Deep learning methods enable the structural prediction of proteins with high accuracy but are unable to model non-protein molecules that are essential for a protein’s biological function. Writing in Science, Krishna et al. introduce RoseTTAFold All-Atom (RFAA) to model the structure of full biological assemblies containing proteins, nucleic acids, small molecules, metals and covalent modifications.
A challenge in modeling generalized biomolecular assemblies is how to present all the components. Whereas proteins and nucleic acids can be modeled as linear chains, many small molecules are not polymers and need different representations. The authors tackle this problem by building RFAA using sequence-based representations of biopolymers combined with an atomic graph representation of small molecules and covalent modifications.
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Marchal, I. RoseTTAFold expands to all-atom for biomolecular prediction and design.
Nat Biotechnol42, 571 (2024). https://doi.org/10.1038/s41587-024-02211-5
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Published: 17 April 2024
Issue Date: April 2024
DOI: https://doi.org/10.1038/s41587-024-02211-5
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