AI-empowered perturbation proteomics for complex biological systems.

IF 11.1 Q1 CELL BIOLOGY
Liujia Qian, Rui Sun, Ruedi Aebersold, Peter Bühlmann, Chris Sander, Tiannan Guo
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引用次数: 0

Abstract

The insufficient availability of comprehensive protein-level perturbation data is impeding the widespread adoption of systems biology. In this perspective, we introduce the rationale, essentiality, and practicality of perturbation proteomics. Biological systems are perturbed with diverse biological, chemical, and/or physical factors, followed by proteomic measurements at various levels, including changes in protein expression and turnover, post-translational modifications, protein interactions, transport, and localization, along with phenotypic data. Computational models, employing traditional machine learning or deep learning, identify or predict perturbation responses, mechanisms of action, and protein functions, aiding in therapy selection, compound design, and efficient experiment design. We propose to outline a generic PMMP (perturbation, measurement, modeling to prediction) pipeline and build foundation models or other suitable mathematical models based on large-scale perturbation proteomic data. Finally, we contrast modeling between artificially and naturally perturbed systems and highlight the importance of perturbation proteomics for advancing our understanding and predictive modeling of biological systems.

针对复杂生物系统的人工智能扰动蛋白质组学。
全面的蛋白质水平扰动数据不足阻碍了系统生物学的广泛应用。在本视角中,我们将介绍扰动蛋白质组学的原理、重要性和实用性。生物系统会受到各种生物、化学和/或物理因素的扰动,然后在不同水平上进行蛋白质组学测量,包括蛋白质表达和周转、翻译后修饰、蛋白质相互作用、转运和定位的变化,以及表型数据。采用传统机器学习或深度学习的计算模型可识别或预测扰动反应、作用机制和蛋白质功能,从而有助于疗法选择、化合物设计和高效实验设计。我们建议概述一个通用的 PMMP(扰动、测量、建模到预测)管道,并基于大规模扰动蛋白质组数据建立基础模型或其他合适的数学模型。最后,我们对比了人工扰动系统和自然扰动系统的建模情况,并强调了扰动蛋白质组学对于促进我们对生物系统的理解和预测建模的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
7.10
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