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Review
. 2021 Feb 23;15(2):2143-2164.
doi: 10.1021/acsnano.0c09509. Epub 2021 Feb 4.

Synthetic Biology and Computer-Based Frameworks for Antimicrobial Peptide Discovery

Marcelo D T Torres  1   2   3 , Jicong Cao  4 , Octavio L Franco  5   6 , Timothy K Lu  4 , Cesar de la Fuente-Nunez  1   2   3
Affiliations
Review

Synthetic Biology and Computer-Based Frameworks for Antimicrobial Peptide Discovery

Marcelo D T Torres et al. ACS Nano. .

Abstract

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.

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

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Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1.
Figure 1.
Multifunctional and diverse nature of AMPs and opportunity for sequence space exploration. (A) Schematic of AMPs as promising scaffolds for engineering multifunctional antimicrobial agents. Peptides are shown as well-known antimicrobial and antibiofilm agents and also as immunomodulatory, chemotactic, and anticancer therapeutics. (B) Biological evolution has only explored a tiny fraction of the total space of possibilities of all potential peptide molecules. Computational methods enable exploration of previously unexplored regions of sequence space that may yield synthetic peptides with enhanced biological function.
Figure 2.
Figure 2.
AMP manufacturing approaches. (A) AMPs can be manufactured by traditional chemical synthesis using solid-phase strategies, but these are time-consuming and costly. (B) High-throughput methods have been developed to speed up the process and make it cheaper and greener, allowing the exploration of AMP chemical space. (C) Large-scale manufacturing is needed for late-stage clinical trials and commercial production, and these processes have been boosted by synthetic biology techniques that allow the expression of peptides by living organisms. These are efficient and low-cost processes that can be used for diverse types of microorganisms and strategies.
Figure 3.
Figure 3.
Discovery tools for antimicrobial peptides. (A) AMPs might be generated by combinatorial synthesis, in silico or extracted from nature; however, these processes are time-consuming and costly. To optimize the discovery of AMPs, computational frameworks based on different methodologies have been developed. (B) Genetic algorithms are commonly used to evaluate and evolve sequences from templates. Another option is to use (C) pattern recognition algorithms to identify minimal portions that are responsible for the biological activities of peptides in redundant sequences or (D) quantitative structure–activity relationship studies to identify activity determinants that might be isolated for the generation of shorter peptides with optimized features for displaying targeted activity.
Figure 4.
Figure 4.
High-throughput frameworks for peptide design. (A) Structure-guided exploration of the functional hotspots of structural and physicochemical descriptors of AMP activity. (B) Algorithms and statistical models combined with existing bioactive peptide templates.
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