Computational selection of druggable targets for the development of antifungals

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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.