Mapping the Multiscale Proteomic Organization of Cellular and Disease Phenotypes.

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Annual Review of Biomedical Data Science Pub Date : 2024-08-01 Epub Date: 2024-07-24 DOI:10.1146/annurev-biodatasci-102423-113534
Anthony Cesnik, Leah V Schaffer, Ishan Gaur, Mayank Jain, Trey Ideker, Emma Lundberg
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Abstract

While the primary sequences of human proteins have been cataloged for over a decade, determining how these are organized into a dynamic collection of multiprotein assemblies, with structures and functions spanning biological scales, is an ongoing venture. Systematic and data-driven analyses of these higher-order structures are emerging, facilitating the discovery and understanding of cellular phenotypes. At present, knowledge of protein localization and function has been primarily derived from manual annotation and curation in resources such as the Gene Ontology, which are biased toward richly annotated genes in the literature. Here, we envision a future powered by data-driven mapping of protein assemblies. These maps can capture and decode cellular functions through the integration of protein expression, localization, and interaction data across length scales and timescales. In this review, we focus on progress toward constructing integrated cell maps that accelerate the life sciences and translational research.

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绘制细胞和疾病表型的多尺度蛋白质组组织图。
虽然人类蛋白质的主要序列已经编目十多年,但确定这些蛋白质是如何组织成一个动态的多蛋白集合体,其结构和功能跨越生物尺度,仍是一项持续的工作。对这些高阶结构的系统化和数据驱动分析正在兴起,有助于发现和理解细胞表型。目前,有关蛋白质定位和功能的知识主要来自人工注释和基因本体等资源的整理,这些资源偏重于文献中注释丰富的基因。在这里,我们设想了一个由数据驱动的蛋白质组装图谱驱动的未来。通过整合跨长度尺度和时间尺度的蛋白质表达、定位和相互作用数据,这些图谱可以捕捉和解码细胞功能。在这篇综述中,我们将重点介绍构建集成细胞图谱的进展,以加速生命科学和转化研究的发展。
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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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