Pirtskhalava Malak, Vishnepolsky Boris, Grigolava Maya, Managadze Grigol
Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi 0160, Georgia.
Pharmaceuticals (Basel). 2021 May 17;14(5):471. doi: 10.3390/ph14050471.
Antimicrobial peptides (AMPs) are anti-infectives that have the potential to be used as a novel and untapped class of biotherapeutics. Modes of action of antimicrobial peptides include interaction with the cell envelope (cell wall, outer- and inner-membrane). A comprehensive understanding of the peculiarities of interaction of antimicrobial peptides with the cell envelope is necessary to perform a rational design of new biotherapeutics, against which working out resistance is hard for microbes. In order to enable de novo design with low cost and high throughput, in silico predictive models have to be invoked. To develop an efficient predictive model, a comprehensive understanding of the sequence-to-function relationship is required. This knowledge will allow us to encode amino acid sequences expressively and to adequately choose the accurate AMP classifier. A shared protective layer of microbial cells is the inner, plasmatic membrane. The interaction of AMP with a biological membrane (native and/or artificial) has been comprehensively studied. We provide a review of mechanisms and results of interactions of AMP with the cell membrane, relying on the survey of physicochemical, aggregative, and structural features of AMPs. The potency and mechanism of AMP action are presented in terms of amino acid compositions and distributions of the polar and apolar residues along the chain, that is, in terms of the physicochemical features of peptides such as hydrophobicity, hydrophilicity, and amphiphilicity. The survey of current data highlights topics that should be taken into account to come up with a comprehensive explanation of the mechanisms of action of AMP and to uncover the physicochemical faces of peptides, essential to perform their function. Many different approaches have been used to classify AMPs, including machine learning. The survey of knowledge on sequences, structures, and modes of actions of AMP allows concluding that only possessing comprehensive information on physicochemical features of AMPs enables us to develop accurate classifiers and create effective methods of prediction. Consequently, this knowledge is necessary for the development of design tools for peptide-based antibiotics.
抗菌肽(AMPs)是一类抗感染药物,有潜力作为一类新型且尚未开发的生物治疗剂。抗菌肽的作用方式包括与细胞包膜(细胞壁、外膜和内膜)相互作用。全面了解抗菌肽与细胞包膜相互作用的特性,对于合理设计新型生物治疗剂至关重要,因为微生物很难对其产生耐药性。为了实现低成本、高通量的从头设计,必须借助计算机预测模型。要开发高效的预测模型,需要全面了解序列与功能的关系。这些知识将使我们能够有效地编码氨基酸序列,并恰当地选择准确的抗菌肽分类器。微生物细胞的一个共同保护层是内质膜。抗菌肽与生物膜(天然和/或人工)的相互作用已得到广泛研究。我们依据对抗菌肽物理化学、聚集和结构特征的研究,综述了抗菌肽与细胞膜相互作用的机制和结果。抗菌肽作用的效力和机制从氨基酸组成以及极性和非极性残基沿链的分布方面进行阐述,即从肽的物理化学特征如疏水性、亲水性和两亲性方面进行阐述。对现有数据的研究突出了一些主题,这些主题对于全面解释抗菌肽的作用机制以及揭示肽发挥其功能所必需的物理化学特性至关重要。已经使用了许多不同的方法对抗菌肽进行分类,包括机器学习。对抗菌肽的序列、结构和作用方式的知识研究表明,只有掌握抗菌肽物理化学特征的全面信息,才能开发出准确的分类器并创建有效的预测方法。因此,这些知识对于基于肽抗生素的设计工具开发是必要的。