Reactome IDG portal: Pathway-based analysis and visualization of understudied human proteins

Reactome IDG 门户：对正在研究的人类蛋白质进行基于通路的分析和可视化

• 批准号: 10348828
• 负责人: PETER G DEUSTACHIO
• 金额: $ 40万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10348828
• 关键词: Animal Model; Bioinformatics; Biological; Biological Process; Biology; Cell physiology; Cells; Code; Communities; Computational Biology; Data; Data Analyses; Databases; Disease; Drug Interactions; Drug Targeting; Fuzzy Logic; Genes; Grant; Human; Infectious Agent; Level of Evidence; Machine Learning; Modeling; Molecular; Mutation; Network-based; Online Systems; Pathway interactions; Pharmaceutical Preparations; Play; Probability; Process; Proteins; Reaction; Research; Research Personnel; Resources; Role; Software Tools; Synaptic Transmission; Technology; Tissue-Specific Gene Expression; Validation; Visualization; Zebrafish; base; data visualization; knowledge base; open source; portability; precision medicine; protein function; protein protein interaction; side effect; simulation; targeted treatment; therapy design; web app

Project Summary Proteins function through interactions with other proteins and biological entities to form biological pathways inside and between cells. Targeted therapies are designed to mitigate or reverse malfunctions caused by mutations in proteins via providing drugs to recover normal biological pathways’ activities. Projecting understudied proteins in the context of biological pathways is a powerful way to infer potential functions of these proteins. Pathway-based approaches are now routinely applied in bioinformatics and computational biology data analysis and visualization. Pathway databases are essential for those approaches. During the past two decades, our team has been working together on building the Reactome knowledgebase, arguably the most popular and comprehensive open source biological pathway database, covering over half of human protein-coding genes and widely used in the research community. In this application, we propose to develop a Reactome IDG pathway portal, which will allow localization of understudied proteins in biological pathways, identifying likely interactions with better-known proteins in specific processes annotated in Reactome, pinpointing most effective drug targets via pathway modeling, thus generating testable predictions of molecular functions of these proteins in key domains of biology. Specially we will develop a web-based application to place understudied proteins in the context of Reactome pathways by importing a variety of data types collected in the IDG projects and other resources and then overlaying them onto the Reactome pathways by leveraging existing Reactome software tools (e.g. interaction overlay). Furthermore, we will develop a machine learning approach to predict functional interactions between understudied proteins and well-known Reactome annotated proteins and a Boolean network-based fuzzy logic modeling approach to integrate the scores produced from the machine learning approach to simulate the impacts of understudied proteins on pathways’ activities. We believe our approach will provide a unique and powerful approach to help the community to understand the contribution of the understudied proteins to cellular functions.

项目概要 蛋白质通过与其他蛋白质和生物实体相互作用形成生物体来发挥作用 细胞内部和细胞之间的通路。靶向治疗旨在减轻或逆转功能障碍 通过提供药物恢复正常生物途径的活性而由蛋白质突变引起。 在生物途径的背景下预测尚未研究的蛋白质是推断潜力的有效方法 这些蛋白质的功能。 基于通路的方法现在通常应用于生物信息学和计算生物学数据 分析和可视化。路径数据库对于这些方法至关重要。过去两年期间 几十年来，我们的团队一直致力于构建 Reactome 知识库，可以说是 最流行、最全面的开源生物通路数据库，覆盖超过一半的人类 蛋白质编码基因并广泛应用于研究界。 在此应用中，我们建议开发一个 Reactome IDG 路径门户，这将允许本地化 生物途径中未被研究的蛋白质，识别与更知名的蛋白质可能的相互作用 在 Reactome 注释的特定过程中，通过途径查明最有效的药物靶标 建模，从而生成这些蛋白质在关键领域的分子功能的可测试预测 生物学。特别是，我们将开发一个基于网络的应用程序，将未充分研究的蛋白质置于上下文中 通过导入 IDG 项目和其他项目中收集的各种数据类型来分析 Reactome 路径 资源，然后利用现有的 Reactome 将它们覆盖到 Reactome 路径上 软件工具（例如交互叠加）。此外，我们将开发一种机器学习方法 预测正在研究的蛋白质和众所周知的带注释的反应组之间的功能相互作用 蛋白质和基于布尔网络的模糊逻辑建模方法来整合产生的分数 通过机器学习方法来模拟未充分研究的蛋白质对通路的影响 活动。 我们相信我们的方法将提供一种独特而强大的方法来帮助社区 了解正在研究的蛋白质对细胞功能的贡献。