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KAIST Accelerates Synthetic Microbe Design by Discovering Novel Enzymes Using AI
< (From left) Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering (top), Hongkeun Ji, PhD candidate of the Department of Chemical and Biomolecular Engineering (top), Ha Rim Kim, PhD candidate of the Department of Chemical and Biomolecular Engineering, and Dr. Gi Bae Kim of the BioProcess Engineering Research Center >
Enzymes are proteins that catalyze biochemical reactions within cells and play a pivotal role in metabolic processes. Accordingly, identifying the functions of novel enzymes is a critical task in the construction of microbial cell factories.
A KAIST research team has leveraged artificial intelligence (AI) to design novel enzymes that do not exist in nature, significantly accelerating microbial cell factory development and boosting the potential for next-generation biotechnological applications such as drug development and biofuel production.
KAIST (represented by President Kwang-Hyung Lee) announced on the 21st of April that Distinguished Professor Sang Yup Lee and his team from the Department of Chemical and Biomolecular Engineering have published a review titled “Enzyme Functional Classification Using Artificial Intelligence,” which outlines the advancement of AI-based enzyme function prediction technologies and analyzes how AI has contributed to the discovery and design of new enzymes.
Professor Lee’s team systematically reviewed the development of enzyme function prediction technologies utilizing machine learning and deep learning, offering a comprehensive analysis.
From sequence similarity-based prediction methods to the integration of convolutional neural networks (CNNs), recurrent neural networks (RNNs), graph neural networks (GNNs), and transformer-based large language models, the paper covers a broad range of AI applications. It analyzes how these technologies extract meaningful information from protein sequences and enhance prediction accuracy.
In particular, enzyme function prediction using deep learning goes beyond simple sequence similarity analysis. By automatically extracting structural and evolutionary features embedded in amino acid sequences, deep learning enables more precise predictions of catalytic functions.
This highlights the unique advantages of AI models compared to traditional bioinformatics approaches.
Moreover, the review suggests that the advancement of generative AI will move future research beyond predicting existing functions to generating entirely new enzymes with functions not found in nature. This shift is expected to profoundly impact the trajectory of biotechnology and synthetic biology.
< Figure 1. Extraction of enzyme characteristics and function prediction using various deep learning structures >
Ha Rim Kim, a Ph.D. candidate and co-first author from the Department of Chemical and Biomolecular Engineering, stated, “AI-based enzyme function prediction and enzyme design are highly important across various fields including metabolic engineering, synthetic biology, and healthcare.”
Distinguished Professor Sang Yup Lee added, “AI-powered enzyme function prediction shows the potential to solve diverse biological problems and will significantly contribute to accelerating research across the entire field.”
The review was published on March 28 in Trends in Biotechnology, a leading biotechnology journal issued by Cell Press.
※ Title: Enzyme Functional Classification Using Artificial Intelligence
※DOI: https://doi.org/10.1016/j.tibtech.2025.03.003
※ Author Information: Ha Rim Kim (KAIST, Co-first author), Hongkeun Ji (KAIST, Co-first author), Gi Bae Kim (KAIST, Third author), Sang Yup Lee (KAIST, Corresponding author)
This research was supported by the Ministry of Science and ICT under the project Development of Core Technologies for Advanced Synthetic Biology to Lead the Bio-Manufacturing Industry (aimed at replacing petroleum-based chemicals), and also by joint support from the Ministry of Science and ICT and the Ministry of Health and Welfare for the project Development of Novel Antibiotic Structures Using Deep Learning-Based Synthetic Biology.
2025.04.07 View 383 -
KAIST-UCSD researchers build an enzyme discovering AI
- A joint research team led by Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering and Bernhard Palsson of UCSD developed ‘DeepECtransformer’, an artificial intelligence that can predict Enzyme Commission (EC) number of proteins.
- The AI is tasked to discover new enzymes that have not been discovered yet, which would allow prediction for a total of 5,360 types of Enzyme Commission (EC) numbers
- It is expected to be used in the development of microbial cell factories that produce environmentally friendly chemicals as a core technology for analyzing the metabolic network of a genome.
While E. coli is one of the most studied organisms, the function of 30% of proteins that make up E. coli has not yet been clearly revealed. For this, an artificial intelligence was used to discover 464 types of enzymes from the proteins that were unknown, and the researchers went on to verify the predictions of 3 types of proteins were successfully identified through in vitro enzyme assay.
KAIST (President Kwang-Hyung Lee) announced on the 24th that a joint research team comprised of Gi Bae Kim, Ji Yeon Kim, Dr. Jong An Lee and Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering at KAIST, and Dr. Charles J. Norsigian and Professor Bernhard O. Palsson of the Department of Bioengineering at UCSD has developed DeepECtransformer, an artificial intelligence that can predict the enzyme functions from the protein sequence, and has established a prediction system by utilizing the AI to quickly and accurately identify the enzyme function.
Enzymes are proteins that catalyze biological reactions, and identifying the function of each enzyme is essential to understanding the various chemical reactions that exist in living organisms and the metabolic characteristics of those organisms. Enzyme Commission (EC) number is an enzyme function classification system designed by the International Union of Biochemistry and Molecular Biology, and in order to understand the metabolic characteristics of various organisms, it is necessary to develop a technology that can quickly analyze enzymes and EC numbers of the enzymes present in the genome.
Various methodologies based on deep learning have been developed to analyze the features of biological sequences, including protein function prediction, but most of them have a problem of a black box, where the inference process of AI cannot be interpreted. Various prediction systems that utilize AI for enzyme function prediction have also been reported, but they do not solve this black box problem, or cannot interpret the reasoning process in fine-grained level (e.g., the level of amino acid residues in the enzyme sequence).
The joint team developed DeepECtransformer, an AI that utilizes deep learning and a protein homology analysis module to predict the enzyme function of a given protein sequence. To better understand the features of protein sequences, the transformer architecture, which is commonly used in natural language processing, was additionally used to extract important features about enzyme functions in the context of the entire protein sequence, which enabled the team to accurately predict the EC number of the enzyme. The developed DeepECtransformer can predict a total of 5360 EC numbers.
The joint team further analyzed the transformer architecture to understand the inference process of DeepECtransformer, and found that in the inference process, the AI utilizes information on catalytic active sites and/or the cofactor binding sites which are important for enzyme function. By analyzing the black box of DeepECtransformer, it was confirmed that the AI was able to identify the features that are important for enzyme function on its own during the learning process.
"By utilizing the prediction system we developed, we were able to predict the functions of enzymes that had not yet been identified and verify them experimentally," said Gi Bae Kim, the first author of the paper. "By using DeepECtransformer to identify previously unknown enzymes in living organisms, we will be able to more accurately analyze various facets involved in the metabolic processes of organisms, such as the enzymes needed to biosynthesize various useful compounds or the enzymes needed to biodegrade plastics." he added.
"DeepECtransformer, which quickly and accurately predicts enzyme functions, is a key technology in functional genomics, enabling us to analyze the function of entire enzymes at the systems level," said Professor Sang Yup Lee. He added, “We will be able to use it to develop eco-friendly microbial factories based on comprehensive genome-scale metabolic models, potentially minimizing missing information of metabolism.”
The joint team’s work on DeepECtransformer is described in the paper titled "Functional annotation of enzyme-encoding genes using deep learning with transformer layers" written by Gi Bae Kim, Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering of KAIST and their colleagues. The paper was published via peer-review on the 14th of November on “Nature Communications”.
This research was conducted with the support by “the Development of next-generation biorefinery platform technologies for leading bio-based chemicals industry project (2022M3J5A1056072)” and by “Development of platform technologies of microbial cell factories for the next-generation biorefineries project (2022M3J5A1056117)” from National Research Foundation supported by the Korean Ministry of Science and ICT (Project Leader: Distinguished Professor Sang Yup Lee, KAIST).
< Figure 1. The structure of DeepECtransformer's artificial neural network >
2023.11.24 View 5086