Dual-encoder contrastive learning accelerates enzyme discovery | JoVE Visualize

Area of Science:

Biotechnology and Synthetic Biology
Computational Biology and Bioinformatics
Enzyme Engineering

Background:

Enzyme engineering is crucial for biotechnology, but finding suitable starting enzymes is a major challenge.
Current computational methods like contrastive learning for enzyme discovery are not yet scaled or experimentally validated.
A scalable and effective computational approach is needed to overcome the bottleneck in enzyme discovery.

Purpose of the Study:

To present Horizyn-1, a computationally efficient deep learning framework for large-scale reaction-to-enzyme recommendation.
To validate the framework's effectiveness through comprehensive experimental testing across various enzyme discovery scenarios.
To demonstrate the potential of Horizyn-1 in accelerating biocatalysis and metabolic engineering.

Main Methods:

Developed Horizyn-1, a deep learning framework combining reaction fingerprints and protein language models.
Trained the model on millions of reaction-enzyme pairs for reaction-to-enzyme recommendation.
Experimentally validated Horizyn-1 for identifying enzymes for orphan reactions, predicting enzyme promiscuity, and discovering enzymes for nonnatural reactions.

Main Results:

Horizyn-1 achieved state-of-the-art performance, identifying correct enzymes within the top 100 hits for over 75% of test reactions.
Experimental validation confirmed Horizyn-1's efficacy in diverse enzyme discovery tasks, including noncanonical amino acid synthesis.
Fine-tuning with minimal data significantly improved performance on underrepresented reaction classes, showing logarithmic scaling with dataset size.

Conclusions:

Horizyn-1 effectively addresses the bottleneck of sourcing initial enzymes for optimization, enabling efficient in silico screening.
The framework's scalability and experimental validation promise to accelerate biocatalysis and metabolic engineering.
Continued improvement is expected with larger and more diverse training datasets, highlighting the framework's future potential.