How to Build and Analyze Protein-Protein Interaction Networks?
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Identification of differentially expressed proteins derived from proteomics platforms such as TMT or DIA
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Standardization of protein identifiers (e.g., UniProt IDs or gene symbols)
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Construction of a preliminary PPI network using the STRING database
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Visualization and module detection using Cytoscape
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Integration of pathway enrichment analyses to uncover potential regulatory mechanisms
Within cells, protein-protein interactions (PPIs) function as central hubs that coordinate diverse biological processes. Proteins rarely act in isolation; instead, through binding, assembly, and regulatory interactions with other proteins, they form highly complex biological networks. A protein-protein interaction network represents these molecular relationships in a systematic and graphical manner, in which nodes correspond to proteins and edges denote their interactions. Such networks not only facilitate the identification of functional protein modules but also provide a systems-level framework for disease research, signaling pathway reconstruction, and therapeutic target discovery.
Mainstream Strategies for PPI Network Construction: Focusing on Protein-Protein Interactions
The construction of a high-quality protein-protein interaction network fundamentally aims to accurately capture authentic interaction relationships among proteins. Currently, the following approaches represent the most widely adopted strategies:
1. Experimental Approaches for PPI Data Acquisition
(1) Yeast two-hybrid (Y2H): This method enables large-scale screening of pairwise protein-protein interactions and is particularly effective for identifying previously unknown binding partners.
(2) Affinity purification-mass spectrometry (AP-MS): By immuno-enriching a target protein and subsequently identifying co-purified interacting partners via mass spectrometry, AP-MS reflects protein interactions under near-physiological conditions.
(3) Cross-linking mass spectrometry (XL-MS): This technique employs chemical cross-linkers to covalently link interacting proteins, followed by mass spectrometric identification of cross-linked peptides.
2. Database-Driven Integration for PPI Network Construction
When proteomics datasets or differential expression results are available, curated databases can be leveraged, including:
(1) STRING: Integrates multiple evidence sources-such as experimental data, computational predictions, and text mining-and assigns confidence scores to interactions.
(2) BioGRID and IntAct: These databases primarily focus on high-confidence experimentally validated interactions and are well suited for constructing accurate PPI networks.
Strategies for Protein-Protein Interaction Network Analysis
1. Network Topology Analysis
(1) Node degree: Proteins with high degree values are typically considered hub proteins within PPI networks, such as TP53 and AKT1, which serve as critical regulatory factors in cancer.
(2) Centrality metrics: Measures including betweenness centrality and closeness centrality are used to identify proteins that function as bridges connecting multiple signaling pathways.
(3) Network density and clustering coefficient: These parameters characterize protein clustering behavior and indicate the presence of functional modules.
2. Network Module Identification
Plugins such as MCODE or ClusterONE within Cytoscape enable the identification of tightly connected protein clusters. These modules often correspond to cooperative protein complexes or groups of proteins participating in the same biological pathway.
3. Functional Enrichment Analysis
Gene Ontology (GO) and KEGG pathway analyses performed on proteins within individual modules facilitate the interpretation of biological processes and disease mechanisms associated with specific PPI network modules.
Representative Applications of Protein-Protein Interaction Networks in Research
1. Elucidation of Disease Mechanisms
In cancer research, for example, constructing PPI networks from tumor tissues enables the identification of key nodes involved in regulating the tumor microenvironment, cell proliferation, and immune evasion, thereby providing insights into molecular disease mechanisms.
2. Novel Target Identification and Drug Repositioning
By mapping known drug targets onto PPI networks, analysis of neighboring proteins and interaction paths can reveal potential indirect targets or rational combination therapy strategies.
3. Biomarker Discovery
Not all differentially expressed proteins are biologically meaningful. Proteins that are embedded within specific PPI modules are more likely to play functional roles. Integrating PPI analysis can therefore improve the accuracy of biomarker identification.
Practical Workflow for Constructing PPI Networks from Proteomics Data
In both academic research and industrial R&D settings, a typical workflow includes:
In complex biological systems, the functional contribution of an individual protein is often limited, whereas its interactions with other proteins form the fundamental basis of regulatory processes. The construction of high-quality protein-protein interaction networks therefore represents not only a critical step in proteomics data mining but also a core component in advancing systems biology research. For researchers seeking to further interrogate regulatory mechanisms, identify key network nodes, or prioritize potential therapeutic targets from proteomics datasets, professional technical support can be highly beneficial. MtoZ Biolabs offers well-established experimental platforms and an experienced bioinformatics team, with a focus on transforming complex protein interaction networks into biologically interpretable and analytically tractable models.
MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider.
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