissue model modulated through integrative control of matrix stiffness and a neurotrophic factor.

Brain function emerges from intricate cellular communication within neural networks. Both In silico neuronal models and primary neuron cells have revealed that the branching architecture of individual neurons determines the bioelectrical signal propagation pattern and dynamics. However, whether stem cell-differentiated neurons can build functional connectivity regulated by neuronal morphology has yet to be determined. Here, we hypothesized that neurite length, branching, or both factors would regulate the functional connectivity of the stem cell-differentiated neural network. We examined this hypothesis by differentiating mouse cortical neural stem cells (NSCs) on Matrigel substrates with varying storage moduli, both with and without basic fibroblast growth factor (bFGF). Interestingly, with bFGF, Matrigel with a storage modulus (G') of 100 Pa drives NSCs to differentiate into neurons with more dendritic branches, while the gel with G' of 50 Pa led to the development of longer neurites with fewer branches. Notably, branch-rich neural networks exhibited an increased frequency of calcium transients. Using a MATLAB-based analysis pipeline incorporating graph theory, we constructed spatial and temporal calcium activity maps, revealing that branching complexity, more than neurite length, correlates with the density and strength of functional neural circuits. Overall, this study demonstrates that the dendritic branching of neurons, modulated with matrix stiffness and neurotrophic factors, is a key element in enhancing the electrophysiological functionality of the stem cell-differentiated neural network. This finding will have a significant impact on efforts to reconstruct functional neural tissue models, advancing both regenerative therapies and unexplored applications, including biological computing.

Animals↗
PubMed1999

Membrane topology of the Rickettsia prowazekii ATP/ADP translocase revealed by novel dual pho-lac reporters.

Here, we report the construction and characterization of dual reporters, consisting of both an Escherichia coli alkaline phosphatase (AP) gene and an alpha-fragment of the beta-galactosidase (BG) gene, for studying membrane protein topology by the gene fusion approach. Each of the reporters, when fused to periplasmic domains of polytopic proteins, produces fusions with high AP activity and, when fused to cytoplasmic domains, produces fusions with high BG activity in E. coli strains capable of alpha-complementation. The dual nature of these reporters simplifies interpretation of data obtained with poorly expressed fusions and allows one to evaluate the reliability of topological data. Deleterious effects resulting from the cell's attempt to export the full-length BG are eliminated in this approach. We describe dual indicator plates that allow for discrimination between colonies bearing cytoplasmic fusions, periplasmic fusions, and no fusions. We have generated a set of fusions to the topologically well-studied lactose permease of E. coli and demonstrated that topological information generated by these new reporters is in good agreement with the existing model. We used this new methodology for the determination of membrane topology of the Rickettsia prowazekii ATP/ADP translocase (Tlc). Our results were in agreement with the proposed in silico topological model in which Tlc traverses the cytoplasmic membrane of E. coli 12 times with its N and C termini facing the cytoplasm.

Alkaline Phosphatase↗