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Related Concept Videos

Intrinsically Disordered Proteins02:18

Intrinsically Disordered Proteins

Intrinsically disordered proteins are a group of proteins that do not fold into specific three-dimensional structures. Their structural flexibility allows them to complement ordered proteins to perform functions that are inaccessible to rigid structures. They are more common in eukaryotes than prokaryotes and may either be exclusively intrinsically disordered or hybrid proteins, consisting of a mix of ordered and disordered regions. The absence of a rigid structure in these proteins can be...
Intrinsically Disordered Proteins02:18

Intrinsically Disordered Proteins

Intrinsically disordered proteins are a group of proteins that do not fold into specific three-dimensional structures. Their structural flexibility allows them to complement ordered proteins to perform functions that are inaccessible to rigid structures. They are more common in eukaryotes than prokaryotes and may either be exclusively intrinsically disordered or hybrid proteins, consisting of a mix of ordered and disordered regions. The absence of a rigid structure in these proteins can be...

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amyloid-predict and LLPS-predict: Predicting phase separation propensities in the intrinsically disordered proteome.

Samuel Lobo1, Leif Griem1, M Scott Shell1

  • 1Department of Chemical Engineering, University of California, Santa Barbara, CA 93106.

Proceedings of the National Academy of Sciences of the United States of America
|May 26, 2026
PubMed
Summary

We developed computational tools to predict protein aggregation (amyloid formation) and liquid-liquid phase separation (LLPS) across the human proteome. These tools offer rapid screening for biological insights and therapeutic design.

Keywords:
amyloidsintrinsically disordered proteinsliquid–liquid phase separationneurodegenerative diseaseprotein language models

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Area of Science:

  • Cellular Biology
  • Biophysics
  • Computational Biology

Background:

  • Amyloid formation and liquid-liquid phase separation (LLPS) are crucial cellular processes implicated in both normal functions and diseases.
  • Understanding the propensity for these phenomena is vital for biological and medical research.

Purpose of the Study:

  • To introduce a computational framework for predicting amyloid and LLPS propensities using protein language models.
  • To enable rapid, proteome-wide annotation of peptides and residues for these properties.
  • To investigate the distribution and biological relevance of amyloid and LLPS propensities in the human proteome.

Main Methods:

  • Development of computational classifiers (amyloid-predict and LLPS-predict) based on protein language model embeddings.
  • Benchmarking against existing AI and physics-based tools for classification performance.
  • Application of classifiers to intrinsically disordered regions (IDRs) across the entire human proteome.

Main Results:

  • The amyloid-predict tool demonstrated superior classification performance and speed compared to existing methods on a hexapeptide benchmark.
  • Analysis revealed specific protein categories enriched in amyloid and/or LLPS propensities, including signaling receptors, carbohydrate-binding proteins, and mRNA-binding proteins.
  • Identified patterns of both high amyloid and LLPS propensity in certain amyloid-forming and prionic proteins.

Conclusions:

  • The developed framework provides a rapid and accurate method for assessing amyloid and LLPS potentials across proteomes.
  • The study offers novel insights into the biological roles of protein aggregation and LLPS by mapping their distribution in disordered proteins.
  • This work serves as a valuable tool for basic research, disease mechanism studies, and the development of peptide therapeutics.