Response to "Caution: Fundamental data quality issues underlying intelligent prediction of enzyme optimal pH".

Abstract

We respond to concerns regarding fundamental data quality issues in datasets used for machine learning prediction of enzyme optimal pH (pHopt). We re-examined six questioned protein entries by tracing their annotated pH values to the original literature. Our analysis confirmed substantial discrepancies for several entries, including incorrect experimental values, misassigned protein identities, and proteins lacking the reported catalytic activity, whereas one entry was consistent with the literature. These findings support the need for rigorous verification of biochemical data underlying predictive models. We emphasize that careful literature based curation is essential for improving dataset reliability and model validity, and future iterations of pHopt predictors will be developed using more rigorously validated datasets.