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Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
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Updated: Jun 8, 2025

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

AI-empowered perturbation proteomics for complex biological systems.

Liujia Qian¹, Rui Sun¹, Ruedi Aebersold²

¹School of Medicine, Westlake University, Hangzhou, Zhejiang Province, China; Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; Research Center for Industries of the Future, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China.

|November 2, 2024

Summary

This summary is machine-generated.

Perturbation proteomics offers a comprehensive approach to understanding biological systems by measuring protein changes after perturbations. This method, combined with computational modeling, enhances predictive capabilities for systems biology applications.

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Area of Science:

Proteomics
Systems Biology
Computational Biology

Background:

Limited protein-level perturbation data hinders systems biology adoption.
Perturbation proteomics addresses this gap by integrating diverse measurements.

Purpose of the Study:

Introduce the rationale, essentiality, and practicality of perturbation proteomics.
Propose a generic perturbation, measurement, modeling to prediction (PMMP) pipeline.
Highlight the role of perturbation proteomics in advancing biological understanding and predictive modeling.

Main Methods:

Perturbing biological systems with various factors (biological, chemical, physical).
Measuring proteomic changes (expression, turnover, PTMs, interactions, localization) and phenotypic data.
Applying computational models (machine learning, deep learning) for response prediction and function identification.

Main Results:

Development of a generic PMMP pipeline.
Potential for building foundation models from large-scale perturbation proteomic data.
Distinction between modeling for artificially and naturally perturbed systems.

Conclusions:

Perturbation proteomics is crucial for advancing systems biology.
Integrated data and modeling improve prediction of biological responses and functions.
This approach aids in therapy selection, compound design, and experiment optimization.