Discovery of Novel Gain-of-Function Mutations Guided by Structure-Based Deep Learning
Raghav Shroff, Austin W. Cole, Daniel J. Diaz, Barrett R. Morrow, Isaac Donnell, Ankur Annapareddy, Jimmy Gollihar, Andrew D. Ellington, Ross Thyer
Abstract
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.