Developing an integrative bioinformatic approach for the selection of therapeutic targets

开发用于选择治疗靶点的综合生物信息学方法

• 批准号: RGPIN-2018-04931
• 负责人: Schapira, Matthieu
• 金额: $ 1.68万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=664436
• 关键词: Developing; integrative; bioinformatic; approach; selection

The exploding volume of genomic and structural data related to human biology, and the continuous development of computational tools to interrogate this data generate novel opportunities to investigate human biology. The goal of this research program is to apply this paradigm to the target discovery process. We will develop novel methodologies to identify protein targets that both have clear genetic linkage to disease, and are structurally druggable. Our long-term goal is to expand methodologies and lessons learnt from this exercise to further integrate at the atomic and genetic level the molecular circuitry that underlies biological processes. Embracing an open science concept will facilitate the dissemination of our findings across and beyond our existing network of collaborators, and maximize our impact.******Selection of a target protein is the first step in most drug discovery programs, and determines whether the program will succeed or fail to deliver a new drug to patients. If the three-dimensional structure of the target is not chemically tractable, the program is doomed before it started. Equally important is the necessity to establish a causative and essential role of the targeted protein in the disease pathway. To increase the success rate in drug discovery, the target selection process needs to be better informed.******Big Data that started flooding the biomedical community is a transformative source of information. The global goal of my research program is to integrate structural and genomics data to investigate human biology, with a focus on the regulation of transcription in health and disease. In this proposal, while focusing on the regulation of gene expression, an area of biology that is central to multiple disease pathways but has resisted most drug discovery efforts, we will interface large databases of chromosomal aberrations from cancer patients and synthetic lethality screens (where deletion of Gene A causes dependency on Gene B for survival) to identify tumor-specific genetic vulnerabilities, and reveal proteins that should be targeted by drugs. We will also analyze the druggability and structural diversity of binding pockets at the surface of thousands of high-resolution structures of human proteins to identify targets that physically can be targeted by drugs. We will map proteins that can, that should, and that are targeted by drugs in the context of protein networks and signalling pathways, and identify novel high-priority targets that so far have been neglected. Students working on this project will acquire a rarely combined set of expertise in structural bioinformatics and computational genomics, and will benefit from a cross-disciplinary, open space environment composed of structural biologists, biochemists, cell biologists. They will also be able to share with and learn from the vibrant local bioinformatics community of downtown Toronto.

与人类生物学相关的基因组和结构数据的爆炸式增长，以及用于查询这些数据的计算工具的不断发展，为研究人类生物学带来了新的机会。该研究计划的目标是将这种范式应用于目标发现过程。我们将开发新的方法来识别与疾病具有明确遗传联系并且在结构上可药物化的蛋白质靶标。我们的长期目标是扩展从这次实践中吸取的方法和经验教训，以在原子和基因水平上进一步整合生物过程背后的分子电路。拥抱开放科学概念将促进我们的发现在现有合作者网络中传播，并最大限度地发挥我们的影响力。******目标蛋白的选择是大多数药物发现计划的第一步，并决定是否该计划将成功或失败地向患者提供新药。如果目标的三维结构无法通过化学方法处理，那么该计划在开始之前就注定失败。同样重要的是有必要确定目标蛋白在疾病途径中的致病和重要作用。为了提高药物发现的成功率，需要更好地了解目标选择过程。******开始充斥生物医学界的大数据是一种变革性的信息来源。我的研究计划的全球目标是整合结构和基因组数据来研究人类生物学，重点是健康和疾病中的转录调控。在这项提案中，虽然重点关注基因表达的调控，这是多种疾病途径的核心生物学领域，但已经抵制了大多数药物发现工作，但我们将连接癌症患者染色体畸变的大型数据库和合成致死率筛选（其中删除基因 A 导致生存依赖基因 B）来识别肿瘤特异性遗传漏洞，并揭示药物应靶向的蛋白质。我们还将分析数千种人类蛋白质高分辨率结构表面的结合袋的成药性和结构多样性，以确定物理上可以被药物靶向的靶点。我们将在蛋白质网络和信号通路的背景下绘制可以、应该和药物靶向的蛋白质图谱，并确定迄今为止被忽视的新的高优先级靶标。从事该项目的学生将获得结构生物信息学和计算基因组学方面很少组合的专业知识，并将受益于由结构生物学家、生物化学家、细胞生物学家组成的跨学科、开放空间环境。他们还将能够与多伦多市中心充满活力的当地生物信息学社区分享并向其学习。