Dockground

Dockground provides curated datasets and computational resources for protein–protein docking and analysis of protein–protein interactions (PPIs).

Key Features:

• Extensive Dataset Collection: Dockground includes bound and unbound protein structures (both experimentally determined and simulated), model-model complexes, docking decoys derived from experimentally determined and modeled proteins, and templates for comparative docking.
• Core Datasets: The core dataset consists of bound protein–protein complexes stored in a relational PostgreSQL database and serves as the source from which other datasets are generated.
• Automated Update Procedures: Automated update procedures incorporate newly available experimental and modeled structures to keep the datasets current and comprehensive.

Scientific Applications:

• Protein Docking: Supports development, testing, and benchmarking of protein docking algorithms and scoring functions using curated complexes and decoys.
• Structural Biology: Enables characterization and analysis of protein–protein interaction interfaces and complex architectures.
• Drug Discovery: Informs identification of protein interaction sites and structure-based analysis relevant to therapeutic design.

Methodology:

Uses computational modeling techniques focused on protein docking, including generation of simulated unbound structures and model-model complexes, creation of docking decoys, template-based comparative docking, dataset derivation from core bound complexes, and automated dataset updates; datasets are maintained in a relational PostgreSQL database.