AIMC Topic: Escherichia coli Proteins

Clear Filters Showing 1 to 10 of 28 articles

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

Nature communications May 7, 2025
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...
Neural Networks, Computer Protein Binding Escherichia coli Proteins Binding Sites Chromatin Immunoprecipitation Sequencing DNA, Bacterial Transcription Factors Escherichia coli
View on PubMed DOI

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

Journal of chemical theory and computation Apr 10, 2025
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...
Nuclear Magnetic Resonance, Biomolecular Neural Networks, Computer Molecular Dynamics Simulation Machine Learning Escherichia coli Proteins Escherichia coli Proteins
View on PubMed DOI

PeptideForest: Semisupervised Machine Learning Integrating Multiple Search Engines for Peptide Identification.

Journal of proteome research Jan 22, 2025
The first step in bottom-up proteomics is the assignment of measured fragmentation mass spectra to peptide sequences, also known as peptide spectrum matches. In recent years novel algorithms have pushed the assignment to new heights; unfortunately, d...
Humans Peptides Proteomics Proteome Machine Learning Software Algorithms Search Engine Tandem Mass Spectrometry Escherichia coli HEK293 Cells Supervised Machine Learning Escherichia coli Proteins
View on PubMed DOI

Prediction of Solubility of Proteins in Escherichia coli Based on Functional and Structural Features Using Machine Learning Methods.

The protein journal Sep 7, 2024
Protein solubility is a critical parameter that determines the stability, activity, and functionality of proteins, with broad and far-reaching implications in biotechnology and biochemistry. Accurate prediction and control of protein solubility are e...
Solubility Escherichia coli Proteins Support Vector Machine Escherichia coli Machine Learning Algorithms
View on PubMed DOI

Deep Learning Powers Protein Identification from Precursor MS Information.

Journal of proteome research Aug 21, 2024
Proteome analysis currently heavily relies on tandem mass spectrometry (MS/MS), which does not fully utilize MS1 features, as many precursors remain unselected for MS/MS fragmentation, especially in the cases of low abundance samples and wide abundan...
Escherichia coli Tandem Mass Spectrometry Escherichia coli Proteins Logistic Models Proteomics Proteome Humans Deep Learning
View on PubMed DOI

Deep-learning-assisted Sort-Seq enables high-throughput profiling of gene expression characteristics with high precision.

Science advances Nov 8, 2023
Owing to the nondeterministic and nonlinear nature of gene expression, the steady-state intracellular protein abundance of a clonal population forms a distribution. The characteristics of this distribution, including expression strength and noise, ar...
Gene Expression Profiling Deep Learning Escherichia coli Gene Expression High-Throughput Nucleotide Sequencing Escherichia coli Proteins
View on PubMed DOI

High throughput optimization of medium composition for Escherichia coli protein expression using deep learning and Bayesian optimization.

Journal of bioscience and bioengineering Dec 29, 2022
To improve synthetic media for protein expression in Escherichia coli, a strategy using deep neural networks (DNN) and Bayesian optimization was performed in this study. To obtain training data for a deep learning algorithm, E. coli harvesting a plas...
Bayes Theorem Escherichia coli Proteins Green Fluorescent Proteins Deep Learning Escherichia coli
View on PubMed DOI

Deep learning-driven insights into super protein complexes for outer membrane protein biogenesis in bacteria.

eLife Dec 28, 2022
To reach their final destinations, outer membrane proteins (OMPs) of gram-negative bacteria undertake an eventful journey beginning in the cytosol. Multiple molecular machines, chaperones, proteases, and other enzymes facilitate the translocation and...
Deep Learning Membrane Proteins Bacterial Outer Membrane Proteins Peptide Hydrolases Molecular Chaperones Escherichia coli Escherichia coli Proteins
View on PubMed DOI

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

Nature communications May 28, 2021
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...
Cross-Linking Reagents Neural Networks, Computer Protein Interaction Mapping Peptides Tandem Mass Spectrometry Multiprotein Complexes Proteomics Time Factors Escherichia coli Proteins Escherichia coli Chromatography, Reverse-Phase Chromatography, High Pressure Liquid
View on PubMed DOI

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

Scientific reports Sep 14, 2020
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 ...
DNA Gyrase Intracellular Signaling Peptides and Proteins Ciprofloxacin Inhibitory Concentration 50 Drug Resistance, Bacterial Genes, Bacterial Machine Learning Escherichia coli Proteins Escherichia coli Anti-Bacterial Agents Toxicity Tests Mutation
View on PubMed DOI
Popular Topics
Recent Journals