4.7 Review

Discovery-Versus Hypothesis-Driven Detection of Protein-Protein Interactions and Complexes

期刊

出版社

MDPI
DOI: 10.3390/ijms22094450

关键词

protein complexes; protein-protein interactions; interactomics; mass-spectrometry; targeted proteomics; data analysis; databases; systems biology

资金

  1. Swiss National Science Foundation [P400PB_191046]
  2. Swiss National Science Foundation (SNF) [P400PB_191046] Funding Source: Swiss National Science Foundation (SNF)

向作者/读者索取更多资源

This article summarizes the advantages and limitations of mass spectrometry-based interactome screening methods, with a focus on the applicability of hypothesis-driven and discovery-driven data analysis concepts in large-scale protein-protein interaction studies. Hypothesis-driven complex- or network-centric analysis approaches are highlighted as promising strategies for comparative research.
Protein complexes are the main functional modules in the cell that coordinate and perform the vast majority of molecular functions. The main approaches to identify and quantify the interactome to date are based on mass spectrometry (MS). Here I summarize the benefits and limitations of different MS-based interactome screens, with a focus on untargeted interactome acquisition, such as co-fractionation MS. Specific emphasis is given to the discussion of discovery- versus hypothesis-driven data analysis concepts and their applicability to large, proteome-wide interactome screens. Hypothesis-driven analysis approaches, i.e., complex- or network-centric, are highlighted as promising strategies for comparative studies. While these approaches require prior information from public databases, also reviewed herein, the available wealth of interactomic data continuously increases, thereby providing more exhaustive information for future studies. Finally, guidance on the selection of interactome acquisition and analysis methods is provided to aid the reader in the design of protein-protein interaction studies.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据