Ddt2000 Database !link! (480p × 8K)

While it may not have the name recognition of UniProt or the sheer size of the PDB, its focused approach to domain-domain interactions makes it an indispensable resource for hypothesis generation and computational benchmarking. As with any scientific tool, the key is knowing when and how to use it.

| Resource | Strength | Weakness | Best For | | :--- | :--- | :--- | :--- | | | Curation of non-redundant domain interfaces; evolutionary focus. | Smaller total entry count. | Benchmarking algorithms; evolutionary studies. | | PISA (PDBe) | Energetic calculations; comes directly from PDB. | Interface definitions are structure-specific, not domain-specific. | Analyzing a single complex in detail. | | Interactome3D | Integrates with human PPI networks. | Less focus on domain boundaries. | Systems biology of whole-cell interactions. | | 3did (3D interacting domains) | Curated domain-domain interaction families. | Primarily known interactions; fewer novel discoveries. | Finding templates for structural modeling. | ddt2000 database

Furthermore, with the rise of cryo-electron microscopy (cryo-EM), we are now solving massive macromolecular complexes (ribosomes, spliceosomes, nuclear pores). These structures contain hundreds of domain-domain interactions. The ddt2000 database is uniquely positioned to catalog these interactions, helping to untangle the complex wiring of the cellular machinery. The ddt2000 database is a powerful, specialized tool that should be in every structural bioinformatician’s arsenal. If your research involves protein engineering, domain evolution, interface design, or mutational analysis of multi-domain proteins, this database offers curated, non-redundant, and physically accurate interaction data that you simply cannot get from generic sequence databases. While it may not have the name recognition

Unlike generalist databases that simply store structural coordinates, the ddt2000 database specializes in the interfaces between protein domains. A single protein often consists of multiple domains—semi-independent folding units that carry out specific functions. The way these domains interact with each other (intramolecularly) or with domains from other proteins (intermolecularly) dictates nearly all biological processes, from signal transduction to immune recognition. | Smaller total entry count

The ddt2000 database often complements these tools. A typical strategy is: use 3did to find interaction templates, use the ddt2000 database to generate a non-redundant benchmark, and use PISA to calculate free energies. As structural biology enters the era of AI-powered prediction (e.g., AlphaFold, RoseTTAFold), the role of databases like ddt2000 is evolving. They are no longer just repositories; they are training sets . The high-quality, non-redundant interaction data in the ddt2000 database is ideal for training machine learning models to predict domain interfaces from sequence alone.