From Static Snapshots to Dynamic Ensembles
Transforming protein structure prediction from static snapshots to dynamic ensembles for de novo design and drug discovery.
Simulation: The Breathing Protein
Proteins are not static statues. They vibrate, shift, and breathe. Hover over the visualization to trigger a conformational shift—simulating the transition from a closed (inactive) to an open (active) state.
Limitations of Rigid-Body Assumptions
Conventional algorithms rely on static crystal structures, ignoring thermodynamic fluctuations.
This leads to false negatives in mutation impact analysis, as steric clashes and entropy changes are overlooked.
Enhanced Sampling & Ensemble Generation
We integrate Deep Learning Potentials with Molecular Dynamics (MD).
By generating Boltzmann-weighted ensembles, we capture the full conformational landscape, revealing transient states critical for function.
Cryptic Pocket Identification & Ligand Docking
We identify cryptic allosteric sites that only open in specific conformational states.
This facilitates the rational design of small molecules for "undruggable" targets and the engineering of de novo enzymes with novel catalytic functions.