Theoretical Framework for Dokdo Macroalgal Die-Off Restoration Using Modified Capybara-Derived CAZymes

This study introduces an innovative theoretical approach to address the macroalgal die-off (gaetnok-eum) issue in the Dokdo marine region resulting from climate change. We propose a framework that optimizes Carbohydrate-Active Enzymes (CAZymes) originating from the gut microbiota of South American capybaras and applies them to laminarin, fucoidan, and alginic acid, the primary constituents of marine seaweeds. This framework explores the potential to efficiently remove dead seaweed debris and promote ecological restoration.Based on the modified equation C_{SuperOpt}^{Dokdo} = C_{WT}^{Capybara} \oplus (\Delta_{Salt} \otimes \Delta_{Temp} \otimes \Delta_{Spec}), various environmental scenarios were analyzed using structure prediction tools (AlphaFold3), energy optimization (Rosetta), binding simulations (AutoDock Vina), molecular dynamics (GROMACS), and temperature-corrected dynamical models.Simulation results indicated that the optimized capybara enzyme variants displayed degradation efficiencies similar to those of local marine microorganisms in Dokdo waters, markedly surpassing other terrestrial-derived enzymes. This establishes a new cross-disciplinary area in which terrestrial microbial resources can aid in the restoration of marine ecosystems. The study highlights the potential for mass production through recombinant technology and its prospects for global application. However, acknowledging the limitations of direct sea urchin control, we recommend the concurrent use of physical interventions as a complementary strategy.Academically, this research expands the horizons of microbial engineering and, in practical terms, contributes to the development of climate-adaptive ecological technologies. Nevertheless, we propose a thorough evaluation of potential ecological and social risks and the implementation of appropriate safety measures. This study is limited to computational predictions, and subsequent experimental validation is essential to confirm its empirical validity.