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A linguistic model for the rational design of antimicrobial peptides

Author

Listed:
  • Christopher Loose

    (Massachusetts Institute of Technology)

  • Kyle Jensen

    (Massachusetts Institute of Technology
    Harvard–MIT Health Sciences and Technology
    Agrivida, 411 Massachusetts Ave B1)

  • Isidore Rigoutsos

    (Massachusetts Institute of Technology
    Thomas J.Watson Research Center)

  • Gregory Stephanopoulos

    (Massachusetts Institute of Technology)

Abstract

The language of antibiotics Antimicrobial peptides are used by the innate immune system to combat bacterial infection in multicellular eukaryotes, and they show promise as potential therapeutics as they seem less susceptible to bacterial resistance than other antibiotics. A linguistic model has been used to design a series of unnatural antimicrobial peptides with little homology to naturally occurring molecules, but with bacteriostatic activity against several species of bacteria, including Staphylococcus aureus and Bacillus anthracis.

Suggested Citation

  • Christopher Loose & Kyle Jensen & Isidore Rigoutsos & Gregory Stephanopoulos, 2006. "A linguistic model for the rational design of antimicrobial peptides," Nature, Nature, vol. 443(7113), pages 867-869, October.
    • Handle: RePEc:nat:nature:v:443:y:2006:i:7113:d:10.1038_nature05233
    DOI: 10.1038/nature05233
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    Cited by:

    1. William F Porto & Állan S Pires & Octavio L Franco, 2012. "CS-AMPPred: An Updated SVM Model for Antimicrobial Activity Prediction in Cysteine-Stabilized Peptides," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-7, December.
    2. Sutapa Datta & Subhasis Mukhopadhyay, 2013. "A Composite Method Based on Formal Grammar and DNA Structural Features in Detecting Human Polymerase II Promoter Region," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-11, February.
    3. Deepesh Nagarajan & Tushar Nagarajan & Neha Nanajkar & Nagasuma Chandra, 2019. "A Uniform In Vitro Efficacy Dataset to Guide Antimicrobial Peptide Design," Data, MDPI, vol. 4(1), pages 1-13, February.
    4. Sutapa Datta & Subhasis Mukhopadhyay, 2015. "A Grammar Inference Approach for Predicting Kinase Specific Phosphorylation Sites," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-19, April.

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