Using AI and molecular dynamic simulations to predict and modify enzyme function
Using AI and molecular dynamic simulations to predict and modify enzyme function lead image
Active sites in enzymatic proteins play pivotal roles in their applications to medical and industrial fields. Altering an enzyme’s environment has the potential to change the structure or behavior of its active site, and by extension, its function or performance.
The plant proteolytic enzyme, papain, has a wide range of biological and commercial applications, such as supplements to improve digestion and reduce inflammation and as a component in meat tenderizer. It is also being studied as a possible treatment to slow the growth of cancerous tumors. However, papain’s activity significantly increases at 330 K, while these applications require papain to operate at room temperature or body temperature, which is significantly lower. Identifying the specific sites which fluctuate at its ideal temperature can guide approaches to modify the enzyme for practical use.
Katsuhiko Nishiyama employed machine learning and molecular dynamics simulations to identify potential mutations of papain’s structure that mimicked the high-temperature performance of the enzyme at a lower temperature.
“Papain has two domains that are linked by a hinge structure,” said Nishiyama. “Modifications in the hinge will change the fluctuation of these two domains by changing papain’s temperature characteristics.”
Nishiyama selected two mutation sites within this hinge structure as targets for modification and employed a deep neural network to identify amino acid replacements most likely to achieve the desired effect. Simulation results confirmed 24 mutations that brought active site fluctuations at room temperature closer to those observed at 330 K. The study also indicated that the hinge structure of papain may be stabilized by neutral side chains.
Source: “Exploration of a mutant enzyme protein with active site fluctuations at 330 K via machine learning and molecular dynamics simulations,” by Katsuhiko Nishiyama, AIP Advances (2023). This article can be accessed at https://doi.org/10.1063/5.0172344 .
