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New AI Model Improves Protein Structure Prediction from Sequences

Chen Wang, Boming Kang, Qinghua Cui· July 28, 2026 View original

Summary

Researchers introduced LC-SEPLM, an AI model that enhances protein language models by incorporating long-range residue-pair contact supervision during training. This adaptation significantly improves performance on protein-level tasks, especially remote-homology recognition, while maintaining sequence-only inference.

A new protein language model, LC-SEPLM (Long-range Contact-supervised ESM Protein Language Model), has been developed to improve the understanding of protein structures directly from their amino-acid sequences. Traditional protein language models primarily focus on sequential dependencies, often missing crucial three-dimensional residue contacts formed during protein folding. LC-SEPLM addresses this limitation by adapting the existing ESM2 model with LoRA and integrating supervision based on long-range residue-pair contacts. This allows the model to learn global sequence context associated with spatial contacts, even though its inference still relies solely on the protein sequence. Trained on a vast dataset of AlphaFold Swiss-Prot proteins, LC-SEPLM demonstrated significant improvements across eight protein-level tasks compared to ESM2. Notably, it achieved a substantial gain in remote-homology recognition, indicating its enhanced ability to identify structurally similar proteins from sequence data alone.

Why it matters

This advancement provides a more powerful tool for drug discovery, biotechnology, and fundamental biological research by enabling more accurate and efficient prediction of protein function and structure from sequence data.

How to implement this in your domain

  1. 1Integrate LC-SEPLM or similar contact-supervised models into existing protein engineering and drug discovery pipelines.
  2. 2Utilize the improved remote-homology recognition capabilities to identify novel protein targets or design new enzymes.
  3. 3Explore the model's potential for predicting protein-protein interactions or designing synthetic proteins.
  4. 4Collaborate with bioinformatics teams to validate and apply these models to specific research questions.

Who benefits

PharmaceuticalsBiotechnologyAcademia/ResearchAgriculture

Key takeaways

  • LC-SEPLM improves protein language models by incorporating structural contact information.
  • The model enhances performance on protein-level tasks, especially remote-homology recognition.
  • It maintains sequence-only inference, making it practical for various applications.
  • This research offers a more accurate way to predict protein function and structure.

Original post by Chen Wang, Boming Kang, Qinghua Cui

"arXiv:2607.22777v1 Announce Type: new Abstract: Protein language models learn transferable sequence representations. However, because they primarily model contextual dependencies along amino-acid sequences, their training objectives do not explicitly constrain the model to learn…"

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Originally posted by Chen Wang, Boming Kang, Qinghua Cui on X · view source

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