Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Dec 13;13(1):22103.
doi: 10.1038/s41598-023-49663-4.

A protein-protein interaction analysis tool for targeted cross-linking mass spectrometry

Jongham Park #  1 , Ahrum Son #  2 , Hyunsoo Kim  3   4   5
Affiliations

A protein-protein interaction analysis tool for targeted cross-linking mass spectrometry

Jongham Park et al. Sci Rep. .

Abstract

Protein networking is critical to understanding the biological functions of proteins and the underlying mechanisms of disease. However, identifying physical protein-protein interactions (PPIs) can be challenging. To gain insights into target proteins that interact with a particular disease, we need to profile all the proteins involved in the disease beforehand. Although the cross-linking mass spectrometry (XL-MS) method is a representative approach to identify physical interactions between proteins, calculating theoretical mass values for application to targeted mass spectrometry can be difficult. To address this challenge, our research team developed PPIAT, a web application that integrates information on reviewed human proteins, protein-protein interactions, cross-linkers, enzymes, and modifications. PPIAT leverages publicly accessible databases such as STRING to identify interactomes associated with target proteins. Moreover, it autonomously computes the theoretical mass value, accounting for all potential cross-linking scenarios pertinent to the application of XL-MS in SRM analysis. The outputs generated by PPIAT can be concisely represented in terms of protein interaction probabilities, complemented by findings from alternative analytical tools like Prego. These comprehensive summaries enable researchers to customize the results according to specific experimental conditions. All functions of PPIAT are available for free on the web application, making it a valuable tool for researchers studying protein-protein interactions.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no conflicts of interest. The Figures included in this manuscript were created using the Biorender software.

Figures

Figure 1
Figure 1
Workflow of PPIAT. The figure illustrates the flow of PPIAT, with input Information for searching protein–protein interaction (PPIs) at the front-end. Data is queried from the database based on the input information, and PPIs and mass values are calculated according to the input conditions. All calculated data is presented at the front-end, and the results can be exported in CSV format. The exported data can used as input for MS/MS analysis.
Figure 2
Figure 2
PPIAT search page. This search page of PPIAT requires users to input specific information for searching protein–protein interactions (PPIs) and their mass values. Required information includes the entry number of the target protein, the enzyme used for protein digestion, the cross-linker utilized for crosslinking, the range of peptide length, the ranking of protein–protein interactions, the charges of the peptide and ion, and any predicted protein modifications.
Figure 3
Figure 3
Result page of PPIAT, which consists of two main sections. The first section identifies the protein–protein interactions (PPIs) and predicts the interacting proteins for the target protein, based on a combined score from the STRING database. The second section calculates the mass values for the PPIs, taking into consideration the conditions for cross-linking mass spectrometry (XL-MS).
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Stelzl U, et al. A human protein-protein interaction network: A resource for annotating the proteome. Cell. 2005;122(6):957–968. doi: 10.1016/j.cell.2005.08.029. - DOI - PubMed
    1. Berg T. Modulation of protein-protein interactions with small organic molecules. Angew. Chem.-Int. Ed. 2003;42(22):2462. doi: 10.1002/anie.200200558. - DOI - PubMed
    1. Richards AL, et al. Mass spectrometry-based protein–protein interaction networks for the study of human diseases. Mol. Syst. Biol. 2021;17(1):e8792. doi: 10.15252/msb.20188792. - DOI - PMC - PubMed
    1. Ewing RM, et al. Large-scale mapping of human protein–protein interactions by mass spectrometry. Mol. Syst. Biol. 2007;3:89. doi: 10.1038/msb4100134. - DOI - PMC - PubMed
    1. Ryan DPMJM. Protein–protein interactions in human disease. Curr. Opin. Struct. Biol. 2005;15(4):441. doi: 10.1016/j.sbi.2005.06.001. - DOI - PubMed

MeSH terms

-