AI-empowered perturbation proteomics for complex biological systems.

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 desi

SUBMITTER: Qian L 

PROVIDER: S-EPMC11605689 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature