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Mutations

Mutations

Covalently Linked Protein Regulators

Covalently Linked Protein Regulators

Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein....

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Updated: Jun 19, 2025

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Stability Oracle: a structure-based graph-transformer framework for identifying stabilizing mutations.

Daniel J Diaz^1,2,3, Chengyue Gong⁴, Jeffrey Ouyang-Zhang⁴

¹UT Austin, Department of Computer Science, Austin, TX, 78712, USA. dannyjdiaz305@gmail.com.

Nature Communications

|July 23, 2024

Summary

This summary is machine-generated.

Stability Oracle, a new structure-based graph-transformer framework, accurately identifies protein mutations that enhance stability. This computational tool accelerates biotechnologies by overcoming data limitations and improving protein engineering.

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Area of Science:

Biotechnology
Protein Engineering
Computational Biology

Background:

Engineering stabilized proteins is crucial for industrial and pharmaceutical applications.
Existing methods face challenges with data scarcity, bias, generalization, and computation time.

Purpose of the Study:

To present Stability Oracle, a novel structure-based graph-transformer framework.
To achieve state-of-the-art (SOTA) performance in identifying thermodynamically stabilizing mutations.

Main Methods:

Developed a structure-based graph-transformer framework named Stability Oracle.
Introduced innovations: Thermodynamic Permutations for data augmentation, structural amino acid embeddings, and a protein structure-specific attention-bias mechanism.
Utilized training/test splits to mitigate data leakage and ensure robust model evaluation.

Main Results:

Stability Oracle achieves SOTA performance in identifying stabilizing mutations.
The framework outperforms sequence-based models like Prostata-IFML, despite being trained on significantly less data and having fewer parameters.
Demonstrated the effectiveness of structural amino acid embeddings and attention-bias mechanisms.

Conclusions:

Stability Oracle offers a powerful and efficient approach to protein stabilization.
The framework provides a viable path for fine-tuning structure-based transformers for various protein engineering tasks.
Accelerates the development of protein-based biotechnologies through accurate prediction of stabilizing mutations.