  Stabilizing proteins is a foundational step in protein engineering. However,
the evolutionary pressure of all extant proteins makes identifying the scarce
number of mutations that will improve thermodynamic stability challenging. Deep
learning has recently emerged as a powerful tool for identifying promising
mutations. Existing approaches, however, are computationally expensive, as the
number of model inferences scales with the number of mutations queried. Our
main contribution is a simple, parallel decoding algorithm. Our Mutate
Everything is capable of predicting the effect of all single and double
mutations in one forward pass. It is even versatile enough to predict
higher-order mutations with minimal computational overhead. We build Mutate
Everything on top of ESM2 and AlphaFold, neither of which were trained to
predict thermodynamic stability. We trained on the Mega-Scale cDNA proteolysis
dataset and achieved state-of-the-art performance on single and higher-order
mutations on S669, ProTherm, and ProteinGym datasets. Code is available at
https://github.com/jozhang97/MutateEverything
