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

项目摘要 蛋白质通过与其他蛋白质和生物实体相互作用而发挥作用，形成生物 细胞内和细胞间的通路。有针对性的治疗旨在缓解或逆转功能障碍 通过提供药物来恢复正常生物途径的活动，从而导致蛋白质突变。 在生物途径的背景下投射未被研究的蛋白质是推断潜力的一种有效方法 这些蛋白质的功能。 基于路径的方法现在被常规地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是必不可少的。在过去的两年中 几十年来，我们的团队一直在共同努力建立Reactome知识库，可以说是 最流行和最全面的开源生物途径数据库，覆盖超过一半的人类 编码蛋白质的基因，并在研究界广泛使用。 在这个应用中，我们建议开发一个Reactome IDG途径门户，它将允许本地化 对生物途径中研究不足的蛋白质进行研究，确定可能与更知名的蛋白质相互作用 在Reactome中注释的特定过程中，通过途径精确定位最有效的药物靶点 建模，从而生成这些蛋白质在关键区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，将未被研究的蛋白质放在上下文中 通过导入在IDG项目和其他项目中收集的各种数据类型 资源，然后通过利用现有的反应组将它们覆盖到反应组路径上 软件工具(例如交互覆盖)。此外，我们将开发一种机器学习方法来 预测未被充分研究的蛋白质与已知反应组间的功能相互作用 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 从机器学习的方法来模拟未被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和强大的方法来帮助社区 了解未被充分研究的蛋白质对细胞功能的贡献。

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.