Ensemblify

Ensemblify generates and analyzes conformational ensembles of intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) using Monte Carlo methods integrated with AlphaFold confidence metrics to guide sampling and enable validation against experimental data.

Key Features:

• Monte Carlo Sampling: Uses a Monte Carlo algorithm coupled with neighbour-aware sampling of dihedral angles from curated or user-defined fragment libraries to explore conformational space.
• AlphaFold Integration: Directly incorporates information from AlphaFold's confidence metrics as flexible energy restraints in PyRosetta to guide the sampling.
• Multi-chain and Multi-domain Support: Supports multi-chain and multi-domain proteins and can sample N-terminal, C-terminal, and inter-domain linkers while preserving folded regions.
• Experimental Validation: Ensemble quality can be validated and refined against experimental data such as SAXS via Bayesian/Maximum Entropy (BME) reweighting.
• Structural Analysis: Interactive dashboards provide in-depth structural analysis and comparison.

Scientific Applications:

• IDP/IDR Ensemble Generation: Generates conformational ensembles for intrinsically disordered proteins and regions that challenge structural characterization due to their dynamic conformational ensembles and lack of stable structure.
• Multi-domain Protein Modeling: Models multi-domain or multi-chain proteins including sampling of N-terminal, C-terminal, and inter-domain linkers.
• Experimental Data Integration: Validates and refines ensembles against experimental data such as SAXS to improve ensemble-data agreement.

Methodology:

Ensemblify uses a Monte Carlo algorithm coupled with neighbour-aware sampling of dihedral angles from curated or user-defined fragment libraries. It incorporates AlphaFold confidence metrics as flexible energy restraints in PyRosetta. Ensemble quality is validated and refined against experimental data such as SAXS via Bayesian/Maximum Entropy (BME) reweighting.