Mining for antimicrobial peptides in sequence space

A machine-learning pipeline identifies potent antimicrobial peptides by gradually narrowing down the search space of polypeptide chain sequences. Access through your institution Buy or

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checkout ADDITIONAL ACCESS OPTIONS: * Log in * Learn about institutional subscriptions * Read our FAQs * Contact customer support REFERENCES * Murray, C. J. L. et al. _Lancet_ 399, 629–655

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references AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational

Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA Fangping Wan & Cesar de la Fuente-Nunez * Departments of Bioengineering and

Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA Fangping Wan & Cesar de la Fuente-Nunez * Penn

Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA Fangping Wan & Cesar de la Fuente-Nunez Authors * Fangping Wan View author publications You can

also search for this author inPubMed Google Scholar * Cesar de la Fuente-Nunez View author publications You can also search for this author inPubMed Google Scholar CORRESPONDING AUTHOR

Correspondence to Cesar de la Fuente-Nunez. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS

ARTICLE CITE THIS ARTICLE Wan, F., de la Fuente-Nunez, C. Mining for antimicrobial peptides in sequence space. _Nat. Biomed. Eng_ 7, 707–708 (2023).

https://doi.org/10.1038/s41551-023-01027-z Download citation * Published: 24 April 2023 * Issue Date: June 2023 * DOI: https://doi.org/10.1038/s41551-023-01027-z SHARE THIS ARTICLE Anyone

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