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.
CASP Blind Prediction Results
Consistent top performance at the most rigorous international benchmarks in structural bioinformatics.
Top-5 Refinement Method
CASP13 Refinement Category
Best Server Award
Lifts models beyond AlphaFold2 accuracy
2nd Place · Ligand-Pose
Surpasses AlphaFold3 Server
Outperforms AlphaFold3
Quaternary Structure Prediction
30,000+ Users
400,000+ jobs processed per year
16,000+ Engineered Features
Protein binder quality assessment
From Research to Cancer Vaccines
Currently building a TCR-pMHC structural prediction pipeline as part of the personalised cancer vaccine programme at InstaDeep (BioNTech Group), integrating cutting-edge tools including AlphaFold2, Boltz-2, and Chai-1 to inform neoantigen targeting strategies.