  Proteins are commonly used by biochemical industry for numerous processes.
Refining these proteins' properties via mutations causes stability effects as
well. Accurate computational method to predict how mutations affect protein
stability are necessary to facilitate efficient protein design. However,
accuracy of predictive models is ultimately constrained by the limited
availability of experimental data. We have developed mGPfusion, a novel
Gaussian process (GP) method for predicting protein's stability changes upon
single and multiple mutations. This method complements the limited experimental
data with large amounts of molecular simulation data. We introduce a Bayesian
data fusion model that re-calibrates the experimental and in silico data
sources and then learns a predictive GP model from the combined data. Our
protein-specific model requires experimental data only regarding the protein of
interest and performs well even with few experimental measurements. The
mGPfusion models proteins by contact maps and infers the stability effects
caused by mutations with a mixture of graph kernels. Our results show that
mGPfusion outperforms state-of-the-art methods in predicting protein stability
on a dataset of 15 different proteins and that incorporating molecular
simulation data improves the model learning and prediction accuracy.
