Introduction to Enzyme Engineering
Enzyme engineering is a significant tool for fine-tuning enzymes to alter their stability, activity, and specificity. Enzymes are highly versatile macromolecular biological catalysts that can accelerate reaction rates. They are also greatly in demand for replacing or complementing manufacturing processes based on transition metals due to characteristics such as catalytic efficiency and high stereoselectivity under mild conditions.1
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Over the past two decades, the conventional strategy in enzyme design consisted of the “inside out” approach, which includes a transition state in combination with its catalytic residues, or theozyme being put into an existing protein scaffold. 2
The protein scaffold chosen is dependent on its ability to accommodate the theozyme. At the same time, other design tools such as Rosetta or Triad are used to mutate the residues around the theozyme to stabilize the transition state and ensure appropriate protein folding.2
To increase efficacy, improvements through designed catalysts and custom-designed proteins can enable catalysis of reactions that overcome challenges natural enzymes face.2 This includes reactions involving covalent intermediates or complex multi-step transformations that natural enzymes typically do not support.
Protein Engineering
Protein engineering, however, involves the engineering of the enzyme's structure, which leads to a change in its characteristics. A protein that exhibits quantifiable activity can be used as a template for enzyme engineering.1
Various approaches exist for protein engineering, including: (i) the rational approach, (ii) the directed evolution approach, and (iii) the semi-rational approach.1
These approaches vary from site-directed mutagenesis to creating custom-made enzymes to carry out a range of biocatalytic applications, to a combination of the two, whereby the promising target sites for custom-made enzymes are already selected based on the data provided by: the sequence of the protein, the protein structure-function relationship, as well as computational predictive algorithms.1
Tools such as machine learning algorithms, quantum mechanics, and molecular dynamics calculations can be used to observe the effect of amino acid substitutions on the structure and stability of proteins. Altogether, these also offer an innovative prediction platform for modifying protein properties by redesigning enzymes and creating new functions through de novo design.1
Custom-Designed Proteins
Protein design consists of a field of synthetic biology, intending to develop de novo custom-designed proteins and peptides for precise applications. Modern advances in this field for small molecule targeting include conceiving novel or innovative active binding sites, scaffold design, sequence optimization, and experimental expression of the protein being constructed.3
This process explores the capability of producing sequences of specific amino acids with unnatural folds, resulting in novel activities or behaviors.3
Additionally, using computer simulations is a significant strategy for designing sequences and folds and adding new functions before continuing with experimental synthesis.3
Recent Breakthroughs
Recent advancements using deep learning-based protein design include designing scaffolds de novo, which can be suited for their desired reaction. Selecting natural proteins as scaffolds enables some limitations to be overcome, such as destabilization after being incorporated by the theozyme.2
An example includes de novo serine hydrolases that were successfully designed with deep learning tools, with challenges that were addressed to overcome computational enzyme design, including but not limited to the ability to support covalent intermediates, custom-designed generation of reaction-specific scaffolds, and solving stability and expressibility problems.2
This design also included filtering along the entire reaction that previously could be achieved through quantum mechanics/molecular mechanics calculations or molecular dynamics simulations. These require significant expertise and computational resources, making them unsuitable for screening large design sets. Novel techniques can overcome these challenges and are more easily adaptable for many labs.2
Designing complex multi-transition-state enzymes with different strategies has also been explored, such as filtering for multiple steps in the reaction process and implementing a more complex theozyme. However, its readiness for application in every reaction is still not possible.2 Still, these tools represent a significant leap toward computationally guided multi-step biocatalysis that mimics or surpasses natural enzyme pathways.2
With the rapidly growing pharmaceutical industry and a global market of over USD 1000 billion in 2020, drug discovery, development, and production costs are also rising, often with long-term and low returns on investment. Along with this and increasing public concern over more environmentally friendly strategies with low waste, these innovative approaches have become a greater focal point in pharmaceutical manufacturing.2
Examples of contemporary protein engineering include engineered cellulases and hemicellulases, which were made to withstand harsh conditions during biomass pretreatment, including high temperatures and acidity. This enables biofuel production to be more economically feasible and environmentally sustainable, while also increasing the activity and robustness of the enzyme.4
Modified cytochrome P450 and amine oxidase are engineered enzymes created to catalyze challenging reactions within drug synthesis. Within medical applications, boceprevir, a chiral amine, was created using a modified monoamine oxidase enzyme used to treat chronic hepatitis C. The use of this engineered enzyme leads to a significant yield increase by 150%, while reducing water usage by 40%.4
Lactobacillus kefir ketoreductases are engineered enzymes used to catalyze the conversion of a ketone intermediate into a chiral alcohol; ketoreductase-mediated processes are used to produce intermediates for diverse drugs such as montelukast and atorvastatin.4
Emerging Tools
Clustered regularly interspaced short palindromic repeats (CRISPR)-associated transposases (CASTs) are an attractive genome editing tool that allows RNA-guided DNA integration without double-strand breaks.5,6
CASTs are categorized into class 1 and class 2 depending on their CRISPR modules, with these differences resulting in distinguishable transposition mechanisms, including how they target DNA sites, recruit transposases, and mediate DNA insertion. Key features in these structures can be leveraged for CAST optimization and increased engineering.6
CRISPR-CASTs systems work by using operons, a group of genes that create CRISPR ribonucleoparticles (RNPs), which are associated with transposon Tn7-like subunits. The RNPs play a significant role in recognizing target DNA without cleaving it. The CASTs system utilizes a novel RNA-guided transposition mechanism for DNA integration: the inactive nuclease RNPs recognize the DNA target and use the Tn7-like transposition machinery (composed of TnsA, TnsB, TnsC, and TniQ) to target the DNA. This process allows insertion of large pieces of DNA into specific genome areas without creating double-stranded breaks.6
CRISPR-CASTs remove the requirement for homology-directed DNA repair mechanisms and have high precision and efficiency in bacterial genome editing.5,6
Future Outlooks
The CRISPR-CAST systems are an innovative genome editing tool and a promising alternative for synthetic biology, gene therapy, and metabolic engineering applications. However, limited activity has been found in human cells.5,6
Ongoing research includes evolving CAST's systems for higher precision and large-scale genome editing in human cells.6 Recent lab-evolved variants promise to overcome delivery and integration barriers, pointing toward eventual therapeutic applications.5
Meanwhile, with rapid growth in the pharmaceutical industry, the use of enzyme engineering to meet its demands can only be predicted to grow exponentially, alongside the global enzymes market size being valued at USD 13.97 billion in 2024.2,7
References
- Ali M, Ishqi HM, Husain Q (2020). Enzyme Engineering: Reshaping the Biocatalytic Functions. Biotechnology and Bioengineering. ;117(6):1877-1894. doi:10.1002/bit.27329.
- Ingwersen B, Höcker B. (2025). Tackling a Textbook Example of Multistep Enzyme Catalysis with Deep Learning-Driven Design. Molecular Cell. ;85(7):1260-1262. doi:10.1016/j.molcel.2025.03.012.
- Pereira JM, Vieira M, Santos SM. (2021). Step‐by‐Step Design of Proteins for Small Molecule Interaction: A Review on Recent Milestones. Protein Science. ;30(8):1502-1520. doi:10.1002/pro.4098.
- Ndochinwa OG, Wang Q-Y, Amadi OC, et al. (2024). Current Status and Emerging Frontiers in Enzyme Engineering: An Industrial Perspective. Heliyon. ;10(11). doi:10.1016/j.heliyon.2024.e32673.
- Marchal I. (2025). A Laboratory-Evolved CRISPR-Associated Transposase Adapts to Human Cells. Nature Biotechnology. ;43(6):864-864. doi:10.1038/s41587-025-02720-x.
- Tenjo-Castaño F, Rout SS, Dey S, Montoya G. (2025). Unlocking the Potential of CRISPR-Associated Transposons: From Structural to Functional Insights. Trends in Genetics. doi:10.1016/j.tig.2025.04.005.
- Enzymes Market Size, Share, Growth Report 2025-2033. Size, Share, Growth Report 2025-2033. https://www.imarcgroup.com/enzymes-market. Accessed June 29, 2025.