In silico screening of ssDNA aptamer against Escherichia coli O157:H7: a machine learning and the Pseudo K-tuple nucleotide composition based approach

Abstract BackgroundGlycerol has become an interesting carbon source for industrial processes as consequence of the biodiesel business growth since it has shown promising results in terms of biomass/substrate yields. Selecting the appropriate metabolic targets to build efficient cell factories and maximize the desired chemical production in as little time as possible is a major challenge in industrial biotechnology. The engineering of microbial metabolism following rational design has been widely studied. However, it is a cost-, time-, and laborious-intensive process because of the cell network complexity; thus, to be proficient is needed known in advance the effects of gene deletions.ResultsAn in silico experiment was performed to model and understand the effects of metabolic engineering over the metabolism by transcriptomics data integration. In this study, systems-based metabolic engineering to predict the metabolic engineering targets was used in order to increase the bioconversion of glycerol to succinic acid by Escherichia coli. Transcriptomics analysis suggest insights of how increase the glycerol utilization of the cell for further design efficient cell factories. Three models were used; an E. coli core model, a model obtained after the integration of transcriptomics data obtained from E. coli growing in an optimized culture media, and a third one obtained after integration of transcriptomics data obtained from E. coli after adaptive laboratory evolution experiments. A total of 2402 strains were obtained from these three models. Fumarase and pyruvate dehydrogenase were frequently predicted in all the models, suggesting that these reactions are essential to increasing succinic acid production from glycerol. Finally, using flux balance analysis results for all the mutants predicted, a machine learning method was developed to predict new mutants as well as to propose optimal metabolic engineering targets and mutants based on the measurement of importance of each knockout’s (feature’s) contribution.ConclusionsThe combination of transcriptome, systems metabolic modeling, and machine learning analyses revealed versatile molecular mechanisms involved in the utilization of glycerol. These data provide a platform to improve the prediction of metabolic engineering targets to design efficient cell factories. Our results may also work a guide platform for the selection/engineering of microorganisms for production of interesting chemical compounds.

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Physicochemical and Structural Parameters Contributing to the Antibacterial Activity and Efflux Susceptibility of Small Molecule Inhibitors of Escherichia coli

Antimicrobial Agents and Chemotherapy ◽

10.1128/aac.01925-20 ◽

2021 ◽

Author(s):

Sara S. El Zahed ◽

Shawn French ◽

Maya A. Farha ◽

Garima Kumar ◽

Eric D. Brown

Keyword(s):

Machine Learning ◽

Escherichia Coli ◽

Antibacterial Activity ◽

Small Molecules ◽

Small Molecule ◽

In Silico ◽

Molecular Descriptors ◽

Structural Parameters ◽

Side Chain ◽

Gram Negative

Discovering new Gram-negative antibiotics has been a challenge for decades. This has been largely attributed to a limited understanding of the molecular descriptors governing Gram-negative permeation and efflux evasion. Herein, we address the contribution of efflux using a novel approach that applies multivariate analysis, machine learning, and structure-based clustering to some 4,500 actives from a small molecule screen in efflux-compromised Escherichia coli. We employed principal-component analysis and trained two decision tree-based machine learning models to investigate descriptors contributing to the antibacterial activity and efflux susceptibility of these actives. This approach revealed that the Gram-negative activity of hydrophobic and planar small molecules with low molecular stability is limited to efflux-compromised E. coli. Further, molecules with reduced branching and compactness showed increased susceptibility to efflux. Given these distinct properties that govern efflux, we developed the first machine learning model, called Susceptibility to Efflux Random Forest (SERF), as a tool to analyze the molecular descriptors of small molecules and predict those that could be susceptible to efflux pumps in silico. Here, SERF demonstrated high accuracy in identifying such molecules. Further, we clustered all 4,500 actives based on their core structures and identified distinct clusters highlighting side chain moieties that cause marked changes in efflux susceptibility. In all, our work reveals a role for physicochemical and structural parameters in governing efflux, presents a machine learning tool for rapid in silico analysis of efflux susceptibility, and provides a proof of principle for the potential of exploiting side chain modification to design novel antimicrobials evading efflux pumps.

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From In silico Protein Epitope Density Prediction to Testing Escherichia coli O157:H7 Vaccine Candidates in a Murine Model of Colonization

Frontiers in Cellular and Infection Microbiology ◽

10.3389/fcimb.2016.00094 ◽

2016 ◽

Vol 6 ◽

Cited By ~ 5

Author(s):

Daniel Tapia ◽

Brittany N. Ross ◽

Anjana Kalita ◽

Mridul Kalita ◽

Christopher L. Hatcher ◽

...

Keyword(s):

Escherichia Coli ◽

Murine Model ◽

In Silico ◽

Escherichia Coli O157 ◽

Vaccine Candidates ◽

Density Prediction ◽

Epitope Density

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Diseño, síntesis, caracterización y evaluación in vitro de la actividad de los péptidos antimicrobianos contra bacterias patógenas resistentes a antibióticos

Revista de la Academia Colombiana de Ciencias Exactas Físicas y Naturales ◽

10.18257/raccefyn.864 ◽

2019 ◽

Vol 43 (169) ◽

pp. 614-627

Author(s):

Claudia Ortiz López

Keyword(s):

Escherichia Coli ◽

Staphylococcus Aureus ◽

Pseudomonas Aeruginosa ◽

In Silico ◽

Escherichia Coli O157

Los péptidos antimicrobianos han atraído mucha atención como nuevos agentes terapéuticos contra enfermedades infecciosas. En este estudio se hizo el diseño racional in silico de 18 péptidos catiónicos con actividad antimicrobiana contra bacterias patógenas resistentes utilizando el programa DEPRAMP desarrollado en el Grupo de Investigación en Bioquímica y Microbiología de la Universidad Industrial de Santander. Posteriormente, los péptidos diseñados se sintetizaron en fase sólida con el método de 9-fluorenilmetoxicarbonilo en medio ácido. Se obtuvieron secuencias cortas de 17 aminoácidos con un grado de pureza entre 95 y 98 %, estructura secundaria de hélice alfa, carga neta catiónica (entre +3 y +6), punto isoeléctrico entre 10,04 y 12,03 e índice de hidropatía entre -0,62 y 1,14. Todos los péptidos antimicrobianos mostraron actividad antibacteriana y bactericida in vitro frente al menos una de las cepas patógenas estudiadas: Escherichia coli O157: H7, Pseudomonas aeruginosa y Staphylococcus aureus resistente a la meticilina. Los péptidos antimicrobianos GIBIM-P5S9K y GIBIM-P5F8W registraron la mejor actividad antibacteriana, alcanzando una concentración mínima inhibitoria (CMI 99) en rangos de 0,5 a 25 μM frente a las tres cepas evaluadas, de las cuales Escherichia coli O157: H7 fue la más sensible frente al péptido antimicrobiano GIBIMP5F8W, con una CMI 99 de 0,5 μM y una concentración mínima bactericida de 10 μM, en tanto que la cepa de Pseudomonas aeruginosa fue la más resistente, con una CMI de más de 100 μM frente a más de cinco péptidos antimicrobianos. La toxicidad de los péptidos sobre los eritrocitos produjo un porcentaje de hemólisis menor al 40 % en concentraciones de 50 μM. Por su parte, en las líneas celulares de carcinoma de pulmón A549 y HepG2, el único compuesto que presentó toxicidad fue GIBIM-P5F8W, presentando un 36% de células viables en concentraciones de 100 μM del péptido en la línea celular A549.

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