BioGRID

Download the human PPI dataset from BioGRID.

Map known drug targets to the PPI network.

Identify proteins that interact directly with known targets but are involved in different disease pathways.

Validate these 'neighbors' as potential novel targets using BioGRID's curated evidence.

Use BioGRID API to pull all physical protein-protein interactions.

Clean data to include only interactions supported by multiple evidence codes.

Feed the interaction graph into an embedding layer of a neural network.

Train the model to identify patterns in protein features that correlate with BioGRID-validated interactions.

Input a list of mutated genes from patient NGS data into BioGRID.

Extract all curated interactors for these genes.

Perform cluster analysis to find dense sub-networks of interacting mutated proteins.

Identify 'hub' proteins that could serve as critical therapeutic vulnerabilities.

Select 'Viral' organism datasets in the BioGRID search console.

Filter for interactions between specific viral proteins (e.g., SARS-CoV-2 Spike) and human proteins.

Analyze the biological processes (GO terms) associated with the human interactors to understand viral entry mechanisms.

Search the 'Dark' protein in BioGRID.

Analyze the known functions of its interaction partners ('guilt by association').

Hypothesize that the unknown protein shares biological pathways with its partners.

Plan laboratory experiments based on these BioGRID-derived insights.

Import Mass Spec results into a spreadsheet.

Use the BioGRID Batch Search tool to compare experimental hits against previously reported interactions.

Highlight 'Novel' interactions that have never been recorded in BioGRID as high-priority for further study.

Extract an interaction sub-graph for a specific cell type.

Submit the interactor list to pathway analysis tools that integrate BioGRID data.

Use the results to determine the primary metabolic or signaling effects of a biological treatment.