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功能，以形成生物活性。 细胞内部和细胞之间的通路。靶向治疗旨在缓解或逆转功能障碍 通过提供药物来恢复正常的生物途径的活动。 在生物学途径的背景下预测未充分研究的蛋白质是推断潜在的 这些蛋白质的功能。 基于通路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至关重要。在过去两 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， 最受欢迎和最全面的开源生物途径数据库，涵盖了人类的一半以上。 蛋白质编码基因，并广泛应用于研究界。 在本申请中，我们建议开发一个Reactome IDG途径门户，该门户将允许本地化 研究生物学途径中未充分研究的蛋白质，确定与已知蛋白质的可能相互作用 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，从而产生这些蛋白质在关键结构域的分子功能的可测试的预测， 生物学特别是，我们将开发一个基于Web的应用程序，将未充分研究的蛋白质放在上下文中 通过导入IDG项目和其他项目中收集的各种数据类型， 资源，然后通过利用现有的Reactome将它们覆盖到Reactome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， 预测未充分研究的蛋白质和已知的Reactome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 从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个独特而强大的方法，以帮助社区， 了解未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.