Enhancing the Catalytic Activity of Thermococcus kodakarensis RuBisCo via Computer-Aided Rational Design

Area of Science:

• Biochemistry
• Enzyme Engineering
• Computational Biology

Background:

• Ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCo) is a key enzyme for photosynthesis.
• Enhancing RuBisCo's efficiency is crucial for improving crop yields and carbon fixation.
• Current methods for enzyme optimization are often slow and labor-intensive.

Purpose of the Study:

• To utilize machine learning for accelerated optimization of RuBisCo.
• To identify novel mutations that enhance RuBisCo's catalytic activity.
• To understand the cooperative effects of mutations on enzyme function.

Main Methods:

• Employed bmDCA, a machine-learning algorithm, to explore the sequence space of *Thermococcus kodakarensis* RuBisCo (Tk-RuBisCo).
• Conducted rapid design-test cycles with approximately 30 variants.
• Utilized 1H NMR and molecular-dynamics simulations to analyze mutant properties.

Main Results:

• Isolated a double mutant, SP9-Y168L-A361P, with a 1.5-fold higher activity than the previous best mutant.
• Observed strictly additive effects in most bmDCA-designed double mutants, indicating successful identification of cooperative mutation pairs.
• Y168L-A361P mutation significantly enhanced carboxylation over oxygenation rates.
• Simulations revealed that mutations indirectly augment substrate binding via an extended interaction network.

Conclusions:

• Machine learning, specifically bmDCA, offers a powerful and efficient approach for enzyme engineering.
• Second-order coevolutionary information aids in discovering cooperative mutations for enzyme enhancement.
• The Y168L-A361P mutations provide a promising strategy for improving RuBisCo function and photosynthetic efficiency.