Computationally guided strategies to modulate enzyme pH profiles for industrial biocatalysis and environmental...

Enzymes are indispensable biocatalysts; however, their industrial and environmental applications are often constrained due to their narrow pH ranges. This review provides a comprehensive overview of computational-guided strategies for rationally engineering enzyme pH profiles, shifting optima, broadening functional windows, and enhancing acid- or alkaline-tolerance. The mechanistic approaches to engineering pH enzymes, including electrostatic optimization (active-site pK_a tuning, surface charge engineering), stability reinforcement (salt-bridge and hydrogen-bond network design), and dynamics-driven analysis using constant-pH molecular dynamics (CpHMD) and free-energy calculations, have also been discussed herein. Beyond physics-based methods, we highlight the transformative role of data-driven and artificial intelligence approaches, such as machine learning, evolutionary-guided consensus design, and generative protein language models for de novo sequence exploration. By integrating mechanistic biophysical models with AI-driven discovery, hybrid computational workflows are enabling a paradigm shift from retrospective explanation to predictive design. Computational-guided rational design of enzyme engineering provides a synergistic framework that accelerates the development of robust, pH-adapted biocatalysts, paving the way for more sustainable industrial processes and effective environmental technologies.