LGNM Learned Graph Network Model
Predict per-residue flexibility, normal modes, and conformational dynamics using a Learned Graph Network Model with quantum-inspired neural PDE operators.
Analysis Suite
Residue-Level Flexibility Prediction
Predict residue-wise flexibility directly from protein structures using a learned Graph Network Model integrated with graph neural networks, providing high-resolution dynamic profiles without molecular dynamics simulations.
Collective Motion Analysis
Compute the lowest-frequency normal modes to characterize intrinsic collective motions associated with conformational changes, domain rearrangements, and functional dynamics.
Quantum-Inspired Computational Engine
Employ a quantum-inspired Hamiltonian formulation to efficiently solve network dynamics, reducing computational complexity while maintaining accurate flexibility estimation for large proteins.
Interactive Structural Visualization
Explore predicted flexibility through an interactive three-dimensional molecular viewer with residue coloring, structural annotations, measurements, and publication-ready rendering.
Comprehensive Analytical Reports
Generate publication-quality RMSF profiles, flexibility distributions, normal mode visualizations, residue statistics, and structural summaries for downstream interpretation.
Exportable Research Outputs
Download annotated protein structures, residue-level prediction tables, processed datasets, high-resolution figures, and analysis reports in standard research formats.
Analysis Workflow
Load Protein Structure
Enter a Protein Data Bank (PDB) accession (e.g., 6LU7) or upload your own
.pdb or .cif structure for analysis.
Configure Analysis
Select the protein chain(s) to analyse and configure the prediction settings before initiating the computation.
Explore and Export Results
Visualize residue-level flexibility in an interactive 3D viewer, examine analytical plots, and download all generated structures, figures, and result files.