Synthetic Biology and Computer-Based Frameworks for Antimicrobial Peptide Discovery
Name
nihms-1759851.pdf
Description
Accepted version
Size
1.61 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Torres, Marcelo DT
Cao, Jicong
Franco, Octavio L
Lu, Timothy K
de la Fuente-Nunez, Cesar
Date Issued
2021
Journal
ACS Nano
Publisher
American Chemical Society (ACS)
Citation
Torres, Marcelo DT, Cao, Jicong, Franco, Octavio L, Lu, Timothy K and de la Fuente-Nunez, Cesar. 2021. "Synthetic Biology and Computer-Based Frameworks for Antimicrobial Peptide Discovery." ACS Nano, 15 (2).
Version
Author's final manuscript
Abstract
© 2021 American Chemical Society. Antibiotic resistance is one of the greatest challenges of our time. This global health problem originated from a paucity of truly effective antibiotic classes and an increased incidence of multi-drug-resistant bacterial isolates in hospitals worldwide. Indeed, it has been recently estimated that 10 million people will die annually from drug-resistant infections by the year 2050. Therefore, the need to develop out-of-the-box strategies to combat antibiotic resistance is urgent. The biological world has provided natural templates, called antimicrobial peptides (AMPs), which exhibit multiple intrinsic medical properties including the targeting of bacteria. AMPs can be used as scaffolds and, via engineering, can be reconfigured for optimized potency and targetability toward drug-resistant pathogens. Here, we review the recent development of tools for the discovery, design, and production of AMPs and propose that the future of peptide drug discovery will involve the convergence of computational and synthetic biology principles.
MIT Department
Massachusetts Institute of Technology. Synthetic Biology Center
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Research Laboratory of Electronics
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1021/ACSNANO.0C09509
