SpirPro

SpirPro analyzes protein-protein interactions and metabolic pathways in Spirulina (Arthrospira) platensis to support investigation of molecular responses to temperature stress.

Key Features:

• Integrated Proteome and PPI Database: Hosts proteomic data specific to Spirulina (Arthrospira) platensis including protein-protein interaction (PPI) information and metabolic pathway insights derived from KEGG.
• High-Throughput Data Management: Manages OMICs-scale datasets including raw 2D-gel images and processed results from interaction and pathway analyses.
• Network Visualization Tools: Provides network visualization capabilities using HTML with CSS/JavaScript and Scalable Vector Graphics (SVG) to explore PPIs and metabolic pathways affected by temperature stresses.
• Domain/Motif Visualization: Enables visualization of protein domains and motifs to analyze interacting domains between proteins of interest.
• Temperature Stress Modeling Integration: Integrates proteomic data with PPI networks to support construction of temperature response models applicable to plant chloroplasts and photosynthetic bacteria.

Scientific Applications:

• Cyanobacterial gene manipulation and photosynthesis studies: Supports research using cyanobacteria as experimental platforms for gene manipulation studies involving photosynthesis.
• Molecular adaptation to environmental stress: Enables investigation of molecular adaptations of Spirulina (Arthrospira) platensis to low- and high-temperature stresses.
• Comparative proteomics for cultivation optimization: Facilitates comparative proteomic and pathway analyses to inform optimization of Spirulina mass production and related biotechnological applications.

Methodology:

Hosts proteomic datasets and PPI information, derives metabolic pathway annotations from KEGG, manages OMICs-scale raw data including 2D-gel images alongside processed interaction and pathway results, provides network visualization via HTML/CSS/JavaScript and SVG, supports domain/motif visualization, and integrates proteomic data with PPI networks for temperature response modeling.