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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

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Embedding-based alignment: combining protein language models with dynamic programming alignment to detect structural

Lorenzo Pantolini^1,2, Gabriel Studer^1,2, Joana Pereira^1,2

¹Biozentrum, University of Basel, Basel 4056, Switzerland.

Bioinformatics (Oxford, England)

|January 4, 2024

Summary

This summary is machine-generated.

Protein language models (pLMs) offer new bioinformatics tools. Our embedding-based protein sequence alignment (EBA) method effectively identifies distant protein relationships, outperforming existing approaches.

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Area of Science:

Bioinformatics
Computational Biology
Structural Biology

Background:

Protein language models (pLMs) are advanced AI tools increasingly applied to biological sequences.
pLMs generate residue-level embeddings that capture the functional context of amino acids within a protein.
These embeddings have shown promise in downstream tasks, including identifying homologous proteins.

Purpose of the Study:

To introduce a novel method for generating protein sequence alignments using embeddings from pLMs.
To evaluate the performance of this new method in detecting structural similarities, especially in the challenging 'twilight zone' of homology.
To demonstrate the utility of combining pLMs with alignment techniques for protein relationship discovery.

Main Methods:

Development of an embedding-based protein sequence alignment (EBA) method.
Utilizing per-residue embeddings generated by protein language models.
Benchmarking EBA against classical alignment methods and other pLM-based approaches.

Main Results:

EBA successfully captures structural similarities between proteins, even in the twilight zone.
The method achieves high accuracy without requiring training or parameter optimization.
EBA outperforms both traditional sequence alignment and other pLM-based methods in detecting distant relationships.

Conclusions:

The integration of protein language models with alignment strategies is a powerful approach for bioinformatics.
EBA provides a robust and accurate tool for identifying homologous proteins, particularly those with low sequence identity.
This method advances the detection of evolutionary and structural relationships in protein sequences.