mGPfusion: predicting protein stability changes with Gaussian process kernel learning and data fusion

Emmi Jokinen, Markus Heinonen, Harri Lähdesmäki

Abstract: 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 is necessary to facilitate efficient protein design. However, accuracy of predictive models is ultimately constrained by the limited availability of experimental data.