BioPlex

BioPlex maps protein interaction networks through high-throughput affinity-purification mass spectrometry (AP-MS) to produce a repository of human protein-protein interactions for studying proteome architecture and function.

Key Features:

• Extensive Interaction Data: BioPlex 3.0 contains AP-MS data from affinity purification of approximately half the human proteome (10,128 proteins) in HEK293T cells, encompassing over 118,162 interactions among 14,586 proteins.
• Cell-Line Specific Networks: Generates cell-line-specific interaction networks enabling comparative analysis between HEK293T and HCT116 cells to reveal shared core interactions and cell-specific subnetwork rewiring.
• Functional Insights: Clusters interacting proteins into communities to identify functional modules corresponding to biological processes, cellular localization, and molecular functions for protein characterization.
• Disease Relevance: Links protein interactions with disease annotations to identify networks associated with over 2,000 diseases and place candidate disease genes in a cellular context.
• Proteome Organization Principles: Highlights how interactions covary among proteins with shared functions, reflecting dynamic remodeling of the proteome to produce specific cellular phenotypes.
• Validation and Customization: Comparative analysis across cell lines validates thousands of interactions and reveals customization of protein networks, emphasizing essential proteins within core complexes and adaptive rewiring of subnetworks.

Scientific Applications:

• Characterization of uncharacterized proteins: Assigns putative functions to incompletely characterized proteins by association with interacting partners and communities.
• Large-scale network analyses: Supports analyses of domain associations, subcellular localization, and co-complex formation at proteome scale.
• Disease-associated interaction discovery: Integrates interaction data with other approaches to uncover biologically or clinically significant interactions, including those implicated in familial amyotrophic lateral sclerosis.

Methodology:

Computational methods explicitly include generation of cell-line-specific interaction networks from AP-MS data, clustering interacting proteins into communities, comparative network analysis across cell lines, linking interactions to disease annotations, and large-scale analyses of domain associations, subcellular localization, and co-complex formation.