Discovery of Novel Gain-of-Function Mutations Guided by Structure-Based Deep Learning

Despite
the promise of deep learning accelerated protein engineering,
examples of such improved proteins are scarce. Here we report that
a 3D convolutional neural network trained to associate amino acids
with neighboring chemical microenvironments can guide identification
of novel gain-of-function mutations that are not predicted by energetics-based
approaches. Amalgamation of these mutations improved protein function in vivo across three diverse proteins by at least 5-fold.
Furthermore, this model provides a means to interrogate the chemical
space within protein microenvironments and identify specific chemical
interactions that contribute to the gain-of-function phenotypes resulting
from individual mutations.