Improving CBCA synthase activity through rational protein design.

Area of Science:

• Biotechnology
• Biocatalysis
• Cannabinoid Research

Background:

• Cannabichromene (CBC) is a minor cannabinoid with emerging pharmaceutical interest.
• Current extraction methods from Cannabis sativa are inefficient for large-scale CBC production.
• Heterologous biosynthesis offers a promising alternative for cannabinoid production.

Purpose of the Study:

• To overcome challenges in biotechnological CBC production by characterizing and improving the CBCA synthase enzyme.
• To predict and validate the crystal structure of CBCA synthase using deep learning.
• To engineer enhanced CBCAS variants with increased catalytic efficiency.

Main Methods:

• Deep learning (AlphaFold) for predicting CBCA synthase crystal structure.
• Structure-function relationship analysis and computational modeling.
• Rational design and screening of enzyme variants for improved activity.

Main Results:

• Deep learning provided a high-quality CBCA synthase structure prediction, superior to homology modeling.
• A novel substrate binding mode for CBCA synthase was identified.
• The best engineered CBCAS variant demonstrated a 22-fold increase in CBCA production from crude lysate.

Conclusions:

• This study presents a validated, deep-learning-derived structure of CBCA synthase, advancing its characterization.
• Engineered CBCAS variants show significantly enhanced activity, paving the way for efficient biotechnological CBC production.
• The findings offer a foundation for optimizing cannabinoid biosynthesis pathways through enzyme engineering.