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Functional annotation of enzyme-encoding genes using deep learning with transformer layers.

Gi Bae Kim^1,2,3, Ji Yeon Kim^1,2,3, Jong An Lee^1,2,3

¹Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.

Nature Communications

|November 14, 2023

Summary

This summary is machine-generated.

DeepECtransformer, a deep learning model, predicts Enzyme Commission (EC) numbers for unannotated microbial genes. This advances functional gene annotation by identifying enzyme functions using sequence data and motifs.

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Area of Science:

Genomics and Bioinformatics
Computational Biology
Enzymology

Background:

Functional annotation of microbial genomes is incomplete, particularly for enzyme-encoding genes.
Enzyme Commission (EC) numbers classify enzyme catalytic functions, crucial for understanding microbial metabolism.
Accurate EC number prediction can significantly improve the annotation of uncharacterized genes.

Purpose of the Study:

To develop a deep learning model for predicting Enzyme Commission (EC) numbers.
To enhance the functional annotation of uncharacterized open reading frames in microbial genomes.
To validate the model's predictions experimentally.

Main Methods:

Developed DeepECtransformer, a deep learning model employing transformer layers for EC number prediction.
Applied the model to the Escherichia coli K-12 MG1655 genome to predict EC numbers for unannotated genes.
Experimentally validated the predicted enzymatic activities for selected proteins (YgfF, YciO, YjdM).

Main Results:

DeepECtransformer successfully predicted EC numbers for 464 un-annotated genes in E. coli.
Experimental validation confirmed the predicted enzymatic activities for three proteins.
Analysis revealed the model utilizes enzyme functional motifs for accurate predictions.

Conclusions:

DeepECtransformer is an effective deep learning method for predicting enzyme functions (EC numbers).
The model facilitates the functional annotation of previously uncharacterized microbial genes.
This approach aids in understanding microbial gene function and metabolic pathways.