Analysis of Protein-Protein Interaction Networks Based on Semi-Quantitative Proteomics

Protein-protein interaction networks are critical tools for elucidating biological functions and mechanisms. By studying the interactions between proteins, scientists gain insights into intracellular signaling pathways, metabolic routes, and the formation of protein complexes. By mapping these interactions, researchers can better understand how cellular functions are coordinated and how disruptions in these networks can lead to diseases. In recent years, advancements in proteomics technologies, particularly mass spectrometry-based semi-quantitative proteomics, have made protein-protein interaction network analysis a crucial approach in biological research.

 

Semi-quantitative proteomics is typically based on mass spectrometry, with the principle being to compare the relative abundance of proteins in different samples to identify changes under various physiological or pathological conditions. Common techniques include labeling methods (e.g., SILAC) and label-free quantification (e.g., LFQ). These approaches allow for the detection and relative quantification of thousands of proteins within a single experiment.

 

Construction of Protein-Protein Interaction Networks

PPI network analysis based on semi-quantitative proteomics typically involves the following steps:

 

1. Data Acquisition

Employ semi-quantitative proteomics technologies such as SILAC, iTRAQ, or TMT to measure relative protein expression levels. These techniques enable mass spectrometric analysis to compare protein expression differences across different samples, such as treatment versus control groups.

 

2. Data Processing

(1) Mass Spectrometry Data Pre-processing: Includes peak identification, calibration, and peak matching to ensure data quality.

(2) Protein Identification and Quantification: Utilize search algorithms (e.g., SEQUEST, Mascot) to match mass spectrometry data with protein databases for protein identification and quantitative analysis.

 

3. PPI Network Construction

(1) Protein Interaction Data Integration: Integrate known protein interaction information from PPI databases such as BioGRID or STRING.

(2) Prediction of New Interactions: Use algorithms (e.g., machine learning models) to predict possible new interactions among proteins that exhibit significant changes in their quantitative data.

 

4. Network Analysis

(1) Network Construction: Construct the network where nodes represent proteins and edges represent their interactions.

(2) Topological Analysis of the Network: Study structural features of the network, such as node degree, clustering coefficient, and path length.

(3) Functional Enrichment Analysis: Perform GO (Gene Ontology) or KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis to understand significant biological processes, molecular functions, or pathways in the network.

 

5. Interpretation and Validation of Results

(1) Formulate biological hypotheses based on the network analysis results.

(2) Experimentally validate these hypotheses, for instance, through co-immunoprecipitation (Co-IP) or yeast two-hybrid assays to verify predicted protein interactions.

 

Protein-protein interaction network analysis based on semi-quantitative proteomics has been widely applied in fields such as disease mechanism research, drug target discovery, and biomarker identification. As technology continues to advance, this field is expected to reveal increasingly complex biological networks and mechanisms.