Harnessing Computational and Structural Biology Platforms for Drug Discovery

利用计算和结构生物学平台进行药物发现

• 批准号: 2440409
• 金额: --
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2440409
• 关键词: Harnessing; Computational; Structural; Biology; Platforms

Drug discovery is expensive and laborious, and although computational tools are cheaper relative to their experimental counterparts, they represent a significant time cost in what is still an iterative and subjective process. Computational aspects of drug discovery are particularly relevant during the COVID-19 pandemic, exemplified by the recent Moonshot effort. This began as a particularly large fragment screening against SARS-CoV-2's main protease and now involves crowdsourcing a set of easily synthesised and diverse fragments in order to create an anti-viral drug compound that will be vital for long term management of the pandemic. The Moonshot effort is composed of scientists from all over the world who have submitted a combined total of over 15,000 fragments. Pooled computer resources are then used to generate lead compounds, which can then be synthesised and tested for efficacy. This process of fragment-based drug discovery (FBDD), in general, involves using small molecule hits from a screening process (e.g. X-ray crystolographic screening) and optimising them to produce leads. These optimised fragments can show an increased affinity for their targets by multiple orders of magnitude. FBDD allows rational and target-driven lead generation, and the field is still growing with dozens of drugs currently in clinical trials. However as of yet only 4 drugs that FBDD has contributed to, have been brought to market. The question this project sets out to answer is:Can this sort of workflow be automated by using individual structural biology/drug discovery platforms in an integrated way for particular bacterial targets? Can the automated pipeline be used by a non-specialist? Individual tools will be assessed for reliability, accuracy and extensibility and a method of integrating them will be developed, using python. An example of a promising (Python-based) tool that can be incorporated into an automated workflow in the Computer-Aided Drug Design (CADD) pipeline is DeLinker. DeLinker is a machine learning approach for fragment linking and scaffold hopping, which addresses the lack of 3D generative fragment-linking software. It can produce linkers using spatial information of two initial fragments, utilising the distance between them and their relative orientations to produce novel linkers not in the initial training database.The automated Python workflow will be validated by optimising leads for two bacterial targets, the transcriptional regulator PrfA in Listeria monocytogenes and the toxin-antitoxin system of Mycobacterium tuberculosis. L. monocytogenes is a food-borne pathogen that causes listerosis, with major outbreaks occuring on an annual basis. A potential target is the virulence machinery in L. monocytogenes and is non-bactericidal, so has less chance of resistance relative to traditional antibacterial approaches. There are known inhibitors for an intraprotein 'tunnel' previously identified in L. monocytogenes using ring-fused 2-pyridone hetero-cycles that reduced virulence by binding and attenuating PrfA, so this particular target is ripe for a fragment-linking approach to develop inhibitors with antivirulence properties, containing that moiety. M. tuberculosis (TB) is the number one cause of death from infectious disease. Toxin-antitoxin systems regulate cellular processes and are therapeutic targets, and the toxin in TB, MbcT, is bactericidal unless neutralized by its antitoxin MbcA, and causes rapid cell death. The search for a small molecule inhibitor for the MbcTA (toxin-antitoxin) complex or the inactivate MbcA antitoxin could be an avenue to combat TB.

药物发现既昂贵又费力，尽管计算工具比实验工具便宜，但在仍然是一个反复和主观的过程中，它们代表着相当大的时间成本。在新冠肺炎大流行期间，药物发现的计算方面尤其相关，最近的登月努力就是一个例证。这项工作最初是针对SARS-CoV-2 S主蛋白酶的一个特别大的片段筛选，现在涉及到众包一套易于合成和多样化的片段，以创造一种对长期控制疫情至关重要的抗病毒药物化合物。登月计划由来自世界各地的科学家组成，他们总共提交了超过15,000个碎片。然后使用汇集的计算机资源来生成先导化合物，然后可以合成这些化合物并测试其有效性。这一基于片段的药物发现(FBDD)过程通常涉及使用筛选过程(例如X射线晶体筛选)中的小分子命中，并对其进行优化以产生铅。这些优化的片段可以显示出对其目标的亲和力增加了好几个数量级。FBDD允许理性和目标驱动的铅产生，而且该领域仍在增长，目前有数十种药物处于临床试验中。然而，到目前为止，FBDD贡献的只有4种药物已经上市。这个项目要回答的问题是：这种工作流程是否可以通过使用针对特定细菌目标的单独结构生物学/药物发现平台的集成方式来实现自动化？自动化管道可以由非专业人员使用吗？将对各个工具的可靠性、准确性和可扩展性进行评估，并将使用Python开发一种整合这些工具的方法。DeLinker就是一个很有前途的(基于Python的)工具，它可以整合到计算机辅助药物设计(CADD)流水线中的自动化工作流程中。DeLinker是一种用于片段链接和脚手架跳跃的机器学习方法，它解决了缺乏3D生成性片段链接软件的问题。它可以利用两个初始片段的空间信息来产生连接子，利用它们之间的距离和它们的相对方向来产生初始训练数据库中没有的新连接子。自动化的巨蟒工作流将通过优化两个细菌靶标-单核细胞增生性李斯特菌中的转录调控因子PrFA和结核分枝杆菌的毒素-抗毒素系统的引线来验证。单核细胞增多性李斯特菌是一种可引起李斯特菌病的食源性病原体，每年都会发生重大疫情。一个潜在的靶点是单核细胞增多性李斯特氏菌的毒力机制，而且是非杀菌的，因此与传统的抗菌方法相比，耐药的可能性较小。已知的是先前在单核细胞增多性李斯特氏菌中发现的蛋白内“隧道”的抑制剂，该抑制剂使用环融合的2-吡啶酮异环，通过结合和减弱PRFA来降低毒力，所以这个特定的靶点已经成熟，可以用片段连接的方法来开发具有抗毒力特性的抑制剂，其中包含该部分。结核分枝杆菌(TB)是传染病的头号死因。毒素-抗毒素系统调节细胞过程，是治疗的靶点，而结核中的毒素MBCT，除非被其抗毒素MBCA中和，否则是杀菌的，并导致细胞迅速死亡。寻找MbcTA(毒素-抗毒素)复合体或灭活的MBCA抗毒素的小分子抑制剂可能是对抗结核病的一种途径。