DEER-PREdict

DEER-PREdict predicts Double Electron-Electron Resonance (DEER) distance distributions and Paramagnetic Relaxation Enhancement (PRE) rates from ensembles of protein conformations to interpret paramagnetic resonance data derived from molecular dynamics simulations.

Key Features:

• Conformational ensemble analysis: Processes large ensembles of protein conformations generated by molecular dynamics simulations to compute ensemble-averaged observables.
• Rotamer library modeling: Implements a rotamer library approach to model covalently attached paramagnetic probes and their spatial orientations.
• DEER and PRE prediction: Computes DEER distance distributions and PRE rates directly from conformational ensembles.
• Model validation support: Facilitates validation or refinement of molecular models and MD ensembles against DEER and PRE experimental data.
• Experimental validation: Has been tested on experimentally characterized systems including HIV-1 protease, T4 Lysozyme, and Acyl-CoA-binding protein.

Scientific Applications:

• DEER data interpretation: Provides ensemble-derived predictions to interpret DEER distance distributions measured by double electron-electron resonance experiments.
• PRE analysis: Provides ensemble-derived predictions to interpret Paramagnetic Relaxation Enhancement rates for mapping transient contacts and dynamics.
• Model validation and refinement: Enables comparison of molecular dynamics ensembles and structural models against DEER and PRE experimental results.
• Study of disordered and multidomain proteins: Analyzes conformational heterogeneity in intrinsically disordered proteins and multidomain proteins using ensemble-based observables.

Methodology:

Uses molecular dynamics simulation ensembles and a rotamer library approach to model covalently attached paramagnetic probes and predict DEER distance distributions and PRE rates from those ensembles.