DynaRepo: the repository of macromolecular conformational dynamics

Area of Science:

• Structural Biology
• Computational Biology
• Biophysics

Background:

• Proteins, RNA, and DNA are essential macromolecules involved in cellular processes, often forming dynamic complexes.
• Current research methods predominantly focus on static structures, neglecting the critical role of molecular dynamics in biological functions.
• The dynamic behavior of macromolecules is vital for interactions like antibody-antigen recognition and protein-nucleic acid binding but remains underexplored.

Purpose of the Study:

• To introduce DynaRepo, a comprehensive repository of macromolecular conformational dynamics.
• To provide a foundational dataset for developing and training dynamics-aware deep learning frameworks.
• To facilitate research into the dynamic mechanisms of biological interactions.

Main Methods:

• Curated a dataset of approximately 450 macromolecular complexes and 270 single-chain proteins from PDBbind and SAbDab.
• Performed extensive molecular dynamics simulations, generating over 1100 microseconds of data for each complex in triplicate.
• Conducted pre-calculated analyses on the simulation data to enhance usability.

Main Results:

• Established DynaRepo, a unique repository containing a large-scale collection of macromolecular dynamics data.
• Generated a substantial and diverse dataset covering various protein and nucleic acid complexes.
• Provided extensive pre-computed analyses alongside the simulation trajectories.

Conclusions:

• DynaRepo serves as a valuable resource for the computational biology and structural biology communities.
• The repository enables deeper investigation into the conformational dynamics of macromolecules.
• Facilitates advancements in deep learning approaches for predicting and understanding biological mechanisms.