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，尽管计算工具相对于实验工具更便宜，但在仍然是一个迭代和主观的过程中，它们代表了巨大的时间成本。药物发现的计算方面在 COVID-19 大流行期间尤其重要，最近的 Moonshot 工作就是例证。一开始是对 SARS-CoV-2 主要蛋白酶进行特别大的片段筛选，现在涉及众包一组易于合成的多样化片段，以创建一种对于长期管理这一流行病至关重要的抗病毒药物化合物。 Moonshot 项目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功效。一般来说，基于片段的药物发现 (FBDD) 过程涉及使用筛选过程（例如 X 射线晶体学筛选）中的小分子命中并优化它们以产生先导化合物。这些优化的片段对其靶标的亲和力可以提高多个数量级。 FBDD 允许理性和目标驱动的先导开发，并且该领域仍在不断发展，目前有数十种药物正在进行临床试验。然而，到目前为止，FBDD 贡献的药物只有 4 种已推向市场。该项目要回答的问题是：是否可以通过针对特定细菌靶点以集成方式使用单独的结构生物学/药物发现平台来实现此类工作流程的自动化？非专业人士可以使用自动化管道吗？将评估各个工具的可靠性、准确性和可扩展性，并将使用 Python 开发集成它们的方法。 DeLinker 是一个很有前景的（基于 Python 的）工具，可以合并到计算机辅助药物设计 (CADD) 流程中的自动化工作流程中。 DeLinker 是一种用于片段链接和支架跳跃的机器学习方法，它解决了 3D 生成片段链接软件的缺乏。它可以使用两个初始片段的空间信息产生连接子，利用它们之间的距离和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Python工作流程将通过优化两个细菌目标（转录调节因子）的线索来验证单核细胞增生李斯特菌中的 PrfA 和结核分枝杆菌的毒素-抗毒素系统。单核细胞增生李斯特菌是一种食源性病原体，可引起李斯特菌病，每年都会发生大规模爆发。潜在的目标是单核细胞增生李斯特菌的毒力机制，并且不具有杀菌性，因此相对于传统抗菌方法，产生耐药性的可能性较小。先前在单增李斯特菌中使用环融合的 2-吡啶酮杂环鉴定出已知的蛋白内“隧道”抑制剂，该抑制剂通过结合和减弱 PrfA 来降低毒力，因此这个特定靶点对于采用片段连接方法来开发抑制剂的时机已经成熟具有抗毒特性，含有该部分。结核分枝杆菌 (TB) 是传染病导致死亡的第一大原因。毒素-抗毒素系统调节细胞过程，是治疗靶标，结核病中的毒素 MbcT 具有杀菌作用，除非被其抗毒素 MbcA 中和，并导致细胞快速死亡。寻找 MbcTA（毒素-抗毒素）复合物或失活的 MbcA 抗毒素的小分子抑制剂可能是对抗结核病的一种途径。