Martins Fábio G, Melo André, Sousa Sérgio F
UCIBIO/REQUIMTE, BioSIM-Departamento de Biomedicina, Faculdade de Medicina da Universidade do Porto, 4200-319 Porto, Portugal.
LAQV/REQUIMTE, Departamento de Química e Bioquímica, Faculdade de Ciências da Universidade do Porto, 4169-007 Porto, Portugal.
Molecules. 2021 Apr 29;26(9):2600. doi: 10.3390/molecules26092600.
Biofilms are aggregates of microorganisms anchored to a surface and embedded in a self-produced matrix of extracellular polymeric substances and have been associated with 80% of all bacterial infections in humans. Because bacteria in biofilms are less amenable to antibiotic treatment, biofilms have been associated with developing antibiotic resistance, a problem that urges developing new therapeutic options and approaches. Interfering with quorum-sensing (QS), an important process of cell-to-cell communication by bacteria in biofilms is a promising strategy to inhibit biofilm formation and development. Here we describe and apply an in silico computational protocol for identifying novel potential inhibitors of quorum-sensing, using CviR-the quorum-sensing receptor from -as a model target. This in silico approach combines protein-ligand docking (with 7 different docking programs/scoring functions), receptor-based virtual screening, molecular dynamic simulations, and free energy calculations. Particular emphasis was dedicated to optimizing the discrimination ability between active/inactive molecules in virtual screening tests using a target-specific training set. Overall, the optimized protocol was used to evaluate 66,461 molecules, including those on the ZINC/FDA-Approved database and to the Mu.Ta.Lig Virtual Chemotheca. Multiple promising compounds were identified, yielding good prospects for future experimental validation and for drug repurposing towards QS inhibition.
生物膜是附着于表面并嵌入自身产生的细胞外聚合物基质中的微生物聚集体，与人类80%的细菌感染有关。由于生物膜中的细菌对抗生素治疗不太敏感，生物膜与抗生素耐药性的产生有关，这一问题促使人们开发新的治疗选择和方法。干扰群体感应（QS），这是生物膜中细菌细胞间通讯的一个重要过程，是抑制生物膜形成和发展的一种有前景的策略。在这里，我们描述并应用了一种计算机计算方案，以来自的群体感应受体CviR为模型靶点，识别群体感应的新型潜在抑制剂。这种计算机方法结合了蛋白质-配体对接（使用7种不同的对接程序/评分函数）、基于受体的虚拟筛选、分子动力学模拟和自由能计算。特别强调使用目标特异性训练集来优化虚拟筛选测试中活性/非活性分子之间的区分能力。总体而言，优化后的方案用于评估66461个分子，包括ZINC/FDA批准数据库中的分子以及Mu.Ta.Lig虚拟化学库中的分子。鉴定出了多种有前景的化合物，为未来的实验验证以及针对群体感应抑制的药物再利用带来了良好的前景。
