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A Multimodal Ensemble Framework for Optimal Mutant Prediction and Computational Enzyme Engineering.

Ding Luo¹, Huining Ji¹, Baodong Hu^2,3,4,5

¹State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, P. R. China.

Angewandte Chemie (International Ed. in English)

|December 18, 2025

Summary

This summary is machine-generated.

We developed GEMS, a new framework for enzyme engineering that uses multiple data types to predict beneficial mutations. GEMS improves enzyme function by accurately modeling complex protein interactions, outperforming existing methods.

Keywords:

Biocatalysis Computational design Directed evolution Enzyme engineering Mutation prediction

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Area of Science:

Biochemistry
Protein Engineering
Computational Biology

Background:

Enzyme engineering is crucial for developing improved biocatalysts.
Traditional methods struggle to model complex protein interactions (epistasis and long-range effects).

Purpose of the Study:

To present GEMS, a novel framework for enzyme engineering.
To leverage ensemble zero-shot learning across multiple data modalities for predicting beneficial enzyme variants.

Main Methods:

GEMS integrates evolutionary, structural, and sequence data.
It models the sequence-structure-function relationship using ensemble zero-shot capabilities.
Benchmarking against state-of-the-art methods was performed.

Main Results:

GEMS demonstrates competitive performance in ranking beneficial variants.
It excels at capturing long-range functional constraints, generating informative variant libraries.
Applied to five diverse enzymes, GEMS identified mutations improving catalytic efficiency by 1.1 to 3.2-fold.

Conclusions:

GEMS is a powerful and versatile tool for advanced enzyme engineering.
The framework effectively models complex protein interactions for improved enzyme function.