Extending the power of structure-guided fragment-based drug discovery (FBDD)

该项目的目标是扩展结构引导的基于片段的药物发现（FBDD）的力量，以开发快速，稳健和有效的方法来创建针对脓肿分枝杆菌的抗生素。因此，我将：优化化学支架-靶标选择，通过(i)使用多样性导向合成创建的hit-finding片段文库的表型筛选，然后（ii）通过光化学交联、全细胞热移测定、逃逸突变体的全基因组测序和针对现有的阵列诱导CRISPR-dCas9亚形态筛选来识别靶标；2. 使用基于lc - ms的化合物筛选的机器学习化学信息学分析，定义细菌渗透、保留和代谢的化学规则；和3。利用这些新的数据集来优化药物靶点和化学选择、结合位点开发和化学修饰，以(i)追求基于片段的药物发现活动（由x射线晶体学指导）和（ii）实施超大尺寸的按需化合物的硅筛选，以提供即时抗菌药物样分子。

The aims of this project are to extend the power of structure-guided fragment-based drug discovery (FBDD) to develop rapid, robust, and effective methods to create antibiotics against Mycobacterium abscessus. I will therefore: 1. Optimise chemical scaffold-target selection, by (i) phenotypic screening of hit-finding fragment libraries created using diversity oriented synthesis, and then (ii) identifying targets through photochemical cross-linking, whole cell thermal shift assay, whole genome sequencing of escape mutants, and screening against existing arrayed inducible CRISPR-dCas9 hypomorphs; 2. Define the chemical rules for bacterial permeation, retention, and metabolism, using machine-learning cheminformatic analysis of LC-MS-based compound screening; and 3. Exploit these new datasets for optimising drug target and chemical selection, binding site exploitation, and chemical elaboration to (i) pursue a fragment-based drug discovery campaign (guided by X-ray crystallography) and (ii) implement ultra-large in silico screening of make-on demand compounds to deliver instant antimicrobial drug-like molecules.