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Cotranslational Protein Translocation01:20

Cotranslational Protein Translocation

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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Published on: July 14, 2015

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

Minsoo Kim1, Hanjin Bae1, Gyeongpil Jo1

  • 1Department of Physics, Sungkyunkwan University, Suwon, 16419, Korea.

Bioinformatics (Oxford, England)
|June 30, 2026
PubMed
Summary
This summary is machine-generated.

A new method, OTalign, uses Optimal Transport theory for protein sequence alignment, significantly improving the detection of remote homologs by adaptively handling gaps. This approach outperforms existing methods on challenging benchmarks.

Keywords:
optimal transportprotein language modelsprotein sequence alignmentremote homology

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Protein sequence alignment is vital in bioinformatics.
  • Aligning remote homologs with low sequence identity is challenging due to difficulties in gap handling.

Purpose of the Study:

  • Introduce a novel method for protein sequence alignment using Optimal Transport (OT) theory.
  • Provide a mathematically principled framework for modeling residue matches and gaps.

Main Methods:

  • Formulate sequence alignment as an entropy-regularized unbalanced optimal transport (UOT) problem.
  • Utilize embeddings derived from protein language models (PLMs).
  • Introduce position-specific, adaptive gap penalties.

Main Results:

  • OTalign consistently outperforms traditional and recent PLM-based methods on remote-homolog benchmarks (SABmark, MALIDUP, MALISAM).
  • Achieved high F1 scores: 0.594 on SABmark Superfamily and 0.358 on SABmark Twilight.
  • Provides a quantitative metric for PLM embedding effectiveness and enables end-to-end PLM fine-tuning.

Conclusions:

  • OTalign offers a superior approach to protein sequence alignment, especially for challenging remote homologs.
  • The method provides interpretable insights into sequence similarity and facilitates the development of alignment-optimized embeddings.
  • The code is publicly available, promoting further research and application.