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Updated: May 2, 2026
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
Published on: October 19, 2021
Prioritizing protein complexes implicated in human diseases by network optimization
Background:
The detection of associations between protein complexes and human inherited diseases is of great importance in understanding mechanisms of diseases. Dysfunctions of a protein complex are usually defined by its member disturbance and consequently result in certain diseases. Although individual disease proteins have been widely predicted, computational methods are still absent for systematically investigating disease-related protein complexes.
Results:
We propose a method, MAXCOM, for the prioritization of candidate protein complexes. MAXCOM performs a maximum information flow algorithm to optimize relationships between a query disease and candidate protein complexes through a heterogeneous network that is constructed by combining protein-protein interactions and disease phenotypic similarities. Cross-validation experiments on 539 protein complexes show that MAXCOM can rank 382 (70.87%) protein complexes at the top against protein complexes constructed at random. Permutation experiments further confirm that MAXCOM is robust to the network structure and parameters involved. We further analyze protein complexes ranked among top ten for breast cancer and demonstrate that the SWI/SNF complex is potentially associated with breast cancer.
Conclusions:
MAXCOM is an effective method for the discovery of disease-related protein complexes based on network optimization. The high performance and robustness of this approach can facilitate not only pathologic studies of diseases, but also the design of drugs targeting on multiple proteins.
Insights
We developed MAXCOM, a novel computational method to identify disease-related protein complexes. MAXCOM effectively prioritizes candidate complexes by optimizing network relationships, aiding disease mechanism and drug discovery.
Area of Science:
- Computational Biology
- Systems Biology
- Genomics
Background:
- Understanding protein complex roles in inherited diseases is crucial for disease mechanism elucidation.
- Protein complex dysfunctions, stemming from member disturbances, are linked to various diseases.
- Existing computational methods primarily focus on individual disease proteins, lacking systematic approaches for protein complexes.
Purpose of the Study:
- To introduce MAXCOM, a computational method for prioritizing candidate disease-related protein complexes.
- To systematically investigate associations between protein complexes and human inherited diseases using network optimization.
Main Methods:
- MAXCOM utilizes a maximum information flow algorithm within a heterogeneous network.
- The network integrates protein-protein interactions and disease phenotypic similarities.
- Candidate protein complexes are prioritized based on their optimized relationships with query diseases.
Main Results:
- Cross-validation on 539 protein complexes showed MAXCOM ranked 70.87% correctly compared to random.
- Permutation experiments confirmed MAXCOM's robustness to network structure and parameters.
- Analysis of top-ranked complexes for breast cancer suggested a potential association with the SWI/SNF complex.
Conclusions:
- MAXCOM is an effective network optimization method for discovering disease-related protein complexes.
- The approach demonstrates high performance and robustness, facilitating disease pathology studies.
- MAXCOM can aid in designing drugs that target multiple proteins involved in disease.
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