Analysis of protein activity data by Gaussian stochastic process models

The effects of certain chemical additives at maintaining a high level of activity in protein constructs during storage is investigated. We use a semiparametric regression technique to model the effects of the additives on protein activity. The model is extended to handle categorical explanatory variables. On the basis of the available data, the important factors are estimated to be buffer, detergent, protein concentration, and storage temperature. The relationships among protein activity and these factors appear to be moderately nonlinear with strong interaction effects. These features are revealed in a data-adaptive way by the semiparametric model, without explicit modeling of the nonlinearities or interactions. We use cross-validation to assess the fit of our model. The protein activity response appears to be extremely erratic. We recommend several sets of storage conditions and that further design points be chosen in regions around these estimated optima.