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Synthetic Biology and Computer-Based Frameworks for Antimicrobial Peptide Discovery

Journal

ACS NANO
Volume 15, Issue 2, Pages 2143-2164

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acsnano.0c09509

Keywords

antimicrobial peptides; peptide design; computational biology; synthetic biology; peptide chemistry; rational design; molecular design frameworks; peptide discovery

Funding

  1. AIChE Foundation
  2. Institute for Diabetes, Obesity, and Metabolism
  3. Penn Mental Health AIDS Research Center of the University of Pennsylvania
  4. National Institute of General Medical Sciences of the National Institutes of Health [R35GM138201]
  5. CNPq
  6. CAPES
  7. FUNDECT
  8. FAPDF

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Antibiotic resistance poses a significant global health challenge, with the need for innovative strategies becoming more urgent. Antimicrobial peptides (AMPs) offer a natural template for the discovery, design, and production of antibiotics, potentially leading the way for future advancements in peptide drug discovery through computational and synthetic biology approaches.
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.

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