Reshaping Phosphatase Substrate Preference for Controlled Biosynthesis Using a “Design–Build–Test–Learn” Framework

Abstract

Abstract Biosynthesis is the application of enzymes in microbial cell factories and has emerged as a promising alternative to chemical synthesis. However, natural enzymes with limited catalytic performance often need to be engineered to meet specific needs through a time‐consuming trial‐and‐error process. This study presents a quantum mechanics (QM)‐incorporated design–build–test–learn (DBTL) framework to rationally design phosphatase BT4131, an enzyme with an ambiguous substrate spectrum involved in N‐acetylglucosamine (GlcNAc) biosynthesis. First, mutant M1 (L129Q) is designed using force field‐based methods, resulting in a 1.4‐fold increase in substrate preference (kcat/Km) toward GlcNAc‐6‐phosphate (GlcNAc6P). QM calculations indicate that the shift in substrate preference is caused by a 13.59 kcal mol−1 reduction in activation energy. Furthermore, an iterative computer‐aided design is conducted to stabilize the transition state. As a result, mutant M4 (I49Q/L129Q/G172L) with a 9.5‐fold increase in kcat‐GlcNAc6P/Km‐GlcNAc6P and a 59% decrease in kcat‐Glc6P/Km‐Glc6P is highly desirable compared to the wild type in the GlcNAc‐producing chassis. The GlcNAc titer increases to 217.3 g L−1 with a yield of 0.597 g (g glucose)−1 in a 50‐L bioreactor, representing the highest reported level. Collectively, this DBTL framework provides an easy yet fascinating approach to the rational design of enzymes for industrially viable biocatalysts.

Keywords

• design–build–test–learn framework
• N‐acetylglucosamine‐6‐phosphate
• phosphatase
• protein engineering
• substrate preference