aceA structure-aware generative AI framework for revealing functional relationships in protein families
Proteins can be studied through their sequence statistics or structural properties. These represent complementary views that are useful but lack a quantitative framework to tell, family by family, which is most informative and how to combine them. We introduce a framework that builds both views in parallel: amino acid (AA) alignments are translated into parallel alignments over a 3D interaction (3Di) structure-informed alphabet. Variational autoencoders compress each into a two-dimensional map, and direct coupli…
Pairing amino acid and 3Di structural alignments in generative landscapes recovered known subfamilies and revealed evolutionary relationships in viral glycoproteins that sequence alone did not show.
Evidence
- Peer-reviewedProceedings of the National Academy of Sciences2026-10-02
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Truvace Impact Record TRV-2026-1381, v1: “A structure-aware generative AI framework for revealing functional relationships in protein families.” Truvace, 2026-10-14. /record/TRV-2026-1381 (accessed at citation time). sha256 30b908e6bc94cf74…
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