Computational Chemistry Via AI and Quantum Computing: New Research Finds Optimal Photocatalysts

     The use of AI to find optimal materials and chemicals is proliferating with some notable successes. By modeling and selecting for certain properties, ideal chemicals have been found and tested for certain processes. New research in enzymes, proteins that help one chemical transform into another, is utilizing photochemistry to enable novel enzymatic synthesis through a physical process called charge transfer. According to Sijia Dong, an assistant professor in the department of chemistry and chemical biology at Northeastern University:

"These charge transfers actually happen on electronic excited states," Dong says, meaning that the electrons of the molecule are at higher energy levels. Incorporating these "excited states into the enzyme design strategy" is "something that hasn't been explored much before."

"We found through computational studies that by engineering the protein we can actually modulate or control the electronic structure of these charge transfer complexes," and thus control the reaction.

     The goal is to synthesize photoenzymes more efficiently and more cost-effectively in order to synthesize useful pharmaceuticals more efficiently and cost-effectively. Dong’s work is in developing a computational framework for studying, predicting, and designing photoenzymes. This new approach will inform catalyst design. Catalysts are substances that alter the rates of chemical reactions, in most cases increasing those rates. In chemistry, increasing reaction rates often means saving energy and money on chemical processes. If you follow new research in chemistry, catalysis is a part of the bulk of new discoveries and processes. Instead of randomly testing different protein sequences and mutating them, the desired functions can be found by using machine learning and simulations for prediction, leading to the faster discovery of better chemicals that provide the desired functions. This method is known as computational protein design.

     Wikipedia describes computational chemistry as follows:

“Computational chemistry is a branch of chemistry that uses computer simulations to assist in solving chemical problems. It uses methods of theoretical chemistry incorporated into computer programs to calculate the structures and properties of molecules, groups of molecules, and solids. The importance of this subject stems from the fact that, with the exception of some relatively recent findings related to the hydrogen molecular ion (dihydrogen cation), achieving an accurate quantum mechanical depiction of chemical systems analytically, or in a closed form, is not feasible. The complexity inherent in the many-body problem exacerbates the challenge of providing detailed descriptions of quantum mechanical systems. While computational results normally complement information obtained by chemical experiments, it can occasionally predict unobserved chemical phenomena.”

“Computational chemistry is a tool for analyzing catalytic systems without doing experiments.”

 

Catalysis

     As noted, the search for effective and better catalysts in many chemical reactions is a major focus of much chemical research. Catalysts are of different types: homogenous, where there is one phase, heterogenous, where there are different phases, and autocatalysis, where one of the reaction products acts as the catalyst. In terms of energy, catalysis may be electrical, photochemical, or thermochemical. Catalysts may be positive, increasing reaction rates, or negative, slowing or inhibiting reaction rates, which may be a goal in some processes. Biologically, enzymes act as catalysts. They are very specific to each reaction, and they are very efficient.

     Below is an explanation, summary, and highlights of the paper published in Chem, entitled: Engineering a photocatalyst to use red light:

Photoenzymes are a class of biocatalysts that use photonic energy to drive a chemical transformation. Although nature only has three known photoenzymes, over the past decade, chemists have found that some established enzyme platforms have latent photochemical functions that were previously unknown. In this field, there is a need for general strategies to spectrally tune the photoenzymatic chromophore to enhance the stability of the enzyme and scalability of these reactions.

Inspired by this goal, we engineered a flavin-dependent “ene”-reductase to use red light for a photoenzymatic radical cyclization previously reported with cyan light. By targeting residues located throughout the protein, we optimized the enzyme activity with red light, enabling the transformation to be run on up to a 10-g scale. Our mechanistic studies revealed that protein engineering changes the substrate-binding conformation, resulting in red absorptions. Importantly, mutations at the protein's surface tune the light-absorbing complex, indicating allostery in artificial photoenzymes, a previously unknown phenomenon. This work demonstrates that the photophysical properties of photoenzymes can be tuned using standard directed evolution techniques, an essential advance toward using photoenzymes for chemical manufacturing.

Highlights

•   Spectral tuning of enzyme-templated charge transfer complexes using directed evolution

•   Computational studies show a different electron transition for cyan and red light

•   Mutations at the protein surface allosterically tune the active site complex

•   Engineered enzymes for other radical reactions improve red-light performance

Summary

Photoenzymatic reactions involving flavin-dependent “ene”-reductases (EREDs) rely on protein-templated charge transfer (CT) complexes between the cofactor and substrate for radical initiation. These complexes typically absorb in the blue region of the electromagnetic spectrum. Here, we engineered an ERED to form CT complexes that absorb red light. Mechanistic studies indicate that red-light activity is due to the growth of a red-absorbing shoulder off the previously identified cyan absorption feature. Molecular dynamics simulations, docking, and excited-state calculations suggest that the cyan feature involves a π→π∗ transition on flavin, whereas the red-light absorption is a π→π∗ transition between flavin and the substrate. Differences in the electronic transition are due to changes in the substrate-binding conformation and allosteric tuning of the electronic structure of the cofactor-substrate complex. Microenvironment tuning of the CT complex for red-light activity is observed with other engineered photoenzymatic reactions, highlighting this effect’s generality.

 

 

 

References:

 

Computational chemistry promises to upset traditional methods of chemical synthesis. Noah Lloyd. Phys.org. November 11, 2024. Computational chemistry promises to upset traditional methods of chemical synthesis

Engineering a photoenzyme to use red light. Jose M. Carceller, Bhumika Jayee, Claire G. Page, Gregory D. Scholes, Sijia S. Dong, and Todd K. Hyster. Chem. Volume 11, Issue 2102318. February 13, 2025. Engineering a photoenzyme to use red light: Chem

Catalyst and Catalysis: Types, Examples, Differences. Jyoti Bashyal. Science Info. August 16, 2023. Catalyst and Catalysis: Types, Examples, Differences

Computational chemistry. Wikipedia. Computational chemistry - Wikipedia