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A peptide bond covalently attaches amino acids through a dehydration reaction. One amino acid's carboxyl group and another amino acid's amino group combine, releasing a water molecule. The resulting bond is the peptide bond. The products that such linkages form are peptides. As more amino acids join this growing chain, the resulting chain is a polypeptide. Each polypeptide has a free amino group at one end. This end has the N-terminal, or the amino-terminal, and the other end has a free...
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The effectiveness of antimicrobial agents depends on various factors influencing their ability to eliminate microbial populations. Larger microbial populations require more time for complete eradication, emphasizing the importance of population size analysis when evaluating antimicrobial efficacy.Microbial resistance to antimicrobial agents varies significantly. Highly resilient microorganisms include endospores, gram-negative bacteria, and non-enveloped viruses, while prions are exceptionally...
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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Novel Object Recognition Test for the Investigation of Learning and Memory in Mice
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Deep learning improves antimicrobial peptide recognition.

Daniel Veltri1,2, Uday Kamath3, Amarda Shehu4,5,6

  • 1Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, U.S. National Institutes of Health, Rockville, MD, USA.

Bioinformatics (Oxford, England)
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Summary
This summary is machine-generated.

Deep learning models can effectively identify antimicrobial peptides (AMPs) for new drug development. This approach surpasses current methods and enables reduced sequence representations for efficient screening.

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Area of Science:

  • Biotechnology
  • Computational Biology
  • Drug Discovery

Background:

  • Antibiotic resistance is a significant global health challenge.
  • Antimicrobial peptides (AMPs) are key components of innate immunity and promising drug candidates.
  • Machine learning accelerates the identification of potential AMPs.

Purpose of the Study:

  • To develop a deep learning model for recognizing antimicrobial activity in peptides.
  • To evaluate the model's performance against existing classification methods.
  • To explore reduced sequence representations for AMP identification.

Main Methods:

  • A neural network model incorporating convolutional and recurrent layers was designed.
  • The model leverages primary amino acid sequence composition for analysis.
  • Performance was assessed on a comprehensive antimicrobial peptide dataset.

Main Results:

  • The proposed deep learning model demonstrated superior performance compared to state-of-the-art classification models.
  • A reduced-alphabet representation using nine amino acid types maintained reasonable AMP recognition.
  • Model and datasets are available via the Antimicrobial Peptide Scanner web server.

Conclusions:

  • Deep learning offers a powerful approach for identifying novel antimicrobial peptides.
  • The developed model provides an efficient tool for drug discovery in the face of antibiotic resistance.
  • Reduced sequence representations can facilitate faster and more effective AMP screening.