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Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Protein Networks

Protein Networks

Protein-protein Interfaces

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...

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Super.Complex: A supervised machine learning pipeline for molecular complex detection in protein-interaction

Meghana V Palukuri¹, Edward M Marcotte²

¹Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, USA.

Biorxiv : the Preprint Server for Biology

|June 30, 2021

Summary

This summary is machine-generated.

Super.Complex is a novel pipeline for identifying protein complexes using supervised machine learning and automated machine learning (AutoML). This approach enhances accuracy and scalability in analyzing protein-protein interaction networks, revealing new insights into cellular functions and disease mechanisms.

Keywords:

graph mining overlapping community detection protein complex protein-interaction network supervised machine learning

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Area of Science:

Computational Biology
Bioinformatics
Systems Biology

Background:

Protein complexes are crucial for cellular functions like gene regulation.
Existing community detection methods for protein networks often lack accuracy and scalability.
Supervised machine learning approaches for protein complex identification are typically serial and can be improved.

Approach:

Developed Super.Complex, a distributed, supervised AutoML-based pipeline for overlapping community detection in weighted networks.
Introduced three new evaluation metrics for comparing detected and known communities.
Implemented a heuristic local search algorithm and a parallel computation strategy for scalability.

Key Points:

Super.Complex outperforms existing supervised and unsupervised methods on yeast protein-interaction networks.
Identified 1,028 protein complexes in a human protein-interaction network, with 234 linked to SARS-CoV-2.
Discovered 111 uncharacterized proteins within 103 identified complexes, highlighting potential new research avenues.

Conclusions:

Super.Complex offers a scalable and accurate method for protein complex identification.
The pipeline is generalizable and can incorporate domain-specific features for improved results.
Learned community characteristics can be transferred to new applications, facilitating broader discoveries.