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Using Caenorhabditis elegans as a Model System to Study Protein Homeostasis in a Multicellular Organism

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Zero-shot mutation effect prediction on protein stability and function using RoseTTAFold.

Sanaa Mansoor^1,2,3, Minkyung Baek^1,2,4, David Juergens^1,2,3

¹Department of Biochemistry, University of Washington, Seattle, Washington, WA, USA.

Protein Science : a Publication of the Protein Society

|September 11, 2023

Summary

This summary is machine-generated.

RFjoint, a protein language model, accurately predicts mutation effects without specific training. This variant of RoseTTAFold demonstrates a broad understanding of protein sequence-structure landscapes, comparable to other advanced models.

Keywords:

deep learning language models mutation effect prediction protein design zero-shot prediction

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Published on: July 14, 2015

Area of Science:

Computational Biology
Protein Engineering
Bioinformatics

Background:

Predicting how mutations affect protein function and stability is a significant challenge in molecular biology.
Current methods often require extensive training on specific protein families.

Purpose of the Study:

To evaluate the performance of RFjoint, a RoseTTAFold variant trained for sequence and structure recovery, in predicting mutation effects.
To compare RFjoint's zero-shot prediction accuracy against other established models.

Main Methods:

Assessed RFjoint's accuracy in predicting mutation effects across diverse protein families.
Compared RFjoint's performance to MSA Transformer (zero-shot) and DeepSequence (family-specific training).

Main Results:

RFjoint achieved comparable accuracy to MSA Transformer and DeepSequence in predicting mutation effects without task-specific fine-tuning.
The model demonstrated a robust understanding of protein mutational landscapes, equivalent to large protein language models.

Conclusions:

RFjoint possesses a broad understanding of protein sequence-structure relationships, making it a versatile tool for protein modeling.
The joint sequence-structure reasoning capability of RFjoint holds promise for enhanced mutation effect predictions with further supervised training.