Regression spline models and model calibration in the identification of protein storage conditions

The study of protein-construct activity provides valuable insight in the early stages of drug discovery. Identification of optimal storage conditions that maintain activity of the constructs is an important issue. In the study reported herein, a space-filling design was used to assess the effects of eight design variables--buffer, pH, NaCl, protein concentration, reducing agent, detergent, MgCl2, and temperature--on protein activity. A regression spline analysis is presented, and settings of the explanatory factors resulting in the best predicted protein activity over the explored space are identified. The models are selected initially based on the Akaike information criterion and later assessed via root mean square error and cross-validation root mean square error. Detergent, buffer, temperature, and smooth functions of pH and protein concentration were found to have large effects on protein activity. The models were calibrated via calculation of the empirical distribution of the cross-validation root mean square error. In this framework several models provide a similar fit, and further experimentation is required for more definitive conclusions regarding protein storage conditions to be made. The cross-validation root mean square error calibration of models is recommended for applications involving comparisons of models with respect to their predictive ability.