AIMC Topic: Pseudomonas aeruginosa

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QSAR Models for Active Substances against Using Disk-Diffusion Test Data.

Molecules (Basel, Switzerland) Mar 19, 2021
is a Gram-negative bacillus included among the six "ESKAPE" microbial species with an outstanding ability to "escape" currently used antibiotics and developing new antibiotics against it is of the highest priority. Whereas minimum inhibitory concent...
Anti-Bacterial Agents Support Vector Machine Models, Biological Pseudomonas aeruginosa Disk Diffusion Antimicrobial Tests Quantitative Structure-Activity Relationship
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Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations.

Nature biomedical engineering Mar 11, 2021
The de novo design of antimicrobial therapeutics involves the exploration of a vast chemical repertoire to find compounds with broad-spectrum potency and low toxicity. Here, we report an efficient computational method for the generation of antimicrob...
Molecular Dynamics Simulation Amino Acid Sequence Pseudomonas aeruginosa Acinetobacter baumannii Staphylococcus aureus Mice, Inbred BALB C Antimicrobial Cationic Peptides Structure-Activity Relationship Anti-Bacterial Agents Drug Resistance, Bacterial Female Animals Mice Klebsiella pneumoniae Klebsiella Infections Escherichia coli Drug Design Drug Discovery Deep Learning Microbial Sensitivity Tests
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Antibiofilm Activity of α-Amylase from Bacillus subtilis and Prediction of the Optimized Conditions for Biofilm Removal by Response Surface Methodology (RSM) and Artificial Neural Network (ANN).

Applied biochemistry and biotechnology Mar 1, 2021
α-amylase is known to have antibiofilm activity against biofilms of both Gram positive and Gram-negative bacterial strains. Partially purified α-amylase from Bacillus subtilis was found to have inhibit biofilm formed by P. aeruginosa and S. aureus. T...
Bacterial Proteins Hydrogen-Ion Concentration Pseudomonas aeruginosa alpha-Amylases Neural Networks, Computer Biofilms Staphylococcus aureus Bacillus subtilis
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PeptiDesCalculator: Software for computation of peptide descriptors. Definition, implementation and case studies for 9 bioactivity endpoints.

Proteins Sep 17, 2020
We present a novel Java-based program denominated PeptiDesCalculator for computing peptide descriptors. These descriptors include: redefinitions of known protein parameters to suite the peptide domain, generalization schemes for the global descriptio...
HIV Infections Antiviral Agents Support Vector Machine Candida albicans Anti-Bacterial Agents Neoplasms Peptides Antineoplastic Agents Protein Stability Peptide Mapping Antifungal Agents Listeria monocytogenes Pseudomonas aeruginosa Hepatitis C Humans Neural Networks, Computer Software
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A Genome-Based Model to Predict the Virulence of Pseudomonas aeruginosa Isolates.

mBio Aug 25, 2020
Variation in the genome of , an important pathogen, can have dramatic impacts on the bacterium's ability to cause disease. We therefore asked whether it was possible to predict the virulence of isolates based on their genomic content. We applied a m...
Genome, Bacterial Pseudomonas aeruginosa Proof of Concept Study Pseudomonas Infections Phenotype Female Animals Mice Genomics Bacteremia Mice, Inbred BALB C Virulence Machine Learning Algorithms
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Prediction and analysis of prokaryotic promoters based on sequence features.

Bio Systems Aug 2, 2020
Promoter recognition is an important part of functional genomic annotation but a difficult problem. Many studies have been carried out to address this issue. However, they still cannot meet application needs. Most of the methods exhibit specificity, ...
Pseudomonas aeruginosa Base Sequence Promoter Regions, Genetic Entropy Base Composition Neural Networks, Computer Escherichia coli Bacillus subtilis
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Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics.

EMBO molecular medicine Feb 12, 2020
Limited therapy options due to antibiotic resistance underscore the need for optimization of current diagnostics. In some bacterial species, antimicrobial resistance can be unambiguously predicted based on their genome sequence. In this study, we seq...
Machine Learning Anti-Bacterial Agents Genome, Bacterial Pathology, Molecular Drug Resistance, Bacterial Transcriptome Microbial Sensitivity Tests Pseudomonas aeruginosa
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Synthesis and in vitro activity of asymmetric indole-based bisamidine compounds against Gram-positive and Gram-negative pathogens.

Bioorganic & medicinal chemistry letters Dec 13, 2019
A series of new asymmetric bisamidine was designed, synthesized, and tested for their in-vitro antibacterial activity using a range of Gram-positive and Gram-negative pathogens. Most compounds demonstrated powerful antibacterial activity, and interes...
Indoles Dose-Response Relationship, Drug Anti-Bacterial Agents Citrobacter freundii Structure-Activity Relationship Microbial Sensitivity Tests Furans Molecular Structure Escherichia coli Klebsiella Pseudomonas aeruginosa
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Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning.

Nature communications Oct 30, 2019
Raman optical spectroscopy promises label-free bacterial detection, identification, and antibiotic susceptibility testing in a single step. However, achieving clinically relevant speeds and accuracies remains challenging due to weak Raman signal from...
Salmonella enterica Klebsiella Enterococcus Streptococcus Bacterial Typing Techniques Proteus mirabilis Humans Candida Neural Networks, Computer Bacterial Infections Principal Component Analysis Pseudomonas aeruginosa Escherichia coli Bacteria Staphylococcus aureus Logistic Models Methicillin-Resistant Staphylococcus aureus Spectrum Analysis, Raman Support Vector Machine Anti-Bacterial Agents Deep Learning Microbial Sensitivity Tests Single-Cell Analysis
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Identifying genetic determinants of complex phenotypes from whole genome sequence data.

BMC genomics Jun 10, 2019
BACKGROUND: A critical goal in biology is to relate the phenotype to the genotype, that is, to find the genetic determinants of various traits. However, while simple monofactorial determinants are relatively easy to identify, the underpinnings of com...
Influenza, Human Pseudomonas aeruginosa Humans Phenotype Machine Learning Influenza A virus Algorithms Proteomics Drug Resistance, Bacterial Whole Genome Sequencing
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