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<p>In aqueous solutions containing electrolytes, ions influence both the solubility and the stability of biomolecules. However, inconsistencies across published data highlight the need for a critical review. To address this, a database was constructed on the solubility of glycine in electrolyte solutions spanning from 1996 to 2024, and the experimental data were critically evaluated. Gaussian Process (GP) models were implemented to analyze, predict, and validate solubility behavior. The GP model successfully captures salting-in and salting-out trends, along with specific ion effects reported in the literature. It also provides predictive uncertainty estimates that help identify potentially inconsistent data points or sets. This uncertainty-based analysis enables the reconciliation of conflicting datasets and helps prioritize new experimental measurements in regions where data are sparse or less reliable. By applying a data-filtering method that removes experimental points falling outside the uncertainty range of the model, the influence of inconsistent values is reduced. This results in a more robust model fit and improved prediction accuracy. Therefore, the GP establishes a quantitative foundation for consolidating the current knowledge on the solubility of glycine in saline solutions, identifying methodological inconsistencies in the literature.</p>

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Meta-analysis on the Salt Effect on Glycine Solubility Applying Gaussian Processes

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月4日

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  • Christopher A. Piske,
  • Priscilla G. Leite,
  • Mónia A. R. Martins,
  • Olga Ferreira,
  • João A. P. Coutinho,
  • Dinis O. Abranches,
  • Simão P. Pinho

摘要

In aqueous solutions containing electrolytes, ions influence both the solubility and the stability of biomolecules. However, inconsistencies across published data highlight the need for a critical review. To address this, a database was constructed on the solubility of glycine in electrolyte solutions spanning from 1996 to 2024, and the experimental data were critically evaluated. Gaussian Process (GP) models were implemented to analyze, predict, and validate solubility behavior. The GP model successfully captures salting-in and salting-out trends, along with specific ion effects reported in the literature. It also provides predictive uncertainty estimates that help identify potentially inconsistent data points or sets. This uncertainty-based analysis enables the reconciliation of conflicting datasets and helps prioritize new experimental measurements in regions where data are sparse or less reliable. By applying a data-filtering method that removes experimental points falling outside the uncertainty range of the model, the influence of inconsistent values is reduced. This results in a more robust model fit and improved prediction accuracy. Therefore, the GP establishes a quantitative foundation for consolidating the current knowledge on the solubility of glycine in saline solutions, identifying methodological inconsistencies in the literature.