Computational Bioprospecting of Enzymes

Abstract

Computational bioprospecting is revolutionizing enzyme discovery by addressing key challenges associated with traditional laboratory and microbiological methods, such as resource-intensive experimentation and the limited cultivability of microorganisms. This review outlines current in silico methodologies, highlighting their effectiveness in identifying and prioritizing enzymes with desirable expression, stability, and catalytic activity properties. We emphasize recent advancements, including deep learning approaches and AlphaFold-based structure predictions, and discuss their integration with classical molecular mechanics techniques. Through our experiences—such as bioprospecting thermostable oxidases and high-activity laccases—we illustrate practical applications of machine learning, molecular simulations, and synthetic data generation to pinpoint promising enzyme candidates efficiently. Finally, we identify critical gaps, including data scarcity and the need for better integration of multi-omics information, which must be addressed to refine computational approaches in enzyme bioprospecting.

This article is categorized under:

• Structure and Mechanism > Computational Biochemistry and Biophysics
• Data Science > Artificial Intelligence/Machine Learning