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利用Reactome通路解析暗蛋白

Illuminating Dark Proteins using Reactome Pathways.

作者信息

Brunson Timothy, Sanati Nasim, Matthews Lisa, Haw Robin, Beavers Deidre, Shorser Solomon, Sevilla Cristoffer, Viteri Guilherme, Conley Patrick, Rothfels Karen, Hermjakob Henning, Stein Lincoln, D'Eustachio Peter, Wu Guanming

机构信息

Oregon Health & Science University, Portland, OR 97239, USA.

NYU Langone Health, New York, NY 10016, USA.

出版信息

bioRxiv. 2023 Jun 5:2023.06.05.543335. doi: 10.1101/2023.06.05.543335.

Abstract

Limited knowledge about a substantial portion of protein coding genes, known as "dark" proteins, hinders our understanding of their functions and potential therapeutic applications. To address this, we leveraged Reactome, the most comprehensive, open source, open-access pathway knowledgebase, to contextualize dark proteins within biological pathways. By integrating multiple resources and employing a random forest classifier trained on 106 protein/gene pairwise features, we predicted functional interactions between dark proteins and Reactome-annotated proteins. We then developed three scores to measure the interactions between dark proteins and Reactome pathways, utilizing enrichment analysis and fuzzy logic simulations. Correlation analysis of these scores with an independent single-cell RNA sequencing dataset provided supporting evidence for this approach. Furthermore, systematic natural language processing (NLP) analysis of over 22 million PubMed abstracts and manual checking of the literature associated with 20 randomly selected dark proteins reinforced the predicted interactions between proteins and pathways. To enhance the visualization and exploration of dark proteins within Reactome pathways, we developed the Reactome IDG portal, deployed at https://idg.reactome.org, a web application featuring tissue-specific protein and gene expression overlay, as well as drug interactions. Our integrated computational approach, together with the user-friendly web platform, offers a valuable resource for uncovering potential biological functions and therapeutic implications of dark proteins.

摘要

对于相当一部分被称为“暗”蛋白的蛋白质编码基因,我们了解有限,这阻碍了我们对其功能及潜在治疗应用的理解。为解决这一问题,我们利用了Reactome,这是最全面的开源、开放获取的通路知识库,以便将暗蛋白置于生物通路中进行背景分析。通过整合多种资源,并采用基于106个蛋白质/基因成对特征训练的随机森林分类器,我们预测了暗蛋白与Reactome注释蛋白之间的功能相互作用。然后,我们利用富集分析和模糊逻辑模拟,开发了三个分数来衡量暗蛋白与Reactome通路之间的相互作用。这些分数与一个独立的单细胞RNA测序数据集的相关性分析为该方法提供了支持证据。此外,对超过2200万篇PubMed摘要进行系统的自然语言处理(NLP)分析,并对与20个随机选择的暗蛋白相关的文献进行人工核查,进一步证实了蛋白质与通路之间的预测相互作用。为了增强在Reactome通路中对暗蛋白的可视化和探索,我们开发了Reactome IDG门户,部署在https://idg.reactome.org,这是一个网络应用程序,具有组织特异性蛋白质和基因表达叠加以及药物相互作用等功能。我们的综合计算方法,连同用户友好的网络平台,为揭示暗蛋白的潜在生物学功能和治疗意义提供了宝贵资源。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/465d/10274615/ba5c74a3d2f8/nihpp-2023.06.05.543335v1-f0001.jpg

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