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Pore Forming Cytotoxic Proteins

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Deep Learning for Novel Antimicrobial Peptide Design.

Biomolecules
Antimicrobial resistance is an increasing issue in healthcare as the overuse of antibacterial agents rises during the COVID-19 pandemic. The need for new antibiotics is high, while the arsenal of available agents is decreasing, especially for the tre...

AMPGAN v2: Machine Learning-Guided Design of Antimicrobial Peptides.

Journal of chemical information and modeling
Antibiotic resistance is a critical public health problem. Each year ∼2.8 million resistant infections lead to more than 35 000 deaths in the U.S. alone. Antimicrobial peptides (AMPs) show promise in treating resistant infections. However, applicatio...

Combining genetic algorithm with machine learning strategies for designing potent antimicrobial peptides.

BMC bioinformatics
BACKGROUND: Current methods in machine learning provide approaches for solving challenging, multiple constraint design problems. While deep learning and related neural networking methods have state-of-the-art performance, their vulnerability in decis...

Alignment-Free Antimicrobial Peptide Predictors: Improving Performance by a Thorough Analysis of the Largest Available Data Set.

Journal of chemical information and modeling
In the last two decades, a large number of machine-learning-based predictors for the activities of antimicrobial peptides (AMPs) have been proposed. These predictors differ from one another in the learning method and in the training and testing data ...

Co-AMPpred for in silico-aided predictions of antimicrobial peptides by integrating composition-based features.

BMC bioinformatics
BACKGROUND: Antimicrobial peptides (AMPs) are oligopeptides that act as crucial components of innate immunity, naturally occur in all multicellular organisms, and are involved in the first line of defense function. Recent studies showed that AMPs per...

Machine learning-enabled predictive modeling to precisely identify the antimicrobial peptides.

Medical & biological engineering & computing
The ubiquitous antimicrobial peptides (AMPs), with a broad range of antimicrobial activities, represent a great promise for combating the multi-drug resistant infections. In this study, using a large and diverse set of AMPs (2638) and non-AMPs (3700)...

Prediction of antimicrobial peptides toxicity based on their physico-chemical properties using machine learning techniques.

BMC bioinformatics
BACKGROUND: Antimicrobial peptides are promising tools to fight against ever-growing antibiotic resistance. However, despite many advantages, their toxicity to mammalian cells is a critical obstacle in clinical application and needs to be addressed.

A deep learning model to detect novel pore-forming proteins.

Scientific reports
Many pore-forming proteins originating from pathogenic bacteria are toxic against agricultural pests. They are the key ingredients in several pesticidal products for agricultural use, including transgenic crops. There is an urgent need to identify no...

Construction of an aerolysin-based multi-epitope vaccine against an machine learning and artificial intelligence-supported approach.

Frontiers in immunology
, a gram-negative coccobacillus bacterium, can cause various infections in humans, including septic arthritis, diarrhea (traveler's diarrhea), gastroenteritis, skin and wound infections, meningitis, fulminating septicemia, enterocolitis, peritonitis,...

An active machine learning discovery platform for membrane-disrupting and pore-forming peptides.

Physical chemistry chemical physics : PCCP
Membrane-disrupting and pore-forming peptides (PFPs) play a substantial role in bionanotechnology and can determine the life and death of cells. The control of chemical and ion transport through cell membranes is essential to maintaining concentratio...