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Escherichia coli

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Improving prediction of bacterial sRNA regulatory targets with expression data.

NAR genomics and bioinformatics
Small regulatory RNAs (sRNAs) are widespread in bacteria. However, characterizing the targets of sRNA regulation in a way that scales with the increasing number of identified sRNAs has proven challenging. Computational methods offer one means for eff...

Comparative study on antibacterial activities and removal of iron ions from water using novel modified sand with silver through the hydrothermal technique.

Scientific reports
The hydrothermal-calcination technique was used to modify raw sand with silver (Ag) at different weight percentages: 2%, 5%, and 10% using silver nitrate. The raw and sand-coated Ag nanoparticle samples were analyzed using various techniques, includi...

Aptamer-functionalized graphene quantum dots combined with artificial intelligence detect bacteria for urinary tract infections.

Frontiers in cellular and infection microbiology
OBJECTIVES: Urinary tract infection is one of the most prevalent forms of bacterial infection, and prompt and efficient identification of pathogenic bacteria plays a pivotal role in the management of urinary tract infections. In this study, we propos...

Seeking Correlation Among Porin Permeabilities and Minimum Inhibitory Concentrations Through Machine Learning: A Promising Route to the Essential Molecular Descriptors.

Molecules (Basel, Switzerland)
Developing effective antibiotics against Gram-negative bacteria remains challenging due to their protective outer membrane. With this study, we investigated the relationship between antibiotic permeation through the OmpF porin of and antimicrobial e...

GANDALF: Generative ANsatz for DNA damage evALuation and Forecast. A neural network-based regression for estimating early DNA damage across micro-nano scales.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: This study aims to develop a comprehensive simulation framework to connect radiation effects from the microscopic to the nanoscopic scale.

Synergistic detection of E. coli using ultrathin film of functionalized graphene with impedance spectroscopy and machine learning.

Scientific reports
Bacterial detection and classification are critical challenges in healthcare, environmental monitoring, and food safety, demanding selective and efficient methods. This study presents a novel, label-free approach for E. coli detection using ultrathin...

Unveiling Berberine analogues as potential inhibitors of Escherichia coli FtsZ through machine learning molecular docking and molecular dynamics approach.

Scientific reports
The bacterial cell division protein FtsZ, a crucial GTPase, plays a vital role in the formation of the contractile Z-ring, which is essential for bacterial cytokinesis. Consequently, inhibiting FtsZ could prevent the formation of proto-filaments and ...

Estimation and validation of solubility of recombinant protein in E. coli strains via various advanced machine learning models.

Scientific reports
This study presents a comprehensive approach to predicting solubility of recombinant protein in four E. coli samples by employing machine learning techniques and optimization algorithms. Various models, including AdaBoost, Decision Tree Regression (D...

LEGOLAS: A Machine Learning Method for Rapid and Accurate Predictions of Protein NMR Chemical Shifts.

Journal of chemical theory and computation
This work introduces LEGOLAS, a fully open source TorchANI-based neural network model designed to predict NMR chemical shifts for protein backbone atoms (N, Cα, Cβ, C', HN, Hα). LEGOLAS has been designed to be fast without loss of accuracy, as our mo...

Predictive biophysical neural network modeling of a compendium of in vivo transcription factor DNA binding profiles for Escherichia coli.

Nature communications
The DNA binding of most Escherichia coli Transcription Factors (TFs) has not been comprehensively mapped, and few have models that can quantitatively predict binding affinity. We report the global mapping of in vivo DNA binding for 139 E. coli TFs us...