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Computational selection of druggable targets for the development of antifungals

用于开发抗真菌药物的药物靶标的计算选择

基本信息

批准号：
1926880
负责人：
金额：
--
依托单位：
University of Manchester
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-1926880
关键词：
Computational selection druggable targets development

项目摘要

The increasing emergence of multi-drug-resistant microorganisms has led the World Health Organisation to plead for action against antimicrobial resistance (AMR). In order to control the spread of infectious diseases and to improve standard of health, is critical that new classes of antimicrobial agents with novel mechanisms of action are developed. Fungal infections pose a serious threat to health affecting about 1.7 billion people worldwide and causing about 1.5 million deaths each year. The majority of mortality is caused by Aspergillus and Candida infections. Our genome wide studies in A. fumigatus and C. albicans have identified a number of essential enzymes (phosphatases and kinases) for the growth of these pathogens, which constitute attractive targets for antifungal therapy. Our aim is to exploit these targets using computational-based identification of potential leads for the development of new antifungal drugs.The use of computational screening of compound libraries against selected targets has expanded exponentially the boundaries of drug discovery in recent years. Rapid identification of potential lead compounds is an essential part of the current global health agenda to fight diseases. Expanding the portfolio of lead compounds means that more drugs can be advanced through clinical trials and better treatments will be possible in the near future. The success of computerbased approaches (molecular docking, genetic algorithms) provides now a number of tools that can be combined to produce high performance/high speed selection of suitable compounds for furtherexperimental testing and functional validation. Recently, we have developed an automatic pipeline for quick screening of compound libraries (VSPipe) that enables rapid identification of inhibitor leads for drug discovery. Now, we want to expand this tool by incorporating computational-based approaches to identify druggable host spots on the selected phosphatase/kinase targets, to evaluate lead-like properties in the ligands and to generate specific pharmacophore models to enable highthroughputdrug design. This project aims to deliver new tools for rapid identification of new leads for drug development.The project will involve the use of several software packages to build a resource suitable for open access and public use, applicable to any protein target. In addition, we will validate the screening results experimentally by using enzyme inhibition and antifungal activity assays.

越来越多的耐多药微生物的出现导致世界卫生组织呼吁采取行动对抗抗菌素耐药性(AMR)。为了控制传染病的传播和提高健康水平，开发具有新作用机制的新型抗菌剂是至关重要的。真菌感染对健康构成严重威胁，每年影响全球约17亿人，造成约150万人死亡。大多数死亡是由曲霉和念珠菌感染引起的。我们对烟曲霉和白色念珠菌的全基因组研究已经确定了一些对这些病原体的生长至关重要的酶(磷酸酶和激酶)，这些酶构成了吸引人的抗真菌治疗的靶点。我们的目标是通过基于计算的识别潜在的线索来开发这些靶点，以开发新的抗真菌药物。近年来，针对选定的靶点对化合物文库进行计算筛选的使用已经指数级地扩展了药物发现的边界。快速确定潜在的先导化合物是当前抗击疾病的全球卫生议程的重要组成部分。扩大先导化合物的产品组合意味着更多的药物可以通过临床试验推进，更好的治疗方法将在不久的将来成为可能。基于计算机的方法(分子对接、遗传算法)的成功提供了许多工具，这些工具可以结合在一起产生高性能/高速选择合适的化合物，用于进一步的实验测试和功能验证。最近，我们开发了一种快速筛选化合物文库的自动管道(VSPipe)，它能够快速识别药物发现的抑制剂先导。现在，我们希望通过结合基于计算的方法来扩展这一工具，以确定选定的磷酸酶/激酶靶标上的可药物宿主点，评估配体中的类铅属性，并生成特定的药效团模型，以实现高通量药物设计。这个项目旨在提供新的工具来快速识别药物开发的新线索。该项目将涉及使用几个软件包来建立一个适合开放获取和公共使用的资源，适用于任何蛋白质靶标。此外，我们将通过酶抑制和抗真菌活性试验对筛选结果进行实验验证。