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Updated: Apr 8, 2026

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

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

Published on: July 14, 2015

pLM-Repeat: Exploiting the sequence representations of protein language models for sensitive repeat detection.

Kaiyu Qiu¹, Andrei N Lupas¹, Stanislaw Dunin-Horkawicz^1,2

¹Department of Protein Evolution, Max Planck Institute for Biology Tübingen, Tübingen, Germany.

Protein Science : a Publication of the Protein Society

|April 7, 2026

Summary

This summary is machine-generated.

We developed pLM-Repeat, a new tool for detecting protein repeats. It is faster and more sensitive than existing methods, enabling the discovery of novel repeat proteins.

Keywords:

AlphaFold database bioinformatic tool protein language model repeat protein sequence analysis

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A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

Area of Science:

Molecular Biology
Bioinformatics
Computational Biology

Background:

Duplication is crucial for molecular evolution and protein evolution.
Detecting ancient duplications is challenging due to sequence divergence.
Current methods like HHrepID are sensitive but slow, limiting large-scale analysis.

Purpose of the Study:

To introduce pLM-Repeat, a novel pipeline for identifying protein repeat patterns.
To leverage protein language models (pLMs) for faster and more sensitive repeat detection.
To discover novel repeat proteins using pLM-Repeat and the AlphaFold Protein Structure Database.

Main Methods:

Utilized sequence embeddings from protein language models (pLMs) via pLM-BLAST.
Developed the pLM-Repeat pipeline to analyze repeat patterns in sequence representations.
Employed a pre-filtering model trained on repeat protein representations for novel protein discovery.

Main Results:

pLM-Repeat demonstrates comparable sensitivity to HHrepID for repeat detection.
pLM-Repeat identifies a greater number of repeat units compared to existing methods.
The tool significantly reduces runtime, enabling efficient large-scale scans.
Novel repeat proteins were identified in the AlphaFold Protein Structure Database.

Conclusions:

pLM-Repeat offers a sensitive and rapid approach for detecting protein repeats.
The pipeline enhances the study of molecular evolution and protein structure.
pLM-Repeat facilitates the discovery of previously unknown repeat proteins.