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基于结构的铜绿假单胞菌抗毒力靶点药物可开发性评估。

Structure-based Druggability Assessment of Anti-virulence Targets from Pseudomonas aeruginosa.

机构信息

Programa de Pos-Graduacao em Biotecnologia da Universidade Estadual de Feira de Santana, Feira de Santana, BA, Brazil.

aculdade de Farmácia da Universidade Federal da Bahia, Bahia, Salvador, BA, Brazil.

出版信息

Curr Protein Pept Sci. 2019;20(12):1189-1203. doi: 10.2174/1389203720666190417120758.

Abstract

Antimicrobial Resistance (AMR) represents a serious threat to health and the global economy. However, interest in antibacterial drug development has decreased substantially in recent decades. Meanwhile, anti-virulence drug development has emerged as an attractive alternative to fight AMR. Although several macromolecular targets have been explored for this goal, their druggability is a vital piece of information that has been overlooked. This review explores this subject by showing how structure- based freely available in silico tools, such as PockDrug and FTMap, might be useful for designing novel inhibitors of the pyocyanin biosynthesis pathway and improving the potency/selectivity of compounds that target the Pseudomonas aeruginosa quorum sensing mechanism. The information provided by hotspot analysis, along with binding site features, reveals novel druggable targets (PhzA and PhzS) that remain largely unexplored. However, it also highlights that in silico druggability prediction tools have several limitations that might be overcome in the near future. Meanwhile, anti-virulence drug targets should be assessed by complementary methods, such as the combined use of FTMap/PockDrug, once the consensus druggability classification reduces the risk of wasting resources on undruggable proteins.

摘要

抗菌药物耐药性(AMR)对健康和全球经济构成严重威胁。然而,近几十年来,人们对抗菌药物开发的兴趣大幅下降。与此同时,抗毒力药物的开发已成为对抗 AMR 的一种有吸引力的替代方法。尽管已经探索了几种用于这一目标的大分子靶点,但它们的成药性是一个被忽视的重要信息。本综述通过展示如何使用基于结构的免费计算机工具(如 PockDrug 和 FTMap)来设计新型绿脓菌素生物合成途径抑制剂,并提高针对铜绿假单胞菌群体感应机制的化合物的效力/选择性,探讨了这一主题。热点分析提供的信息以及结合位点特征揭示了新的可成药性靶点(PhzA 和 PhzS),这些靶点在很大程度上尚未被探索。然而,这也突显出,计算机药物成药性预测工具存在一些限制,这些限制可能在不久的将来得到克服。同时,抗毒力药物靶点应该通过互补方法进行评估,例如 FTMap/PockDrug 的联合使用,一旦共识药物成药性分类降低了在不可成药性蛋白上浪费资源的风险。

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