Duration: 3 min

Jeffery Shi

Protein and Antibody Product Marketing

Jeffrey Shi, Head of Protein and Antibody Product Marketing Team of Marketing Department. He and his team are responsible for customer-centric development of full product life cycle management for Protein and Antibody, and drive the sustainable development of the protein antibody business.

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CONTENTS

• Predicting Stability and Aggregation
• Assessing Immunogenicity
• Optimizing Manufacturability

Enzyme design blends traditional and AI-driven methods to create tailored enzymes for various fields, including medicine and industry. Traditional techniques—such as rational design, directed evolution, and recombinant protein engineering—offer mature, reliable approaches but are often limited by high costs, time demands, and scalability ^[1][2]. In contrast, AI-based methods like machine learning provide efficiency and innovation, although they require extensive data and validation ^[3][4]. Together, these approaches balance precision and adaptability, paving the way for advanced enzyme applications.

Traditional enzyme design methods have been widely used for decades. Thanks to the well-established method, high efficiency, and proven precision, it is able to reliably modify enzyme structures^[1]. However, they also have notable drawbacks: they are time-consuming, costly, and often lack precision, limiting their applicability to a broader range of enzymes. Additionally, traditional techniques rely on trial-and-error experiments to determine feasible modifications, lacking innovations ^[2].

In recent years, AI methods—like machine learning, deep learning, and natural language processing—have introduced a new wave of efficiency and adaptability in enzyme design. These AI-driven techniques can rapidly analyze large datasets to predict enzyme modifications and even design entirely novel enzyme structures, they offer unparalleled speed and a broader range of applications, uncovering potential innovations that traditional methods might miss. But AI approaches still face challenges, such as a heavy reliance on high-quality data and complex algorithms that make it difficult to interpret results ^[3]. The enzymes developed using AI tools also require extensive laboratory validation to confirm their effectiveness.

Together, traditional and AI-based methods complement each other: traditional techniques offer reliability and accuracy, while AI provides speed and creativity. This holds promise for the future of enzyme design, pushing the boundaries of enzyme innovation in ways previously unachievable.

References

[1]. Arnold, F. H. (1998). Design by directed evolution. Accounts of Chemical Research, 31(3), 125-131.

[2]. Lutz, S., & Bornscheuer, U. T. (2009). Protein Engineering Handbook. Wiley-VCH Verlag GmbH & Co. KGaA.

[3]. Yang, Kevin K et al. “Machine-learning-guided directed evolution for protein engineering.” Nature methods vol. 16,8 (2019): 687-694. doi:10.1038/s41592-019-0496-6.

[4]. Jumper, John et al. “Highly accurate protein structure prediction with AlphaFold.” Nature vol. 596,7873 (2021): 583-589. doi:10.1038/s41586-021-03819-2.