A Translator Knowledge Provider for Systems Chemical Biology

化学生物学和系统生物学在不同的分子或细胞尺度上工作 组织，需要在各学科之间进行翻译(例如，术语、现有证据、 新发现)，以允许彼此通知对方(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。 最重要的是，系统化学生物学(SCB)的前景是发现新的治疗方法 和重新利用机会，避免毒性和其他副作用，并理解 小分子如何发挥作用(即，作用机制研究)。使用了多种证据类型 解决这类问题也可以用来理解类似的改装功能 分子尺度，如翻译后修饰和编码蛋白质的遗传变异。 目前，该领域的研究人员面临着重大的数据获取和解释挑战。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，对纯净的不同生物活性 蛋白质和细胞等)对于大多数研究人员来说，这些数据数量太多、种类太多，难以查询。 有许多基于网络的工具可以共享这些数据，但它们是分散的，并不总是很容易找到， 而且通常彼此之间缺乏任何沟通。随着该领域的成熟和日益 接受高通量实验，这些问题只会变得更糟(3)。 我们推荐的渣打银行知识提供商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、注明的目标) 多种现有资源； ·整合和分析多个小分子的生物活性数据 生物标度(结合、细胞活动、疾病指征)(4)； ·为系统通知的小分子及其目标的活动提供背景 生物学(复合体、途径、过程)。 我们推荐的渣打银行知识提供商将能够回答以下问题： ·从表型筛选中识别出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，还有哪些 候选目标可能会影响我的目标在特定细胞环境中的活动？ ·鉴于我对疾病过程或途径的兴趣，我应该选择哪些候选目标 在相关模型中考虑一下，已知哪些化合物可以调节这些靶标？ 我们期望通过寻找包含小分子的其他来源来填补数据中的空白 关于蛋白质靶标功能的生物活性和上下文信息(见数据 里程碑)。我们将实施现有的并开发新的方法来处理不一致之处 并对返回的结果进行优先级排序(请参阅方法里程碑)。最后，我们提出了一项建议 委员会帮助确定与研究人员最相关的内容的优先顺序，克服开发 在这方面，我们将继续努力克服各种障碍，并与新出现的数据和方法保持同步(见外联里程碑)。 通过15年以上的经验，我们拥有实现这一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个)，外加表型筛选方面的专业知识， 小分子图谱和MOA研究(8-10)，需要综合理解 假说驱动的科学的不同数据。我们也为翻译者做出了重要贡献 可行性，包括主题专家对数据探索的以工作流为中心的观点， 确定和提供用于生产花絮和原型的关键数据集和方法 工作流程中基于集合(例如，基因列表)的分析。这些经历使我们处于理想的位置 实现一个面向翻译人员的系统化学生物学知识提供者。

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.