Integrative Strategies to Enhance Enzyme-Protein Interactions for Drug Discovery and Biocatalysis.

Enzyme-protein interactions (EPIs) are critical targets in both pharmaceutical development and industrial biocatalysis, but their effective modulation has been challenged by high false-positive rates (20-30%) in conventional screening methods and limited accuracy in computational predictions. To address these issues, we have developed an innovative hybrid platform that integrates computational modeling with experimental validation. Our approach combines (1) advanced simulation techniques, including molecular docking, MD simulations, and QM/MM calculations; (2) machine learning-driven candidate prioritization; and (3) high-precision validation methods such as FRET/BRET and SPR. This integrated framework achieved exceptional predictive accuracy, with computational binding energies (ΔG = -8 to -10 kcal/mol) strongly correlating with experimental measurements (K_D = 100-500 nM), while enhancing target specificity by 40% and reducing off-target effects by 30% (p < 0.01). The platform's therapeutic potential was demonstrated through the identification of BRAF V600E inhibitors (predicted ΔG = -9.5 kcal/mol, experimental EC50 = 11 nM), which induced 45% tumor regression in vivo. In industrial applications, our structure-guided engineering approach boosted biofuel and L-lysine production yields by 35% and 28%, respectively, with successful scale-up to 10,000-50,000 L bioreactors. Implemented using open-source computational tools (GROMACS, AutoDock Vina), the platform reduced screening false positives to < 5%, shortened development timelines by 20%, and reduced production costs by 18.6%. The demonstrated success in both biomedical and biotechnological applications establishes our framework as a versatile solution for precision EPI modulation, offering transformative potential for drug discovery and sustainable manufacturing.