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以分子或细胞的不同尺度运行 组织，需要学科之间的翻译（例如术语，现有证据， 新发现）允许每个发现通知另一个（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商可以满足这一需求。 中心，系统化学生物学的承诺（SCB）正在发现新的疗法 并重新设置机会，避免毒性和其他副作用以及理解 小分子的功能如何（即行动机理研究）。许多证据类型 解决此类问题也可以用于了解类似的更改功能 分子尺度，例如翻译后修饰和蛋白质编码遗传变异。 目前，该领域的研究人员面临着主要的数据访问和解释挑战。为了 例如，为了支持行动机理（MOA）研究，许多实验方法生产 相关数据（基因表达，蛋白质 - 蛋白质相互作用，对纯的生物活性 蛋白质和细胞等）太多且异质，无法查询大多数研究人员。 存在许多基于Web的工具来共享此类数据，但是它们分散了，并不总是很容易找到， 而且通常缺乏彼此之间的任何沟通。随着场的成熟和越来越多的 拥抱高通量实验，这些问题只会变得更糟（3）。 我们提出的SCB知识提供者将通过以下方式缓解这些挑战。 •从 多个现有资源； •整合和分析多个小分子的生物学活性数据 生物量表（结合，细胞活性，疾病指示​​）（4）；和 •提供小分子活动及其目标的背景 生物学（复合物，途径，过程）。 我们拟议的SCB知识提供者将启用回答以下问题： •从表型筛选中鉴定出的小分子的作用机理是什么？ •哪些化合物可用于调节我感兴趣的目标，以及哪些其他 候选目标可能会在特定的细胞环境中影响我的目标的活动吗？ •鉴于我对疾病过程或途径的兴趣，候选目标我应该 在相关模型中考虑，已知哪些化合物可以调节这些目标？ 我们预计通过搜索包含其他小分子来源来解决数据中的差距 有关蛋白质目标功能的生物活性和上下文信息（请参阅数据 里程碑）。我们将实施现有的现有方法，以表现出不一致之处 并确定返回的结果（请参阅方法里程碑）。最后，我们提出了一个咨询 委员会有助于确定与研究人员最相关的内容的优先级，克服发展 障碍，并与新兴的数据和方法保持一致（请参阅外展里程碑）。 我们拥有需要通过15年以上的经验来实现SCB愿景的核心专业知识 小分子生物活性数据库和门户（5-7），以及表型筛查方面的专业知识， 小分子分析和MOA研究（8-10），要求对 假设驱动的科学的多种数据。我们还为翻译者做出了关键的贡献 可行性，包括以工作流程为中心的数据探索的观点 识别并提供用于生产花絮和原型制作的关键数据集和方法 基于集合（例如，基因列表）在工作流程中分析。这些经验理想地将我们定位为 实现一个化学生物学知识提供者的转换器。