Methods to Improve Enzyme Thermal Stability

Introduction

In industrial enzyme and synthetic biology applications, the thermal stability of enzymes directly determines reaction efficiency, process cost, and product purity, making it one of the c

ore transformation goals in industrial enzyme engineering. However, there are many methods to modify enzyme thermal stability—from rational design to directed

evolution, from semi-rational strategies to AI intelligent design. Each method has its pros and cons, and beginne

rs often don't know where to start. This article systematically dismantles the core principles of enzyme thermal stability modification, the applicable scenarios and selection criteria for four mainstream methods

, and introduces how Shanghai Matwings Technology's MatwingsVenus™ ™ platform uses AI to drive thermal stability modification from "trial and error" to "precise design."

1. Why Enzyme Thermal Stability Is the Core Bottleneck for Industrial Enzymes

Enzyme thermal

stability is usually measured by the melting temperature Tm (melting temperature) or the half-life t₁/₂ at specific temperatures, referring to the enzyme's ability to maintain its three-dimensional structure and catalytic activity under high-temperature cond

itions. For industrial applications, improving the thermal stability of enzymes has multiple benefits: higher reaction temperatures mean faster substrate transformation and shorter production cycles (within the enzyme's active temperature range (below the optimal reaction temperature, every 10°C increase usually increases the enzymatic reaction rate by 1-2 times); Under high-temperature conditions, substrate solubility is higher and the risk of contamination by unwanted bacteria is lower, significantly reducing production costs; In addition, highly stable en

zymes can be adapted to extreme industrial scenarios such as high temperatures, strong acids and bases, or organic solvents.

However, natural enzymes are often "optimized" for physiological conditions (room temperature, normal pressure, neutral pH), and are less stable and prone to deactivation under extreme industrial conditions. Therefore, the modification of enzyme thermal stability has become a required topic in industrial enzyme engineering.

2. Four mainstream methods for enzyme thermal stability modification 

Comparison of the Four Strategies in Four Columns

After decades of development, methods for improving enzyme thermal stability have formed four major technical approaches, each with its own suitable applications, advantages, and disadvantages.

2.1 Rational Design: Structure- and Mechanism-Based Site-Directed Mutagenesis

Rational design is the classic strategy for improving thermal stability—based on the enzyme’s 3D structure and mechanism, specific sites are intentionally chosen for site-directed mutagenesis to enhance stability.

Common rational design strategies include: introducing disulfide bonds (forming covalent cross-links via cysteine pairs to increase structural rigidity), the proline effect (adding proline in flexible loop regions to boost local rigidity), surface charge optimization (adjusting charge distribution to increase salt bridges and hydrogen bonding networks), and hydrophobic core packing (improving internal hydrophobic core density), among others. By 2026, multiple studies had confirmed the effectiveness of computationally assisted rational design in various industrial enzymes such as β-agarase, feruloyl esterase, and chondroitinase. Using consensus predictions from multiple ΔΔG tools and structure-guided screening, they successfully obtained variants with significantly improved thermal stability.

The advantages of rational design are fewer mutations, lower experimental cost, and a clear mechanism. However, it requires high-quality 3D structures and a deep understanding of the enzyme’s mechanism, and the success rate heavily depends on the researcher’s experience, usually resulting in modest single-step Tₘ improvements.

2.2 Directed Evolution: Random Mutagenesis & High-Throughput Screening

Directed evolution mimics natural evolution—introducing random mutations via techniques like error-prone PCR and DNA shuffling to create a mutant library, which is then screened with high throughput to select variants with improved stability. Multiple rounds of iteration accumulate beneficial mutations over time.

The advantage of directed evolution is that it doesn’t require structural information or detailed mechanism understanding, making it widely applicable. It can accumulate the synergistic effects of multiple mutations over several rounds, usually achieving Tₘ improvements of 5-15℃, with exceptional cases reaching 15-20℃. The challenge is that library construction and screening are costly and time-consuming, requiring efficient screening methods, and finding beneficial mutations often depends largely on luck.

2.3 Semi-Rational Design: Focused Mutagenesis Guided by Computation

Semi-rational design is a compromise between rational design and directed evolution—first, computational analysis (conservation analysis, stability prediction, B-factor analysis, etc.) is used to identify sites or regions most likely to affect stability, and then saturated mutagenesis or limited combination is performed at these sites to create a focused library for screening.

The advantage of this method is that it significantly reduces the size of the mutant library, lowering the screening effort from millions to a few thousand or tens of thousands, balancing the discovery power of directed evolution with the efficiency of rational design. Consensus design is also a commonly used semi-rational strategy—by aligning homologous sequences, specific sites in the target enzyme are replaced with conserved amino acids in the family, often significantly improving stability.

2.4 AI-Driven Smart Design

In recent years, deep learning technologies, represented by protein language models (pLM), are reshaping enzyme thermostability engineering. By pretraining on billions of protein sequences, pLMs can learn deep mappings between sequence, structure, and stability, directly predicting the impact of single-point or multi-point mutations on stability (ΔΔG), and further designing optimized sequences.

Compared with traditional methods, AI-driven design has three major advantages: first, no experimental structure is needed and zero-shot prediction can be made from sequence alone; second, it can systematically explore combinatorial mutation space, identifying positively synergistic mutation combinations and overcoming the trade-off between stability and activity; third, it greatly improves efficiency—computational screening can be done in hours, narrowing experimental validation from thousands to dozens of candidates. A study published in Nature Communications in 2025 showed that deep learning-based stability design methods have a significantly higher success rate across multiple enzymes compared to traditional methods.

Quick Guide for Method Selection: If the target Tₘ increase is < 5°C, prioritize rational design; 5-15°C, suitable for semi-rational design or AI pre-screening with a small library validation; > 15°C, AI combinatorial optimization followed by multiple rounds of directed evolution is needed. If the structure is available, select rational/semi-rational paths; if no structure, start from AI prediction.

3. Six FAQs on Enzyme Thermostability Engineering

 Guidance on Decision-Making for the Modification of Enzyme Thermal Stability

Q1: Where do you usually start when modifying enzyme thermal stability?

It depends on what information you have. If you have a high-quality 3D structure and some understanding of the mechanism, you can start with rational design (like introducing disulfide bonds or optimizing surface charges); if you only have sequence information or the structure is unclear, it's recommended to start with a semi-rational approach using AI predictions and testing a small library. AI methods can now provide high-value mutation candidates based solely on the sequence, which can be a good starting point.

Q2: How should you choose between rational design and directed evolution?

A simple rule of thumb: usually, if you want to increase Tₘ by less than 5°C, rational design works; 5-15°C is suitable for semi-rational design; beyond 15°C, you'll need multiple rounds of directed evolution or AI-combined optimization. Rational design is fast but has a low ceiling, directed evolution has a high ceiling but is costly and time-consuming, and AI methods are starting to break this trade-off—by pre-screening computationally, you can drastically reduce experiments while still allowing room for performance jumps from combined mutations.

Q3: Will improving thermal stability affect the enzyme's catalytic activity?

It might. There's sometimes a stability-activity trade-off—too much structural rigidity could reduce the flexibility of the active site, affecting catalytic efficiency. But it's not absolute: by carefully choosing mutation sites (like surface mutations away from the active site or optimizing the core packing), you can improve stability without affecting or even enhancing activity. One big advantage of AI methods is that they can systematically evaluate each mutation's impact on both stability and activity, reducing the risk of "winning the battle but losing the war."

Q4: Can you modify thermal stability without a crystal structure?

Yes. Traditional structure-dependent methods (like FoldX, Rosetta) need high-quality 3D structures, but AI methods based on protein language models can make zero-shot predictions just from sequences, no experimental structure required. For new industrial enzymes without crystal structures, you can first use AI to predict stability mutation sites and then kick off the modification process with experimental verification.

Q5: How can we tell if the improvement after modification is truly thermal stability or just a change in expression level?

This is a common experimental design pitfall. When screening for thermal stability improvements, increased apparent activity could be due to the enzyme itself being more stable or simply due to higher soluble expression. Strict verification requires: ① Measuring Tₘ after purification (e.g., using differential scanning fluorimetry (DSF) or circular dichroism (CD)); ② Measuring specific activity and half-life at different temperatures; ③ Comparing expression levels and solubility between the wild type and the mutant. Only when Tₘ rises and specific activity does not drop can we say it’s a true increase in thermal stability.

Q6: Are AI-predicted mutations reliable? Do we need to verify all of them?

The reliability of AI predictions depends on the model type and the specific task. For single-point stability predictions, current mainstream supervised structure-assisted models usually have Spearman correlation coefficients between 0.6-0.8. Zero-shot sequence models can reach 0.4-0.6 on proteins with plenty of homologous sequences, but the prediction accuracy drops significantly for orphan proteins with very few homologs. Predicting combined mutations is even harder, but iterative rounds (predict → validate → feedback → re-predict) can steadily improve accuracy. In practice, it’s recommended to start by verifying the top 20-50 single-point mutations or 5-10 combinations from the predictions; the hit rate is usually much higher than random library screening.

4. MatwingsVenus™ (XiaoWu™): AI-driven integrated platform for directed evolution

 MatwingsVenus™ Closed-Loop Wet and Dry Working Link

Facing the efficiency bottlenecks of traditional methods, Shanghai Matwings Technology independently developed the MatwingsVenus™ (Xiaowu ™) conversational protein R&D agent, deeply integrating AI-driven protein structure prediction,protein function prediction and design capabilities into the entire directional evolution chain, providing enzyme engineering researchers with an integrated solution from computational prediction to experimental validation.

Self-developed large model-driven multidimensional protein design. The platform centers on the self-developed Venus series of protein large models, trained on a protein sequence dataset worth tens of billions, covering multi-dimensional prediction and optimization design including protein stability, activity, expression levels, and solubility. The Venus series models, released in March 2026, focus on protein-oriented evolution as their core capability. Researchers only need to input the sequence of the target enzyme to quickly obtain high-value mutation candidates sorted by functional enhancement, enabling transformation without structural information.

Directional evolutionary design from single-point scanning to combinatorial optimization. To address the bottleneck of limited improvement in single-point mutations, the platform leverages the large model's sequence-to-function mapping capabilities to optimize multi-point combinational mutation design, assisting in identifying positive collaborative site combinations and overcoming the bottlenecks of traditional methods in combinatorial design. In the transformation project of the sweet protein Monellin, after multiple rounds of AI-guided iterations, several variants achieved sweetness more than ten times higher than the wild variant, and maintained heat resistance at a high range of about 75°C, confirming the practical effectiveness of combination optimization and dry-wet closed-loop solutions.

Conversational interaction lowers the barrier to use. Centered around agents, the platform integrates over 200 protein design tools and more than 30 expert-tuned skills. Users only need to describe their needs in natural language—for example, "Help me scan the mutation sites on this sequence that improve thermal stability and design several multi-point combinations." The agent then automatically schedules the corresponding toolchain to complete the calculations and returns a list of candidate mutations with confidence and functional interpretation. No command-line operations or GPU setup required, allowing researchers with experimental backgrounds to get started quickly.

From prediction to experimentation, the dry and wet closed loop. Even more transformative, the platform directly integrates computational design with automated wet experiments. Predicted high-value mutant sites can be converted into experimental protocols with one click, completing gene synthesis, protein expression, purification, and functional testing through automated shared laboratories. Experimental results are automatically transmitted and guided for the next round of design, forming a closed loop of "prediction-design-validation-iteration." The platform has completed multiple implementation projects in innovative drugs and synthetic biology, including PET degrading enzymes and highly active alkaline phosphatase proteins, which have entered the industrialization stage.

5. Conclusion: From Trial-and-Error to Smart Design Paradigm Upgrade

From the rational design approach of 'one-by-one trials' to the directed evolution method of 'random screening,' and now to AI-driven smart design, the evolution of enzyme thermostability modification methods has always focused on two goals: higher modification efficiency and greater potential for performance improvement. Today, AI methods are breaking the traditional trade-off of 'cannot have both efficiency and performance ceiling'—through computational prescreening and combinatorial optimization, they significantly reduce experimental costs while retaining or even surpassing the performance leap potential of directed evolution.

Shanghai Matwings Technology's Matwings Venus™ (Xiaowu™) platform is driving this paradigm upgrade: it leverages AI-driven directed evolution and stability design as one of its core capabilities, connecting upwards to billion-scale sequence databases and protein large models, and downwards to functional design, automated experimental validation, and dry-wet closed-loop iteration. This compresses the traditional long 'library-building - screening - iteration' R&D cycle into an efficient 'predict-validate-optimize' closed loop.

The next breakthrough in industrial enzyme engineering will belong to researchers who can deeply integrate AI smart design with experimental validation. Matwings Venus™ (Xiaowu™) is providing a unified capability foundation for exactly that kind of research.