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Antimicrobial Peptides

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Designing antimicrobial peptides using deep learning and molecular dynamic simulations.

Briefings in bioinformatics
With the emergence of multidrug-resistant bacteria, antimicrobial peptides (AMPs) offer promising options for replacing traditional antibiotics to treat bacterial infections, but discovering and designing AMPs using traditional methods is a time-cons...

AMP-BERT: Prediction of antimicrobial peptide function based on a BERT model.

Protein science : a publication of the Protein Society
Antimicrobial resistance is a growing health concern. Antimicrobial peptides (AMPs) disrupt harmful microorganisms by nonspecific mechanisms, making it difficult for microbes to develop resistance. Accordingly, they are promising alternatives to trad...

AMPpred-EL: An effective antimicrobial peptide prediction model based on ensemble learning.

Computers in biology and medicine
Antimicrobial peptides (AMPs) are important for the human immune system and are currently applied in clinical trials. AMPs have been received much attention for accurate recognition. Recently, several computational methods for identifying AMPs have b...

Identification of antimicrobial peptides from the human gut microbiome using deep learning.

Nature biotechnology
The human gut microbiome encodes a large variety of antimicrobial peptides (AMPs), but the short lengths of AMPs pose a challenge for computational prediction. Here we combined multiple natural language processing neural network models, including LST...

Do deep learning models make a difference in the identification of antimicrobial peptides?

Briefings in bioinformatics
In the last few decades, antimicrobial peptides (AMPs) have been explored as an alternative to classical antibiotics, which in turn motivated the development of machine learning models to predict antimicrobial activities in peptides. The first genera...

Accelerating the discovery of antifungal peptides using deep temporal convolutional networks.

Briefings in bioinformatics
The application of machine intelligence in biological sciences has led to the development of several automated tools, thus enabling rapid drug discovery. Adding to this development is the ongoing COVID-19 pandemic, due to which researchers working in...

AMPlify: attentive deep learning model for discovery of novel antimicrobial peptides effective against WHO priority pathogens.

BMC genomics
BACKGROUND: Antibiotic resistance is a growing global health concern prompting researchers to seek alternatives to conventional antibiotics. Antimicrobial peptides (AMPs) are attracting attention again as therapeutic agents with promising utility in ...

sAMP-PFPDeep: Improving accuracy of short antimicrobial peptides prediction using three different sequence encodings and deep neural networks.

Briefings in bioinformatics
Short antimicrobial peptides (sAMPs) belong to a significant repertoire of antimicrobial agents and are known to possess enhanced antimicrobial activity, higher stability and less toxicity to human cells, as well as less complex than other large biol...

Machine Learning Prediction of Antimicrobial Peptides.

Methods in molecular biology (Clifton, N.J.)
Antibiotic resistance constitutes a global threat and could lead to a future pandemic. One strategy is to develop a new generation of antimicrobials. Naturally occurring antimicrobial peptides (AMPs) are recognized templates and some are already in c...