A Translator Knowledge Provider for Systems Chemical Biology

系统化学生物学翻译知识提供者

• 批准号: 10548044
• 负责人: PAUL ANDREW CLEMONS
• 金额: $ 71.13万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-23 至 2022-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10548044
• 关键词: Biology; Chemicals; Knowledge; Provider; System; Text

Chemical biology and systems biology operate at different scales of molecular or cellular organization, requiring translation between the disciplines (e.g., terminology, existing evidence, new findings) to allow each to inform the other (1, 2). We propose a Biomedical Data Translator Knowledge Provider for Systems Chemical Biology to address this need. Centrally, the promise of systems chemical biology (SCB) is discovering novel therapeutics and re-purposing opportunities, avoiding toxicity and other side effects, and understanding how small molecules function (i.e., mechanism-of-action studies). Many evidence types used to solve such problems can also be used to understand functions of alterations at similar molecular scales, such as post-translational modification and protein-coding genetic variants. Currently, researchers in the field face major data access and interpretation challenges. For example, to support mechanism-of-action (MoA) studies, many experimental methods produce relevant data (gene-expression, protein-protein interactions, diverse bioactivities against pure proteins and cells, etc.) that are too numerous and heterogeneous to query for most researchers. Many web-based tools exist to share such data, but they are scattered, not always easy to find, and generally lack any communication between each other. As the field matures and increasingly embraces high-throughput experiments, these problems will only get worse (3). Our proposed SCB Knowledge Provider will mitigate these challenges by: • integrating and reconciling core molecular data (structure, name, annotated target) from multiple existing resources; • integrating and analyzing biological activity data for small molecules across multiple biological scales (binding, cellular activity, disease indication) (4); and • providing context for activities of small molecules and their targets informed by systems biology (complexes, pathways, processes). Our proposed SCB Knowledge Provider will enable answering questions such as: • what is the mechanism of action of a small molecule identified from a phenotypic screen? • which compounds are available to modulate my target of interest, and which other candidate targets might influence the activity of my target in a particular cellular context? • given my interest in a disease process or pathway, which candidate targets should I consider in relevant models, and which compounds are known to modulate those targets? We anticipate addressing gaps in data by scouting for inclusion additional sources of small-molecule bioactivity and contextual information about protein target function (see Data Milestones). We will implement existing and develop new methods to surface inconsistencies and prioritize returned results (see Methods Milestones). Finally, we propose an Advisory Committee to help prioritize content most relevant to researchers, overcome development obstacles, and keep abreast of emerging data and methods (see Outreach Milestones). We have core expertise needed to realize this SCB vision via 15+ years of experience with small-molecule bioactivity databases and portals (5-7), plus expertise in phenotypic screening, small-molecule profiling, and MoA studies (8-10), which demand integrated understanding of diverse data for hypothesis-driven science. We also made key contributions to Translator feasibility, including a workflow-centric view of data exploration by subject-matter experts, identifying and providing key datasets and methods for production of TIDBITS, and prototyping set-based (e.g., gene-list) analyses in workflows. These experiences ideally position us to realize a Systems Chemical Biology Knowledge Provider for Translator.

化学生物学和系统生物学在分子或细胞的不同尺度上运作。 组织，需要学科之间的翻译（例如，术语，现有证据， 新的发现），以允许每一个通知对方（1，2）。我们提出了一个生物医学数据翻译器 系统化学生物学的知识提供者，以满足这一需求。 系统化学生物学（SCB）的核心是发现新的治疗方法 重新利用机会，避免毒性和其他副作用， 小分子如何起作用（即，作用机制研究）。使用的许多证据类型 解决这些问题也可以用来理解功能的变化， 分子尺度，如翻译后修饰和蛋白质编码的遗传变异。 目前，该领域的研究人员面临着重大的数据访问和解释挑战。为 例如，为了支持作用机制（MoA）研究，许多实验方法产生 相关数据（基因表达，蛋白质-蛋白质相互作用，对纯 蛋白质和细胞等）对于大多数研究人员来说，它们数量太多，种类太多，无法查询。 许多基于网络的工具可以共享这些数据，但它们分散，并不总是容易找到， 并且通常彼此之间缺乏任何通信。随着该领域的成熟和日益 拥抱高通量实验，这些问题只会变得更糟（3）. 我们建议的SCB知识提供者将通过以下方式缓解这些挑战： ·整合和协调核心分子数据（结构、名称、注释的靶标）， 多种现有资源; ·整合和分析多个小分子的生物活性数据 生物尺度（结合、细胞活性、疾病适应症）（4）;以及 ·为小分子及其目标的活动提供背景信息 生物学（复合体、途径、过程）。 我们建议的SCB知识提供者将能够回答以下问题： ·从表型筛选中鉴定的小分子的作用机制是什么？ ·哪些化合物可用于调节我感兴趣的目标，以及哪些其他化合物 候选目标可能会影响我的目标在特定细胞环境中的活动？ ·考虑到我对疾病过程或途径的兴趣，我应该选择哪些候选靶点 在相关模型中考虑，已知哪些化合物可调节这些靶点？ 我们希望通过寻找其他小分子来源来解决数据缺口， 生物活性和关于蛋白质靶向功能的背景信息（参见数据 Milkman）。我们将实施现有的和开发新的方法来表面不一致 并对返回的结果进行优先级排序（请参阅方法里程碑）。最后，我们提出了一个建议。 委员会帮助优先考虑与研究人员最相关的内容，克服发展 障碍，并随时了解新出现的数据和方法（见外联里程碑）。 我们拥有通过15年以上的经验实现SCB愿景所需的核心专业知识， 小分子生物活性数据库和门户网站（5-7），加上表型筛选方面的专业知识， 小分子谱分析和MoA研究（8-10），这需要对 为假说驱动的科学提供多样化的数据。我们还为Translator做出了重要贡献 可行性，包括主题专家以工作流为中心的数据探索视图， 确定并提供关键数据集和制作TIDBITS的方法，并制作原型 基于集合的（例如，基因列表）分析。这些经验使我们能够 实现一个系统的化学生物学知识提供者的翻译。