Effective structure-aware protein alignment via residue-level contrastive learning

Protein alignment is indispensable for biological discovery, supporting structure comparison, functional annotation, and evolutionary inference. While structure-based methods are highly effective at detecting structural similarity, their applicability is constrained by the limited availability of experimentally resolved protein structures and high computational cost. Sequence-based approaches using pretrained protein language models (pLMs) provide scalable alternatives, yet supervised methods based on differentiable dynamic programming have not consistently outperformed simpler unsupervised strategies. Here, we present CLAlign, a structure-aware protein alignment framework based on contrasti