Combining Machine Learning, Molecular Dynamics and Membrane Biophysics to identify new therapeutics for the treatment of Tuberculosis

结核病（TB）目前是世界上主要的死亡原因之一，仅2017年就报告了1000万新发病例，130万人死亡（2018年全球结核病报告WHO），另一个复杂因素是多药和完全耐药菌株的演变。迫切需要开发有效的新治疗剂来靶向TB，并且在该过程中关键的是鉴定合适的靶向蛋白质。MmpL 3是一种跨膜蛋白，其对于细菌细胞的复制和活力是必需的，因此代表了合适的靶标。MmpL 3的结构测定表明，MmpL 3的结构与MmpL 3的结构一致。smeglitazone（Cell 2019; 176：636-648）为开发新的治疗策略提供了起点。将利用分子动力学（MD）模拟来构建M的MmpL 3模型。结核分枝杆菌（Mtb）的抑制剂有助于研究药物-蛋白质相互作用，已知的抑制剂将在生理条件下建模，其中蛋白质嵌入细胞膜的真实表示中。计算模型的验证将通过研究模型膜的结构和力学来实现，其中Mtb MmpL 3被嵌入，通过X射线衍射和光学显微镜。已知抑制剂与Mtb MmpL 3和已知耐药突变体的结合模式的鉴定将被用作机器学习（ML）的输入，以生成搜索大型化合物文库（特别是锌数据库）的规则，从而鉴定合适的化合物以进行筛选。该项目将为学生提供广泛的技能，计算建模，机器学习，蛋白质表达和纯化以及实验膜生物物理学。

Tuberculosis (TB) is currently one of the world's leading causes of mortality with 10 million new cases reported in 2017 alone and 1.3 million deaths (Global Tuberculosis Report 2018 WHO), a further complicating factor is the evolution of multi-drug and totally drug resistant strains. There is an urgent need to develop effective new therapeutic agents to target TB and critical in this process is the identification of a suitable protein to target. MmpL3 is a transmembrane protein which is essential for the replication and viability of bacterial cells and therefore represents a suitable target. The recent determination of the structure of MmpL3 from M. smegmatis (Cell 2019; 176: 636-648) provides the starting point for developing new therapeutic strategies. Molecular Dynamics (MD) simulations will be utilised to construct a model of MmpL3 for M. tuberculosis (Mtb) facilitating investigation of drug-protein interactions, known inhibitors will be modelled at physiological conditions with the protein embedded in a realistic representation of the cell membrane. Validation of the computational model will be achieved through the investigation of the structure and mechanics of model membranes, in which Mtb MmpL3 is embedded, via X-ray diffraction and light microscopy. Identification of the binding modes of know inhibitors to Mtb MmpL3 and known drug resistant mutants will be used as input into Machine Learning (ML) to generate rules to search large compound libraries, in particular the Zinc database, to identify suitable compounds to screen. This project will provide the student with a broad range of skills, computational modelling, machine learning, protein expression and purification and experimental membrane biophysics.