GOdMD

GOdMD traces pathways for conformational transitions in macromolecules by combining discrete molecular dynamics (DMD) with biasing strategies to efficiently explore protein conformational space.

Key Features:

• Discrete Molecular Dynamics (DMD) core: Uses discrete molecular dynamics (DMD) as the core sampling engine for protein conformational space.
• Multiple minima Go-like potential: Employs a multiple minima Go-like potential energy function to model conformational landscapes without distorting chemical structures.
• Enhanced sampling techniques: Integrates metadynamics, Maxwell-Demon sampling strategies (Maxwell Demon Molecular Dynamics) and Essential Dynamics to improve exploration of conformational space.
• Integration of essential dynamics and Maxwell-Demon sampling: Combines essential dynamics with Maxwell-Demon sampling strategies to focus sampling on relevant collective motions.
• Restraints and customization: Allows inclusion of additional restraints such as ligands, known intermediates, or maintained contacts during transition tracing.
• Resolution versatility: Operates at multiple resolutions, including atomistic detail, to define initial pathways that can be further refined with atomistic molecular dynamics simulations.
• Reproduces experimental transitions: Capable of capturing a wide range of experimentally observed conformational transitions.

Scientific Applications:

• Protein folding and unfolding: Investigating protein folding and unfolding pathways.
• Ligand binding mechanisms: Studying ligand binding mechanisms and ligand-influenced transitions.
• Allosteric regulation: Exploring allosteric regulation and conformational coupling in proteins.
• Structural transition mapping: Tracing non-linear conformational transitions in macromolecules for structural biology studies.

Methodology:

Applies discrete molecular dynamics (DMD) with biasing techniques including metadynamics, Maxwell-Demon sampling (Maxwell Demon Molecular Dynamics) and Essential Dynamics, uses a multiple minima Go-like potential energy function, supports optional restraints (ligands, known intermediates, maintained contacts), and can operate at atomistic resolution to produce initial pathways for refinement by atomistic molecular dynamics simulations.