Artificial intelligence facilitates enzyme engineering

Artificial intelligence facilitates enzyme engineering

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Enzyme substrate specificity involving material conversion processes is relevant, for example, to biochemistry, metabolic engineering and environmental remediation; however, it is one of the most difficult tasks in protein engineering. We revealed that the redox cofactor preference of malic enzymes can be strikingly converted by applying phylogenetic analysis to machine learning, without experimental screening. This method can predict mutation positions and candidate amino acids that affect substrate specificity, which is difficult to infer from crystal structures alone. Machine learning uses the amino acid sequences of structurally homologous yet functionally distinct enzymes as input datasets to efficiently navigate to target function and potentially provide fundamental new insights into enzyme-substrate specificity. Credit: 2022, Teppei Niide, Logistic regression-guided identification of residues contributing to cofactor specificity in the enzyme with sequence datasets partitioned by catalytic properties, ACS Synthetic Biology

You can’t move a pharmaceutical scientist from a lab to a kitchen and expect the same research result. Enzymes behave exactly the same way: they depend on a specific environment. But now, in a study recently published in ACS Synthetic Biologyresearchers at Osaka University have imparted an analogous level of adaptability to enzymes, a goal that has remained elusive for more than 30 years.

Enzymes perform impressive functions, made possible by the unique arrangement of their constituent amino acids, but usually only in a specific cellular environment. When you change the cellular environment, the enzyme rarely works well, if at all. Thus, a long-standing research goal has been to maintain or even improve the function of enzymes in different environments; for example, favorable conditions for the production of biofuels. Traditionally, such work has involved a great deal of experimental trial and error that might have little assurance of achieving an optimal result.

Artificial intelligence (a computer tool) can minimize this trial and error, but still relies on experimentally obtained crystal structures of enzymes, which may be unavailable or not particularly useful. So, “the relevant amino acids that one should mutate in the enzyme might just be guesses,” says co-lead author Teppei Niide. “To address this issue, we devised an amino acid ranking methodology that depends solely on the widely available amino acid sequence of analogous enzymes from other living species.”

The researchers focused on the amino acids involved in the specificity of the malic enzyme to the molecule that the enzyme transforms (i.e. the substrate) and to the substance that helps the transformation (i.e. i.e. the cofactor). By identifying amino acid sequences that have not changed during evolution, researchers have identified amino acid mutations that are adaptations to different cellular conditions in different species.

“Using artificial intelligence, we identified unexpected amino acid residues in the malic enzyme that correspond to the enzyme’s use of different redox cofactors,” says co-lead author Hiroshi Shimizu. “This has helped us understand the substrate specificity mechanism of the enzyme and will facilitate optimal engineering of the enzyme in laboratories.”

This work succeeded in using artificial intelligence to significantly accelerate and improve the success of substantially reconfiguring an enzyme’s specific mode of action, without fundamentally altering the function of the enzyme. Future advances in enzyme engineering will greatly benefit fields such as pharmaceutical and biofuel production that require carefully adjusting the versatility of enzymes to different biochemical environments, even in the absence of corresponding enzyme crystal structures.

More information:
Sou Sugiki et al, Logistic regression-guided identification of residues contributing to cofactor specificity in the enzyme with sequence datasets partitioned by catalytic properties, ACS Synthetic Biology (2022). DOI: 10.1021/acssynbio.2c00315

Provided by Osaka University

Quote: Artificial Intelligence Makes Enzyme Engineering Easy (2022, November 3) Retrieved November 3, 2022, from

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