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Antimicrobial Peptides

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Lead Informed Artificial Intelligence Mining of Antitubercular Host Defense Peptides.

Biomacromolecules
Identifying host defense peptides (HDPs) that are effective against drug-resistant infections is challenging due to their vast sequence space. Artificial intelligence (AI)-guided design can accelerate HDP discovery, but it traditionally requires larg...

Unlocking Antimicrobial Peptides: In Silico Proteolysis and Artificial Intelligence-Driven Discovery from Cnidarian Omics.

Molecules (Basel, Switzerland)
Overcoming the growing challenge of antimicrobial resistance (AMR), which affects millions of people worldwide, has driven attention for the exploration of marine-derived antimicrobial peptides (AMPs) for innovative solutions. Cnidarians, such as cor...

ML-AMPs designed through machine learning show antifungal activity against C. albicans and therapeutic potential on mice model with candidiasis.

Life sciences
AIMS: C. albicans resistant strains have led to increasingly severe treatment challenges. Antimicrobial peptides with low resistance-inducing propensity for pathogens have been developed. A series of antimicrobial peptides de novo designed through ma...

Deep Learning for Antimicrobial Peptides: Computational Models and Databases.

Journal of chemical information and modeling
Antimicrobial peptides are a promising strategy to combat antimicrobial resistance. However, the experimental discovery of antimicrobial peptides is both time-consuming and laborious. In recent years, the development of computational technologies (es...

BERT-AmPEP60: A BERT-Based Transfer Learning Approach to Predict the Minimum Inhibitory Concentrations of Antimicrobial Peptides for and .

Journal of chemical information and modeling
Antimicrobial peptides (AMPs) are a promising alternative for combating bacterial drug resistance. While current computer prediction models excel at binary classification of AMPs based on sequences, there is a lack of regression methods to accurately...

Exploring the repository of de novo-designed bifunctional antimicrobial peptides through deep learning.

eLife
Antimicrobial peptides (AMPs) are attractive candidates to combat antibiotic resistance for their capability to target biomembranes and restrict a wide range of pathogens. It is a daunting challenge to discover novel AMPs due to their sparse distribu...

Fluorescent sensor array for rapid bacterial identification using antimicrobial peptide-functionalized gold nanoclusters and machine learning.

Talanta
Bacterial infectious diseases pose significant challenges to public health, emphasizing the need for rapid and accurate diagnostic tools. Here, we introduced a multichannel fluorescent sensor array based on antimicrobial peptide-functionalized gold n...

Deep-Learning-Based Approaches for Rational Design of Stapled Peptides With High Antimicrobial Activity and Stability.

Microbial biotechnology
Antimicrobial peptides (AMPs) face stability and toxicity challenges in clinical use. Stapled modification enhances their stability and effectiveness, but its application in peptide design is rarely reported. This study built ten prediction models fo...

IAMPDB: A Knowledgebase of Manually Curated Insects-Derived Antimicrobial Peptides.

Journal of peptide science : an official publication of the European Peptide Society
Insects, a majority of animal species, rely on innate immunity and antimicrobial peptides (AMPs), which are a part of their innate immunity, to combat diverse parasites and pathogens. These peptides have applications ranging from agriculture to antim...

Discovery of naturally inspired antimicrobial peptides using deep learning.

Bioorganic chemistry
Non-ribosomal peptides (NRPs) are promising lead compounds for novel antibiotics. Bioinformatic mining of silent microbial NRPS gene clusters provide crucial insights for the discovery and de novo design of bioactive peptides. Here, we describe the e...