ModelCIF: An Extension of PDBx/mmCIF Data Representation for Computed Structure Models.

Area of Science:

• Computational structural biology
• Bioinformatics
• Data science

Background:

• Macromolecular structure data is crucial for biological research.
• Existing standards like PDBx/mmCIF primarily focus on experimentally determined structures.
• A need exists for a standardized framework for computationally derived structural models.

Purpose of the Study:

• To introduce ModelCIF, a data information framework for computational structural biology.
• To enable the deposition, archiving, and dissemination of FAIR computed structure models.
• To describe the architecture, content, governance, and community support for ModelCIF.

Main Methods:

• ModelCIF is developed as an extension of the PDBx/mmCIF data standard.
• It defines specific attributes and metadata for macromolecular models generated computationally.
• The framework is managed by the Worldwide Protein Data Bank (wwPDB) partnership.

Main Results:

• ModelCIF provides an extensible data representation for computed structure models (CSMs).
• It ensures data is Findable, Accessible, Interoperable, and Reusable (FAIR).
• Community tools and software libraries supporting ModelCIF are available.

Conclusions:

• ModelCIF accelerates scientific discovery by standardizing and facilitating access to computational structural models.
• It enhances the FAIRness of predicted macromolecular structures.
• The framework supports the growing field of computational structural biology.