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

doi:10.1038/s41587-022-01230-4

Cited by 5 publications

(1 citation statement)

References 5 publications

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“…We developed a new drug-drug network, which was the rst component of DLCDPS, the DDN of system used the critically acclaimed machine learning algorithm XGBoost 36 to learn drug-drug interaction in humans and mice based on drug features, which were collected from DrugBank. Hyperparameters for model prediction, such as learning rate, were selected by using 5-fold cross-validation of the training data, and the results showed that cross-validation 37 performance in drug-combination datasets collected from DrugBank (Supplementary Fig. 1a).…”

Section: Drug-drug Networkmentioning

confidence: 99%

Nanonitrator: novel enhancer of inorganic nitrate’s protective effects, predicated on deep learning approach

Pan

et al. 2022

Preprint

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Inorganic nitrate is an indispensable nutrient, which has been used in experimental studies on the prevention and treatment of several diseases. However, its short half-life limits inorganic nitrate’s clinical application. To increase nitrate’s usability and overcome the challenges of traditional combination drug discovery through large-scale high-throughput biological experiments, we developed our deep learning–based combination drug prediction system, which identified vitamin C as the drug of choice to be combined with nitrate. Employing microencapsulation technology, we used vitamin C, sodium nitrate, and chitosan 3000 as the core material to prepare a nitrate nanoparticle, which we named Nanonitrator. The long-circulating delivery of nitrate by Nanonitrator significantly increased the efficacy and effect duration of nitrate in two animal models of injury, without compromising safety. Moreover, Nanonitrator at the same dose could better maintain intracellular homeostasis than nitrate (with or without vitamin C), emphasizing its potential for clinical use. More importantly, our work provides a method for the incorporation of inorganic compounds into sustained-release nanoparticles.

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Section: Drug-drug Networkmentioning

confidence: 99%

Nanonitrator: novel enhancer of inorganic nitrate’s protective effects, predicated on deep learning approach

Pan

et al. 2022

Preprint

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Discovery of Antimicrobial Lysins from the “Dark Matter” of Uncharacterized Phages Using Artificial Intelligence

Zhang,

Li,

Zou

et al. 2024

Advanced Science

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The rapid rise of antibiotic resistance and slow discovery of new antibiotics have threatened global health. While novel phage lysins have emerged as potential antibacterial agents, experimental screening methods for novel lysins pose significant challenges due to the enormous workload. Here, the first unified software package, namely DeepLysin, is developed to employ artificial intelligence for mining the vast genome reservoirs (“dark matter”) for novel antibacterial phage lysins. Putative lysins are computationally screened from uncharacterized Staphylococcus aureus phages and 17 novel lysins are randomly selected for experimental validation. Seven candidates exhibit excellent in vitro antibacterial activity, with LLysSA9 exceeding that of the best‐in‐class alternative. The efficacy of LLysSA9 is further demonstrated in mouse bloodstream and wound infection models. Therefore, this study demonstrates the potential of integrating computational and experimental approaches to expedite the discovery of new antibacterial proteins for combating increasing antimicrobial resistance.

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Antimicrobial properties of tomato juice and peptides against typhoidal Salmonella

Kwon,

Lee,

Lee

et al. 2024

Microbiol Spectr

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Tomatoes are readily available and affordable vegetables that offer a range of health benefits due to their bioactive molecules, such as antioxidants and antimicrobials. In contrast to the widely recognized antioxidant properties of tomatoes, their antimicrobial properties remain largely unexplored. Here, we present our findings on the antimicrobial properties of tomato juice and peptides, namely, tomato-derived antimicrobial peptides (tdAMPs), in relation to their effectiveness against typhoidal Salmonella . Our research has revealed that tomato juice demonstrates significant antimicrobial properties against Salmonella Typhi, a pathogen that specifically affects humans and is responsible for causing typhoid fever. By employing computational analysis of the tomato genome sequence, conducting molecular dynamics simulation, and performing functional analyses, we have successfully identified two tdAMPs, namely, tdAMP-1 and tdAMP-2. These tdAMPs have demonstrated potent antimicrobial properties by effectively disrupting bacterial membranes. The efficacy of tdAMP-2 is shown to be more effective than tdAMP-1. The efficacy of tdAMP-1 and tdAMP-2 has been demonstrated against drug-resistant S . Typhi, as well as hyper-capsular S . Typhi variants that possess hypervirulent characteristics, which are presently circulating in countries with endemicity. Tomato juice, along with the two tdAMPs, has demonstrated effectiveness against uropathogenic Escherichia coli as well. This underscores their potential as viable agents in combating certain Gram-negative pathogens. This study provides valuable insights into the development of effective and sustainable public health strategies that utilize tomato and its derivatives as lifestyle interventions. IMPORTANCE In this study, we investigate the antimicrobial properties of tomato juice, the most widely consumed affordable vegetables, as well as tomato-derived antimicrobial peptides, in relation to their effectiveness against foodborne pathogens with an emphasis on Salmonella Typhi, a deadly human-specific pathogen.

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

Cited by 5 publications

References 5 publications

Nanonitrator: novel enhancer of inorganic nitrate’s protective effects, predicated on deep learning approach

Nanonitrator: novel enhancer of inorganic nitrate’s protective effects, predicated on deep learning approach

Discovery of Antimicrobial Lysins from the “Dark Matter” of Uncharacterized Phages Using Artificial Intelligence

Antimicrobial properties of tomato juice and peptides against typhoidal Salmonella

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