Affiliation:
1. School of Automation, Central South University , Changsha, Hunan 410083 , China
2. School of Computer Science and Engineering, Central South University , Changsha, Hunan 410083 , China
3. Department of Biomedical Informatics, Harvard Medical School , Boston , USA
4. Institute of Biology , Westlake Institute for Advanced Study, , Hangzhou, Zhejiang 310024 , China
5. Westlake University , Westlake Institute for Advanced Study, , Hangzhou, Zhejiang 310024 , China
Abstract
AbstractThe design of enzyme catalytic stability is of great significance in medicine and industry. However, traditional methods are time-consuming and costly. Hence, a growing number of complementary computational tools have been developed, e.g. ESMFold, AlphaFold2, Rosetta, RosettaFold, FireProt, ProteinMPNN. They are proposed for algorithm-driven and data-driven enzyme design through artificial intelligence (AI) algorithms including natural language processing, machine learning, deep learning, variational autoencoder/generative adversarial network, message passing neural network (MPNN). In addition, the challenges of design of enzyme catalytic stability include insufficient structured data, large sequence search space, inaccurate quantitative prediction, low efficiency in experimental validation and a cumbersome design process. The first principle of the enzyme catalytic stability design is to treat amino acids as the basic element. By designing the sequence of an enzyme, the flexibility and stability of the structure are adjusted, thus controlling the catalytic stability of the enzyme in a specific industrial environment or in an organism. Common indicators of design goals include the change in denaturation energy (ΔΔG), melting temperature (ΔTm), optimal temperature (Topt), optimal pH (pHopt), etc. In this review, we summarized and evaluated the enzyme design in catalytic stability by AI in terms of mechanism, strategy, data, labeling, coding, prediction, testing, unit, integration and prospect.
Funder
National Natural Science Foundation of China
Hunan Provincial Science and Technology Program
Graduate Innovation Project of Central South University
Publisher
Oxford University Press (OUP)
Subject
Molecular Biology,Information Systems
Cited by
8 articles.
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