OTalign: Optimal Transport Alignment for Remote Protein Homologs Using Protein Language Model Embeddings.

Authors

Minsoo Kim, Hanjin Bae, Gyeongpil Jo, Kunwoo Kim, Jejoong Yoo, Keehyoung Joo

Published in

Bioinformatics (Oxford, England). Jun 30, 2026. Epub Jun 30, 2026.

Abstract

Protein sequence alignment is a crucial task in bioinformatics, yet aligning remote homologs with low sequence identity remains a longstanding challenge, particularly due to the difficulty of handling gaps. We introduce a new method that applies Optimal Transport (OT) theory to sequence alignment, providing a mathematically principled framework for modeling residue matches and gaps.
OTalign formulates sequence alignment as an entropy-regularized unbalanced optimal transport (UOT) problem over embeddings derived from protein language models (PLMs). Unlike traditional methods, it introduces position-specific gap penalties that adapt to each sequence pair. On challenging remote-homolog benchmarks (SABmark, MALIDUP, MALISAM), OTalign consistently outperforms baselines (Needleman-Wunsch, HHalign) and recent PLM-based methods (PLMAlign, DeepBLAST), achieving F1 scores of 0.594 on SABmark Superfamily and 0.358 on SABmark Twilight. Furthermore, OTalign provides a quantitative and interpretable metric of how effectively PLM embeddings represent sequence similarity relationships. Finally, its differentiable nature enables end-to-end fine-tuning of PLMs, establishing a framework for learning embeddings explicitly optimized for alignment tasks.
This code is available at https://github.com/DeepFoldProtein/OTalign.
Supplementary data are available at Bioinformatics online.

PMID:
42378434
Bibliographic data and abstract were imported from PubMed on 01 Jul 2026.

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