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Review
. 2023 Nov 18;44(6):1095-1114.
doi: 10.24272/j.issn.2095-8137.2023.246.

Design methods for antimicrobial peptides with improved performance

James Mwangi  1   2 , Peter Muiruri Kamau  1   2 , Rebecca Caroline Thuku  1   2 , Ren Lai  1   3
Affiliations
Review

Design methods for antimicrobial peptides with improved performance

James Mwangi et al. Zool Res. .

Abstract
in English, Chinese

The recalcitrance of pathogens to traditional antibiotics has made treating and eradicating bacterial infections more difficult. In this regard, developing new antimicrobial agents to combat antibiotic-resistant strains has become a top priority. Antimicrobial peptides (AMPs), a ubiquitous class of naturally occurring compounds with broad-spectrum antipathogenic activity, hold significant promise as an effective solution to the current antimicrobial resistance (AMR) crisis. Several AMPs have been identified and evaluated for their therapeutic application, with many already in the drug development pipeline. Their distinct properties, such as high target specificity, potency, and ability to bypass microbial resistance mechanisms, make AMPs a promising alternative to traditional antibiotics. Nonetheless, several challenges, such as high toxicity, lability to proteolytic degradation, low stability, poor pharmacokinetics, and high production costs, continue to hamper their clinical applicability. Therefore, recent research has focused on optimizing the properties of AMPs to improve their performance. By understanding the physicochemical properties of AMPs that correspond to their activity, such as amphipathicity, hydrophobicity, structural conformation, amino acid distribution, and composition, researchers can design AMPs with desired and improved performance. In this review, we highlight some of the key strategies used to optimize the performance of AMPs, including rational design and de novo synthesis. We also discuss the growing role of predictive computational tools, utilizing artificial intelligence and machine learning, in the design and synthesis of highly efficacious lead drug candidates.

病原体对传统抗生素的抵抗力使得治疗和根除细菌感染变得更加困难。对此，开发新型抗菌药物来对抗抗生素耐药菌株已成为当务之急。抗菌肽 (AMP) 是一类普遍存在的天然化合物，具有广谱抗病原体活性，有望成为当前抗菌素耐药性 (AMR) 危机的有效解决方案。多种 AMP 的治疗应用已得到鉴定和评估，其中许多已进入药物开发流程. AMP 具有许多独特的特性，例如高靶点特异性、效力高以及绕过微生物耐药机制的能力，使其成为传统抗生素的有前途的替代品。然而，一些挑战，如高毒性、不易蛋白水解降解、稳定性低、药代动力学差和生产成本高等，继续阻碍其临床应用。因此，最近的研究重点是优化 AMP 的特性以提高其性能。通过了解与其活性相对应的 AMP 理化特性，例如两亲性、疏水性、结构构象、氨基酸分布和组成，研究人员可以设计出具有所需和改进性能的 AMP。该综述重点介绍了用于优化 AMP 性能的一些关键策略，包括合理设计和从头合成，还讨论了利用人工智能和机器学习预测计算工具在设计和合成高效先导候选药物中日益重要的作用。.

Keywords: Antimicrobial peptides; Antimicrobial resistance; Artificial intelligence; Design methods; Peptidomimetics.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Graphical summary of origin and classification of antimicrobial peptides
Figure 2
Figure 2
Helical wheel projections of magainin 2, LL-37, and their derivatives (pexiganan and SAAP-148, respectively)
Figure 3
Figure 3
Representation of several techniques (C-terminal amidation and N-terminal modification acetylation) used to optimize AMP performance
Figure 4
Figure 4
Two linear AMP cyclization techniques (disulfide bridge formation and head-to-tail beta-lactam ring formation)
Figure 5
Figure 5
Conjugation or encapsulation of AMPs with different nanoparticles to optimize performance
Figure 6
Figure 6
Representation of several techniques used to generate peptidomimetics
Figure 7
Figure 7
Schematic representation of multiple steps involved in de novo design, synthesis, and evaluation of therapeutic potential of novel peptide sequences
Figure 8
Figure 8
General overview of AMP design using artificial intelligence and machine learning models
See this image and copyright information in PMC

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Grants and funding

This work was supported by the National Natural Science Foundation of China (31930015, 32200397), Ministry of Science and Technology of China (2018YFA0801403), Chinese Academy of Sciences (XDB31000000, KFJ-BRP-008-003), Yunnan Province Grant (202003AD150008, 202002AA100007), Kunming Science and Technology Bureau (2023SCP001), and New Cornerstone Investigator Program
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