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Escherichia coli Proteins

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Emergence of mcr-1 mediated colistin resistant Escherichia coli from a hospitalized patient in Bangladesh.

Journal of infection in developing countries
INTRODUCTION: The emergence of plasmid mediated mcr in bacteria has become global public health threat. Herein, we report a mcr-1 positive E. coli in normal human flora from a patient admitted in Dhaka Medical College Hospital (DMCH).

In vitro activity of flomoxef against extended-spectrum β-lactamase-producing Escherichia coli and Klebsiella pneumoniae in Korea.

Diagnostic microbiology and infectious disease
To find an alternative regimen for the treatment of extended-spectrum β-lactamase (EBSL)-producing Enterobacteriaceae infections, we examined the in vitro activity of flomoxef against Escherichia coli and Klebsiella pneumoniae having CTX-M-1 group an...

Sequence-Based Prediction of Cysteine Reactivity Using Machine Learning.

Biochemistry
As one of the most intrinsically reactive amino acids, cysteine carries a variety of important biochemical functions, including catalysis and redox regulation. Discovery and characterization of cysteines with heightened reactivity will help annotate ...

Boosted neural networks scoring functions for accurate ligand docking and ranking.

Journal of bioinformatics and computational biology
Predicting the native poses of ligands correctly is one of the most important steps towards successful structure-based drug design. Binding affinities (BAs) estimated by traditional scoring functions (SFs) are typically used to score and rank-order p...

Computational Prediction of Sigma-54 Promoters in Bacterial Genomes by Integrating Motif Finding and Machine Learning Strategies.

IEEE/ACM transactions on computational biology and bioinformatics
Sigma factor, as a unit of RNA polymerase holoenzyme, is a critical factor in the process of gene transcriptional regulation. It recognizes the specific DNA sites and brings the core enzyme of RNA polymerase to the upstream regions of target genes. T...

Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models.

Nature communications
Knowing the catalytic turnover numbers of enzymes is essential for understanding the growth rate, proteome composition, and physiology of organisms, but experimental data on enzyme turnover numbers is sparse and noisy. Here, we demonstrate that machi...

Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning.

Scientific reports
It is important that antibiotics prescriptions are based on antimicrobial susceptibility data to ensure effective treatment outcomes. The increasing availability of next-generation sequencing, bacterial whole genome sequencing (WGS) can facilitate a ...

Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry.

Nature communications
Crosslinking mass spectrometry has developed into a robust technique that is increasingly used to investigate the interactomes of organelles and cells. However, the incomplete and noisy information in the mass spectra of crosslinked peptides limits t...

Pathogenic potential assessment of the Shiga toxin-producing by a source attribution-considered machine learning model.

Proceedings of the National Academy of Sciences of the United States of America
Instead of conventional serotyping and virulence gene combination methods, methods have been developed to evaluate the pathogenic potential of newly emerging pathogens. Among them, the machine learning (ML)-based method using whole-genome sequencing ...