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Related Concept Videos

Encoding01:19

Encoding

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Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
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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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Antimicrobial Effectiveness01:28

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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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Antimicrobial Proteins01:23

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Antimicrobial proteins are important components of the immune system. They aid the body in combating pathogens by either killing them directly or hindering their replication processes. Four main types of antimicrobial substances are interferons, the complement system, iron-binding proteins, and antimicrobial proteins.
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Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
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Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
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Encodings and models for antimicrobial peptide classification for multi-resistant pathogens.

Sebastian Spänig1, Dominik Heider1

  • 1Department of Bioinformatics, Faculty of Mathematics and Computer Science, Philipps-University of Marburg, Marburg, Germany.

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Antimicrobial peptides (AMPs) are crucial for immunity and fighting resistant pathogens. This review explores advanced machine learning encodings for automated AMP discovery, overcoming limitations of traditional methods.

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Area of Science:

  • Biochemistry and Molecular Biology
  • Immunology
  • Computational Biology

Background:

  • Antimicrobial peptides (AMPs) are vital components of the innate immune system across diverse organisms.
  • AMPs exhibit potent activity against multi-drug resistant pathogens, offering a critical solution to the growing threat of antibiotic resistance.
  • AMPs also possess antitumor and antiviral properties, driving significant interest in their discovery and pharmaceutical development.

Purpose of the Study:

  • To review state-of-the-art amino acid encodings for predicting antimicrobial peptide (AMP) activity.
  • To highlight the importance of effective encodings for machine learning-based automated AMP discovery.
  • To introduce advanced machine learning models, particularly those utilizing support vector machines and deep learning, for enhanced AMP prediction.

Main Methods:

  • Review of current literature on amino acid encodings for peptide and protein representation.
  • Analysis of sequence and structure-based aggregation properties of various encodings.
  • Examination of machine learning classifiers, including support vector machines and deep learning models, tailored for AMP prediction.

Main Results:

  • Identified state-of-the-art amino acid encodings and their properties.
  • Emphasized the critical role of informative encodings in machine learning model performance for AMP discovery.
  • Showcased the trend towards specialized machine learning models, especially those leveraging support vector machine and deep learning-derived encodings.

Conclusions:

  • Novel amino acid encodings are essential for advancing automated antimicrobial peptide discovery.
  • Machine learning, particularly deep learning and SVM-based approaches, offers a powerful avenue for exploring sequence variations and identifying novel AMPs.
  • While focused on AMPs, many discussed encodings have broader applications in general protein and peptide representation.