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From high-throughput evaluation to wet-lab studies: advancing mutation effect prediction with a retrieval-enhanced

Yang Tan^1,2,3,4, Ruilin Wang², Banghao Wu^1,4

¹Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, 200240, China.

Bioinformatics (Oxford, England)

|July 15, 2025

Summary

VenusREM, a novel protein language model, enhances enzyme engineering by predicting mutation effects. This deep learning tool improves enzyme stability and activity, validated through computational and experimental studies.

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Area of Science:

Biochemistry
Computational Biology
Protein Engineering

Background:

Enzyme engineering is crucial for industrial and research applications, traditionally relying on directed evolution and rational design.
Deep learning models offer advanced, cost-effective alternatives by capturing coevolutionary patterns in proteins.
Understanding protein sequence, structure, and function relationships remains a key challenge in enzyme engineering.

Purpose of the Study:

To introduce VenusREM, a retrieval-enhanced protein language model for enzyme engineering.
To evaluate VenusREM's performance in predicting mutation effects on enzyme properties.
To validate VenusREM's utility through computational and experimental assessments.

Main Methods:

Developed VenusREM, a protein language model incorporating retrieval mechanisms.
Assessed VenusREM on 217 ProteinGym benchmark assays.
Conducted experimental validation using VHH antibody and DNA polymerase mutants.

Main Results:

VenusREM achieved state-of-the-art performance on the ProteinGym benchmark.
Computational and experimental analyses confirmed VenusREM's ability to improve VHH antibody stability and binding affinity.
Designed DNA polymerase mutants with enhanced activity at elevated temperatures, validated experimentally.

Conclusions:

VenusREM is a reliable computational tool for enzyme engineering.
The study provides a comprehensive framework for evaluating mutation effect prediction models.
Deep learning approaches significantly advance protein engineering capabilities.