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Daily Signal January 27, 2026 · 8 min read

Issue #33: ModelCIF update: Supporting Emerging Classes of Computational Macromolecular Models.

Protein Design Digest - 2026-01-27 - ModelCIF update: Supporting Emerging Classes of Computational Macromolecular Models.

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ModelCIF update: Supporting Emerging Classes of Computational Macromolecular Models.

The recent development of highly accurate protein structure prediction tools has led to a rapid expansion in the scope of computational structural biology, enabling a much wider range of modelling studies than ever before. These new in silico opportunities help life science researchers understand how proteins interact with their environment and support design of new molecules with desired properties. Ultimately, they have broad applications, e.g. in medicine, drug discovery or engineering. To ensure reproducibility and to facilitate data exchange and reuse, predicted structures or computed structure models can be stored using ModelCIF, a rich data representation designed to include the atomic coordinates/metadata. The previously published version of ModelCIF (1.4.4; 2022-12-21) mainly covered protein structure predictions generated by homology and ab initio modelling. In this work, we present an extension of the ModelCIF (https://github.com/ihmwg/ModelCIF) data standard and its associated tools. This extension supports important new use cases, including modelling protein-ligand and protein-protein interactions, sampling multiple conformational states and designing proteins de novo. We define guidelines for storage and validation of modelling results for those use cases by applying new and existing ModelCIF categories to capture protocols, inputs and outputs. Additionally, we outline updates to the software tools and resources that implement these new standards and provide functionality for model generation, validation, archiving, and visualisation. By enabling consistent metadata capture across different modelling workflows, this framework aims to support the FAIR dissemination of computational models, thereby promoting reproducibility and reusability in downstream applications.

Why this matters: Critical for improving fold accuracy and reducing structural uncertainty in de novo design.

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Deep learning is not a magic wand, but a powerful lens for structural biology. — Recep Adiyaman

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