A Unified Protein Embedding Model With Local and Global Structural Sensitivity

Overview

Structural comparison between proteins is key to many research tasks, including evolutionary analysis, peptidomimetics, and functional annotation. Traditional structure alignment tools such as TM-Align, DALI, or ProBiS, which directly superimpose one protein onto another, are accurate but computationally expensive, and impractical at scale. Existing protein language models (PLMs), such as TM-Vec, improve computational efficiency but only capture global structural similarity, overlooking important motif-level structural details. In this paper, we propose a novel PLM consisting of a Siamese neural network, enabling efficient embedding-based structural comparison while also capturing both global and local structural similarity. Our model was trained on a dual loss function combining TM-score, a global similarity metric, and a variation of lDDT scores, a per-residue similarity metric. We tested against two datasets: a varied TM-score dataset from TM-Vec, and a high TM-score mutant dataset from VIPUR. Against these sets, our model achieved a TM-score MAE of 0.0741 and 0.0583, respectively, and a lDDT-score MAE of 0.0788 and 0.0038, respectively. Our model fulfills two key roles: first, it rapidly detects global structural differences. Second, it supports fine-grained structural assessments, improving sensitivity to subtle but functionally important structural changes.

Awards (1)

• Second Award of $2,400 $2,400

Competition history

• ISEF 2026 Computational Biology and Bioinformatics · Entry CBIO077 Affiliated fair in MA, United States of America

Resources

• ISEF abstract page ↗

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