Electrostatics as a Guiding Principle in Understanding and Designing Enzymes

Electrostatics as a Guiding Principle in Understanding and Designing Enzymes

February 27, 2024 | J. Javier Ruiz-Pernía, Katarzyna Świderek, Joan Bertran, Vicent Moliner, and Iñaki Tuñón
The article discusses the role of electrostatic principles in understanding and designing enzymes. It highlights the importance of electrostatic interactions in stabilizing the transition state (TS) and reorganizing the environment to facilitate catalysis. The authors use examples such as dihydrofolate reductases (DHFRs), glycine N-methyltransferase (GNMT), and 20S proteasome to illustrate how electrostatic preorganization and reorganization contribute to catalytic efficiency. They also explore the impact of electric fields on chemical reactivity, particularly in reactions involving dipole moment changes. The article further discusses the application of electrostatic principles in enzyme design, including the design of Kemp eliminase (KE), transforming an esterase into an amidase, and improving the design of poly(ethylene terephthalate) (PET)ases. The authors emphasize the potential of combining molecular simulations with machine learning to guide enzyme design and highlight the challenges and future directions in this field.The article discusses the role of electrostatic principles in understanding and designing enzymes. It highlights the importance of electrostatic interactions in stabilizing the transition state (TS) and reorganizing the environment to facilitate catalysis. The authors use examples such as dihydrofolate reductases (DHFRs), glycine N-methyltransferase (GNMT), and 20S proteasome to illustrate how electrostatic preorganization and reorganization contribute to catalytic efficiency. They also explore the impact of electric fields on chemical reactivity, particularly in reactions involving dipole moment changes. The article further discusses the application of electrostatic principles in enzyme design, including the design of Kemp eliminase (KE), transforming an esterase into an amidase, and improving the design of poly(ethylene terephthalate) (PET)ases. The authors emphasize the potential of combining molecular simulations with machine learning to guide enzyme design and highlight the challenges and future directions in this field.
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[slides and audio] Electrostatics as a Guiding Principle in Understanding and Designing Enzymes