In Silico Model Escherichia Coli Membranes: Simulating a Lipid with a Cyclopropane Ring

ABSTRACT 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 α-ketoglutarate per liter, 2 g of lactate per liter, or 2 g of pyruvate per liter at both 30 and 37°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.

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Validated In Silico Model for Biofilm Formation in Escherichia coli

ACS Synthetic Biology ◽

10.1021/acssynbio.1c00445 ◽

2022 ◽

Author(s):

Purnendu Bhowmik ◽

Sreenath Rajagopal ◽

Rothangamawi Victoria Hmar ◽

Purnima Singh ◽

Pragya Saxena ◽

...

Keyword(s):

Escherichia Coli ◽

In Silico ◽

Biofilm Formation ◽

In Silico Model

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Computer-Aided Discovery in Antimicrobial Research: In Silico Model for Virtual Screening of Potent and Safe Anti-Pseudomonas Agents

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207318666150305144249 ◽

2015 ◽

Vol 18 (3) ◽

pp. 305-314 ◽

Cited By ~ 13

Author(s):

Alejandro Speck-Planche ◽

Maria Cordeiro

Keyword(s):

Virtual Screening ◽

In Silico ◽

In Silico Model ◽

Computer Aided

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Usefulness of collaboration between mathematical models and cell engineering for elucidating complex disease mechanisms and discover effective drugs

European Heart Journal ◽

10.1093/ehjci/ehaa946.3600 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

H Kohjitani ◽

A Kashiwa ◽

T Makiyama ◽

F Toyoda ◽

Y Yamamoto ◽

...

Keyword(s):

Mathematical Model ◽

In Silico ◽

Complex Disease ◽

Ion Selectivity ◽

Public Institution ◽

Cell Engineering ◽

Funding Source ◽

Patch Clamp Technique ◽

In Silico Model

Abstract Background A missense mutation, CACNA1C-E1115K, located in the cardiac L-type calcium channel (LTCC), was recently reported to be associated with diverse arrhythmias. Several studies reported in-vivo and in-vitro modeling of this mutation, but actual mechanism and target drug of this disease has not been clarified due to its complex ion-mechanisms. Objective To reveal the mechanism of this diverse arrhythmogenic phenotype using combination of in-vitro and in-silico model. Methods and results Cell-Engineering Phase: We generated human induced pluripotent stem cell (hiPSC) from a patient carrying heterozygous CACNA1C-E1115K and differentiated into cardiomyocytes. Spontaneous APs were recorded from spontaneously beating single cardiomyocytes by using the perforated patch-clamp technique. Mathematical-Modeling Phase: We newly developed ICaL-mutation mathematical model, fitted into experimental data, including its impaired ion selectivity. Furthermore, we installed this mathematical model into hiPSC-CM simulation model. Collaboration Phase: Mutant in-silico model showed APD prolongation and frequent early afterdepolarization (EAD), which are same as in-vitro model. In-silico model revealed this EAD was mostly related to robust late-mode of sodium current occurred by Na+ overload and suggested that mexiletine is capable of reducing arrhythmia. Afterward, we applicated mexiletine onto hiPSC-CMs mutant model and found mexiletine suppress EADs. Conclusions Precise in-silico disease model can elucidate complicated ion currents and contribute predicting result of drug-testing. Funding Acknowledgement Type of funding source: Public Institution(s). Main funding source(s): Japan Society for the Promotion of Science, Grant-in-Aid for Young Scientists

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A corneal-PAMPA-based in silico model for predicting corneal permeability

Journal of Pharmaceutical and Biomedical Analysis ◽

10.1016/j.jpba.2021.114218 ◽

2021 ◽

pp. 114218

Author(s):

Anna Vincze ◽

Gergö Dargó ◽

Anita Rácz ◽

György T. Balogh

Keyword(s):

In Silico ◽

Corneal Permeability ◽

In Silico Model

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In silico molecular docking and in vitro antimicrobial efficacy of phytochemicals against multi-drug-resistant enteroaggregative Escherichia coli and non-typhoidal Salmonella spp.

Gut Pathogens ◽

10.1186/s13099-021-00443-3 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Padikkamannil Abishad ◽

Pollumahanti Niveditha ◽

Varsha Unni ◽

Jess Vergis ◽

Nitin Vasantrao Kurkure ◽

...

Keyword(s):

Escherichia Coli ◽

Molecular Docking ◽

In Silico ◽

Docking Studies ◽

Antimicrobial Efficacy ◽

Drug Resistant ◽

Protein Motifs ◽

Phytochemical Compounds ◽

In Silico Molecular Docking

Abstract Background In the wake of emergence of antimicrobial resistance, bioactive phytochemical compounds are proving to be important therapeutic agents. The present study envisaged in silico molecular docking as well as in vitro antimicrobial efficacy screening of identified phytochemical ligands to the dispersin (aap) and outer membrane osmoporin (OmpC) domains of enteroaggregative Escherichia coli (EAEC) and non-typhoidal Salmonella spp. (NTS), respectively. Materials and methods The evaluation of drug-likeness, molecular properties, and bioactivity of the identified phytocompounds (thymol, carvacrol, and cinnamaldehyde) was carried out using Swiss ADME, while Protox-II and StopTox servers were used to identify its toxicity. The in silico molecular docking of the phytochemical ligands with the protein motifs of dispersin (PDB ID: 2jvu) and outer membrane osmoporin (PDB ID: 3uu2) were carried out using AutoDock v.4.20. Further, the antimicrobial efficacy of these compounds against multi-drug resistant EAEC and NTS strains was determined by estimating the minimum inhibitory concentrations and minimum bactericidal concentrations. Subsequently, these phytochemicals were subjected to their safety (sheep and human erythrocytic haemolysis) as well as stability (cationic salts, and pH) assays. Results All the three identified phytochemicals ligands were found to be zero violators of Lipinski’s rule of five and exhibited drug-likeness. The compounds tested were categorized as toxicity class-4 by Protox-II and were found to be non- cardiotoxic by StopTox. The docking studies employing 3D model of dispersin and ompC motifs with the identified phytochemical ligands exhibited good binding affinity. The identified phytochemical compounds were observed to be comparatively stable at different conditions (cationic salts, and pH); however, a concentration-dependent increase in the haemolytic assay was observed against sheep as well as human erythrocytes. Conclusions In silico molecular docking studies provided useful insights to understand the interaction of phytochemical ligands with protein motifs of pathogen and should be used routinely before the wet screening of any phytochemicals for their antibacterial, stability, and safety aspects.

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