  Reliable prediction of protein variant effects is crucial for both protein
optimization and for advancing biological understanding. For practical use in
protein engineering, it is important that we can also provide reliable
uncertainty estimates for our predictions, and while prediction accuracy has
seen much progress in recent years, uncertainty metrics are rarely reported. We
here provide a Gaussian process regression model, Kermut, with a novel
composite kernel for modelling mutation similarity, which obtains
state-of-the-art performance for protein variant effect prediction while also
offering estimates of uncertainty through its posterior. An analysis of the
quality of the uncertainty estimates demonstrates that our model provides
meaningful levels of overall calibration, but that instance-specific
uncertainty calibration remains more challenging. We hope that this will
encourage future work in this promising direction.
