DrLLPS

DrLLPS provides an integrative database of known and computationally predicted liquid-liquid phase separation (LLPS) proteins across eukaryotes to support study of membraneless organelles and LLPS-related molecular mechanisms.

Key Features:

• Comprehensive Protein Collection: DrLLPS contains 437,887 known and computationally predicted proteins associated with LLPS across 164 eukaryotic species, including 150 scaffold proteins, 987 regulators, and 8,148 potential client proteins.
• Categorization into Biomolecular Condensates: Proteins are organized into 40 distinct biomolecular condensates to enable condensate-specific analysis.
• Ortholog Identification: The database includes potential orthologs of known LLPS-associated proteins to support comparative genomic studies across species.
• Detailed Annotation in Model Organisms: For eight model organisms, DrLLPS integrates annotations from 110 resources covering protein disordered regions, domain annotations, post-translational modifications (PTMs), genetic variations and cancer mutations, molecular interactions and disease associations, drug-target relationships, physicochemical properties, functional protein annotations, proteomics data, 3D protein structures, subcellular localizations, mRNA expression profiles, DNA & RNA elements, and DNA methylations.

Scientific Applications:

• Understanding MLO Formation: Provides scaffold, regulator, and client protein data and related annotations to investigate mechanisms underlying membraneless organelle formation.
• Investigating Disease Mechanisms: Includes genetic variation, cancer mutation, and disease-association annotations to explore links between LLPS dysregulation and disease.
• Drug Discovery and Development: Contains drug-target relationship annotations to identify potential therapeutic targets within LLPS-associated pathways.
• Comparative Genomics and Evolutionary Studies: Supplies ortholog information and species-spanning protein data to analyze evolutionary conservation and divergence of LLPS mechanisms.

Methodology:

Includes computational prediction of LLPS-associated proteins, ortholog identification, and integration of annotations from 110 external resources.