De Novo Enzyme Design for Green Chemistry

De Novo Enzyme Design for Green Chemistry is a groundbreaking scientific technique that involves designing and engineering new enzymes from scratch. This approach is pivotal for Green Chemistry, a field of science that is gaining increasing attention in our quest for sustainable development. By creating catalysts that can drive reactions with greater efficiency, selectivity, and sustainability in comparison to traditional chemical methods, De Novo Enzyme Design contributes to the reduction of hazardous substances, conservation of resources, and overall environmental protection.

De Novo design of food enzymes (Xinglong W., et al., 2023)

Service Process

Here is our organized, step-by-step service process designed specifically for De Novo enzyme design in green chemistry:

1. Initial Consultation: We begin our service process with an in-depth initial consultation. During this stage, we aim to fully comprehend your specific needs and objectives. We believe that understanding our clients' goals is the foundation of developing a successful, personalized solution.
2. Computational Design of New Enzymes: After gaining a clear understanding of your requirements, we move on to the computational design of new enzymes. To ensure the highest level of accuracy and effectiveness, we employ state-of-the-art software tools in this process. Our team of experts meticulously designs and models new enzymes based on your needs.
3. Laboratory Synthesis and Testing: Once we have a robust enzyme design, our team proceeds to synthesize the proposed enzymes in the laboratory. This critical stage involves creating the physical enzyme based on our computer-generated designs.
4. Verification of Enzyme Functionality: Post-synthesis, it is essential to confirm that the new enzymes function as expected. We conduct rigorous testing to verify their functionality, ensuring that they perform their intended roles effectively and efficiently.
5. Final Delivery of Designed Enzyme: After confirming the successful functionality of the new enzymes, we move to the final delivery. We provide you with the designed enzymes, ready for implementation in your specific application.
6. Continuous Support for Implementation: We understand that our responsibility does not end at the delivery of the enzymes. We offer continuous support for the implementation of the designed enzymes in your processes, ensuring they work seamlessly and deliver the desired results.

We're here to assist you. If you have any questions, need more information, or would like to discuss a potential project, please don't hesitate to contact us. Our team is always eager to help and share our expertise.

Applications

Case Study

Luciferase is often regarded as part of green chemistry due to its role as a biocatalyst, enabling chemical reactions under mild conditions, thereby reducing energy consumption and waste production. The figure below illustrates the generation of idealized scaffolds and computational design of de novo luciferases. (a) Family-wide hallucination. Sequences encoding proteins with the desired topology are optimized by Markov chain Monte Carlo (MCMC) sampling with a multicomponent loss function. Structurally conserved regions (peach) are evaluated on the basis of consistency with input residue–residue distance and orientation distributions obtained from 85 experimental structures of NTF2-like proteins, whereas variable non-ideal regions (teal) are evaluated on the basis of the confidence of predicted inter-residue geometries calculated as the KL divergence between network predictions and the background distribution. The sequence-space MCMC sampling incorporates both sequence changes and insertions and deletions to guide the hallucinated sequence towards encoding structures with the desired folds. Hydrogen-bonding networks are incorporated into the designed structures to increase structural specificity. b–d, The design of luciferase active sites. (b) Generation of luciferase substrate (DTZ) conformers. (c) Generation of a Rotamer Interaction Field (RIF) to stabilize anionic DTZ and form hydrophobic packing interactions. (d) Docking of the RIF into the hallucinated scaffolds, and optimization of substrate–scaffold interactions using position-specific score matrices (PSSM)-biased sequence design. (e) Selection of the NTF2 topology. The RIF was docked into 4,000 native small-molecule-binding proteins, excluding proteins that bind the luciferin substrate using more than five loop residues.

De novo design of luciferases (Andy Hsien-Wei Yeh, et al., 2023)