How to Build and Analyze Protein-Protein Interaction Networks?
In cells, protein-protein interactions (PPI) constitute the communication hub of biological processes. Proteins often do not function in isolation, but rather through binding, aggregation, and regulation with other proteins, forming complex biological networks. Protein-protein interaction networks express these molecular relationships in a systematic map: each node represents a protein, and interactions are connected through edges. This network can not only reveal protein functional modules but also provide systematic support for disease research, signal pathway reconstruction, and target screening.
I. Mainstream strategies for constructing PPI networks: Starting from protein-protein interactions
Constructing high-quality protein-protein interaction networks is essentially about accurately capturing the true interaction relationships between proteins. The following methods are the current mainstream construction approaches:
1. Experimental methods for obtaining PPI data
(1) Yeast two-hybrid (Y2H): Can be used to screen large-scale one-to-one protein-protein interactions, especially suitable for discovering unknown binding relationships.
(2) Affinity purification-mass spectrometry (AP-MS): By immuno-enriching the target protein and then using mass spectrometry to detect interacting proteins that co-purify with it, it can reflect interactions in a near-native state.
(3) Cross-linking mass spectrometry (XL-MS): Uses chemical cross-linkers to covalently connect interacting proteins, followed by mass spectrometry identification.
2. Database integration for constructing PPI networks
When existing proteomics or differential expression data is available, databases such as:
(1) STRING: Integrates multiple data sources (experimental, predictive, text mining) and scores interaction confidence.
(2) BioGRID, IntAct: Focuses on high-confidence experimental data sources, suitable for constructing precise networks.
II. Analysis strategies for protein-protein interaction networks
1. Network topology analysis
(1) Node degree: Proteins with high node degrees in the protein-protein interaction network are usually 'hub proteins', such as TP53, AKT1, which are critical regulators in cancer.
(2) Centrality measures: Such as betweenness centrality and closeness centrality, identifying proteins that act as 'bridges' between multiple pathways.
(3) Network density and clustering coefficient: Reflects the clustering characteristics among proteins, indicating the existence of functional modules.
2. Network module extraction
Using Cytoscape's MCODE or ClusterONE plugins, one can identify protein clusters that are closely related in function. These modules typically represent protein complexes working together or members involved in the same biological pathway.
3. Biological function enrichment analysis
Performing GO, KEGG analysis on proteins within each module can help understand the biological processes and disease mechanisms behind specific protein-protein interaction network modules.
III. Typical applications of protein-protein interaction networks in research
1. Disease mechanism analysis
Taking cancer research as an example, constructing protein-protein interaction networks in cancer tissues can identify key nodes regulating the tumor microenvironment, cell proliferation, and immune evasion, aiding in the elucidation of molecular mechanisms of diseases.
2. New target and drug repositioning
Mapping existing drug targets to the PPI network can predict potential indirect action sites or combination drug strategies by analyzing adjacent proteins and pathways.
3. Biomarker screening
Not all differentially expressed proteins have biological significance; if they appear in specific protein-protein interaction modules, their potential function is more noteworthy. Combining PPI analysis can improve the accuracy of biomarker screening.
IV. Practical workflow for constructing PPI networks from proteomics data
In actual research or corporate R&D, common workflows are as follows:
- Obtain differentially expressed proteins (from proteomics, such as TMT, DIA)
- Standardize protein ID information (unify to UniProt or Gene Symbol)
- Use STRING database to construct basic PPI network
- Import into Cytoscape software for visualization and module identification
- Integrate pathway enrichment analysis to explore potential mechanisms
In complex biological systems, the function of a single protein is limited, and its interaction with other proteins is the essence of regulation. Constructing high-quality protein-protein interaction networks is not only a key step in proteomics data mining but also an important support for achieving systems biology goals. If you wish to further explore regulatory mechanisms, find key nodes, or screen potential targets from proteomics data, feel free to contact us. Biotech PacBio has a mature technical platform and bioinformatics team dedicated to transforming complex protein interaction networks into clear and interpretable biological models.
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