It is well-known that pH (potential of hydrogen) influences enzyme catalytic activity (Schomburg and Salzmann, 1991; Nelson et al., 2021). The pH optimum (pHopt), at which an enzyme displays maximal catalytic activity, is therefore critical for enzyme design and applications (Zhang et al., 2025). To identify suitable enzymes for target pH environments or optimize enzymatic performance in biotechnology, enzyme engineering requires efficient characterization of kinetic properties across large numbers of amino acid sequences. However, experimental determination of pHopt for numerous sequences is time-consuming, labor-intensive, and costly. To address these limitations, computational approaches based on machine learning have been developed for rapid prediction of enzyme pHopt, supporting applications in protein engineering.
Recent advances, exemplified by EpHod (enzyme pH optimum prediction with deep learning) (Gado et al., 2025), enable prediction of the enzyme pHopt directly from protein sequences. The EpHod model leverages embeddings from the protein language model (PLM) ESM-1v and achieves a root mean squared error (RMSE) of 1.25 pH units on the held-out test data (Gado et al., 2025). To further improve predictive performance, an increasing number of AI-driven tools have been developed. For instance, (Zhang et al. 2025) introduced the model VENUS-DREAM, which employs the PLM ESM-2 and reduces the RMSE to 0.809. These AI-powered tools are revolutionizing enzyme discovery and design by enabling high-throughput prediction of pHopt.
Similarly, studies of microbial adaptation to environmental pH frequently require knowledge of enzyme pHopt. This information can be used to investigate the underlying adaptive mechanisms. However, experimental determination of pHopt for large-scale enzyme sequences remains impractical due to high costs and low throughput. Fortunately, the high-throughput predictive capacity of these AI-driven tools offers a powerful alternative for obtaining enzyme pHopt values, which can facilitate investigations into the mechanisms by which microorganisms adapt to environmental pH. The potential applications are illustrated with examples below.
Zhou P., Zhang Z.-X., Pan X., Dai X. & Chen Q., 2026. Leveraging AI-driven predictors of enzyme pH optima to unravel microbial adaptation to environmental pH. Frontiers in Microbiology 17: 1788098. doi: 10.3389/fmicb.2026.1788098. Article.
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