Protein immunoaffinity purification coupled with mass spectrometry (IP-MS) can identify numerous potential target protein-interacting proteins. In subsequent studies, it is usually necessary to select a few key interacting proteins from the identified ones for further validation and functional mechanism exploration.

When IP-MS identifies a small number of interacting proteins, one can individually search the relevant literature and functional descriptions of each interacting protein in databases such as PubMed, NCBI, and UniProt. However, if IP-MS identifies a large number of interacting proteins, or the information obtained from the literature and database searches is either too abundant or too scarce, making selections becomes challenging. In such cases, the following strategies can be considered to focus on the selection of key proteins for further research.

Selecting key interacting proteins based on quantitative IP-MS data

When selecting key interacting proteins based on quantitative IP-MS data, researchers typically rely on two main indicators: fold change and intensity.

Fold change represents the magnitude of the protein level difference between the experimental and control groups, while intensity reflects the signal strength of the detected proteins in the IP sample, which can serve as a proxy for protein abundance.

Typically, proteins that exhibit relatively large fold changes and high signal intensities in the experimental group are prioritized for further investigation. These proteins are considered to play important roles in the biological process being studied and are thus selected as potential research targets.

It is important to note that in IP-MS experiments, the fold changes and signal intensities of the interacting proteins usually do not exceed those of the bait protein. If a protein's fold change and signal intensity exceed those of the bait protein, caution is required, as it may indicate non-specific binding or contamination, rather than a genuine interaction. Therefore, in the analysis of IP-MS data, careful consideration and validation are necessary to distinguish true interacting proteins from potential false positives or contaminants.

Selecting key interacting proteins based on protein-protein interaction networks

Constructing and visualizing protein-protein interaction networks using the STRING database and Cytoscape software is a common practice in proteomics research.

First, install the Cytoscape software and the StringApp plugin. The StringApp can conveniently retrieve interaction data from the STRING database.

In Cytoscape, enter the gene names or UniProt IDs of the target proteins, select the "Physical network" and a confidence threshold of 0.4, and a maximum of 100 external interacting proteins to obtain a protein-protein interaction network based on the STRING database.

Then, based on the IP-MS experiment results, import the bait protein and its interacting proteins into Cytoscape to generate an interaction network based on the quantitative IP-MS data.

Finally, use the merge function in Cytoscape to integrate the interaction networks based on the STRING database and the quantitative IP-MS data. Adjust the visual attributes of the network nodes, such as color and size, to reflect the source of the interacting proteins, the IP-MS fold changes, and p-values, creating a professional visualization of the interaction network.

The protein-protein interaction network not only displays the IP-MS results but can also be used to select key interacting proteins for further research. The most central nodes in the network are often considered to have more important functional roles. You can use the Cytohubba plugin in Cytoscape to calculate node importance scores based on different algorithms and select the highest-scoring key node proteins for further investigation.

Selecting key interacting proteins based on functional annotation and enrichment analysis

Performing functional annotation and enrichment analysis on the IP-MS-identified interacting proteins is another effective strategy for selecting key proteins.

You can use tools like Metascape, Panther, or the built-in annotation enrichment analysis function in the STRING database to identify significantly enriched signaling pathways and functional categories among the interacting proteins.

Based on this, you can select proteins belonging to the most interesting functional categories or those with the most differential expression in each enriched category for further study.

Furthermore, some interacting proteins identified in IP-MS are usually not chosen as the focus of subsequent research. These proteins are often highly abundant cellular proteins, such as ribosomal proteins (RPS family), cytoskeletal proteins (e.g., Actin), and keratins (Keratin family), which may appear in IP-MS results due to their involvement in translation processes, widespread presence in cells, or as accidental contaminants during the experimental procedures.

In summary

In conclusion, after obtaining the list of IP-MS-identified interacting proteins, the following strategies can be considered to select key proteins for further exploration and validation:

1. Search the literature and functional descriptions of each interacting protein in databases such as PubMed and UniProt.

2. Select proteins with relatively large fold changes and high signal intensities in the experimental group.

3. Use Cytoscape plugins like Cytohubba to extract key subnetworks from the protein-protein interaction network and select important node proteins.

4. Based on functional annotation and enrichment analysis, select proteins belonging to the most interesting functional categories or those with the most differential expression.

5. For highly abundant common cellular proteins, such as ribosomal proteins, cytoskeletal proteins, and keratins, exercise caution in selection to avoid potential false-positive results.

By comprehensively applying these strategies, you can effectively select key proteins from the large number of IP-MS-identified interacting proteins for further in-depth research.