OpenMDlr

OpenMDlr models and refines protein structures using inter-atomic distance constraints and optional dihedral angles to generate or improve models consistent with experimental distance data and biomolecular force fields.

Key Features:

• Pairwise-distance modeling: Models protein structures from pairwise distances between any atoms to accommodate diverse experimental inter-atomic distance data.
• Dihedral angle integration: Optionally incorporates dihedral angles into the modeling objective to enhance conformational accuracy.
• Biomolecular force-field refinement: Leverages modern biomolecular force fields to score and refine protein conformations against distance and angle restraints.
• Multi-core computation: Utilizes multiple compute cores to accelerate modeling and refinement tasks.
• Implementation and environment: Implemented in Python 3.x with dependencies manageable via the Conda package management system.

Scientific Applications:

• Experimental distance-based structure determination: Constructs protein models from experimental distance restraints derived from NMR spectroscopy or cryo-electron microscopy.
• Predicted-structure refinement: Refines computationally predicted protein structures to better satisfy inter-atomic distance and dihedral constraints and force-field energies.

Methodology:

Integration of pairwise distance constraints and optional dihedral angles into structural models using biomolecular force fields for scoring and conformational refinement.