AIMC Topic: Escherichia coli

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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...

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 ...

Performance and hypothetical clinical impact of an mNGS-based machine learning model for antimicrobial susceptibility prediction of five ESKAPEE bacteria.

Microbiology spectrum
UNLABELLED: Antimicrobial resistance is an escalating global health crisis, underscoring the urgent need for timely and targeted therapies to ensure effective clinical treatment. We developed a machine learning model based on metagenomic next-generat...

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...

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...

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.

Combining diffusion and transformer models for enhanced promoter synthesis and strength prediction in deep learning.

mSystems
UNLABELLED: In the field of synthetic biology, the engineering of synthetic promoters that outperform their natural counterparts is of paramount importance, which can optimize the expression of exogenous genes, enhance the efficiency of metabolic pat...

Aggregation induced emission luminogen bacteria hybrid bionic robot for multimodal phototheranostics and immunotherapy.

Nature communications
Multimodal phototheranostics utilizing single molecules offer a "one-and-done" approach, presenting a convenient and effective strategy for cancer therapy. However, therapies based on conventional photosensitizers often suffer from limitations such a...