scholarly journals Model-guided in silico Overexpression of adhE Gene Predicts Increased Ethanol Production in Escherichia coli from Xylose

2015 ◽  
Vol 3 (3) ◽  
pp. 39-43
Author(s):  
Bashir Sajo Mienda ◽  
Mohd Shahir Shamsir
Keyword(s):  
Escherichia Coli ◽  
Ethanol Production ◽  
In Silico

scholarly journals 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 ◽  
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.

Putative binding mode of Escherichia coli exopolyphosphatase and polyphosphates based on a hybrid in silico/biochemical approach

2016 ◽  
Vol 606 ◽  
pp. 64-72 ◽  
Author(s):  
Cristhian Boetsch ◽  
Daniel R. Aguayo-Villegas ◽  
Fernando D. Gonzalez-Nilo ◽  
Á. Teresita Lisa ◽  
Paola R. Beassoni
Keyword(s):  
Escherichia Coli ◽  
In Silico ◽  
Binding Mode ◽  
Biochemical Approach

scholarly journals Metabolic Engineering of Escherichia coli for Enhanced Production of Succinic Acid, Based on Genome Comparison and In Silico Gene Knockout Simulation

2005 ◽  
Vol 71 (12) ◽  
pp. 7880-7887 ◽  
Author(s):  
Sang Jun Lee ◽  
Dong-Yup Lee ◽  
Tae Yong Kim ◽  
Byung Hun Kim ◽  
Jinwon Lee ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Succinic Acid ◽  
In Silico ◽  
Acid Production ◽  
Gene Knockout ◽  
Fermentation Products ◽  
E Coli ◽  
Metabolic Analysis ◽  
Enhanced Production ◽  
Succinic Acid Production

ABSTRACT Comparative analysis of the genomes of mixed-acid-fermenting Escherichia coli and succinic acid-overproducing Mannheimia succiniciproducens was carried out to identify candidate genes to be manipulated for overproducing succinic acid in E. coli. This resulted in the identification of five genes or operons, including ptsG, pykF, sdhA, mqo, and aceBA, which may drive metabolic fluxes away from succinic acid formation in the central metabolic pathway of E. coli. However, combinatorial disruption of these rationally selected genes did not allow enhanced succinic acid production in E. coli. Therefore, in silico metabolic analysis based on linear programming was carried out to evaluate the correlation between the maximum biomass and succinic acid production for various combinatorial knockout strains. This in silico analysis predicted that disrupting the genes for three pyruvate forming enzymes, ptsG, pykF, and pykA, allows enhanced succinic acid production. Indeed, this triple mutation increased the succinic acid production by more than sevenfold and the ratio of succinic acid to fermentation products by ninefold. It could be concluded that reducing the metabolic flux to pyruvate is crucial to achieve efficient succinic acid production in E. coli. These results suggest that the comparative genome analysis combined with in silico metabolic analysis can be an efficient way of developing strategies for strain improvement.

scholarly journals In silico profiling of Escherichia coli and Saccharomyces cerevisiae as terpenoid factories

2013 ◽  
Vol 12 (1) ◽  
pp. 84 ◽  
Author(s):  
Evamaria Gruchattka ◽  
Oliver Hädicke ◽  
Steffen Klamt ◽  
Verena Schütz ◽  
Oliver Kayser
Keyword(s):  
Escherichia Coli ◽  
Saccharomyces Cerevisiae ◽  
In Silico

Specific Ethanol Production Rate in Ethanologenic Escherichia coli Strain KO11 Is Limited by Pyruvate Decarboxylase

2008 ◽  
Vol 15 (1) ◽  
pp. 55-64 ◽  
Author(s):  
Gerardo Huerta-Beristain ◽  
José Utrilla ◽  
Georgina Hernández-Chávez ◽  
Francisco Bolívar ◽  
Guillermo Gosset ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Ethanol Production ◽  
Production Rate ◽  
Pyruvate Decarboxylase ◽  
Ethanol Production Rate
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