PT  - JOURNAL ARTICLE
AU  - Shroff, Raghav
AU  - Cole, Austin W.
AU  - Morrow, Barrett R.
AU  - Diaz, Daniel J.
AU  - Donnell, Isaac
AU  - Gollihar, Jimmy
AU  - Ellington, Andrew D.
AU  - Thyer, Ross
TI  - A structure-based deep learning framework for protein engineering
AID  - 10.1101/833905
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 833905
4099  - http://biorxiv.org/content/early/2019/11/08/833905.short
4100  - http://biorxiv.org/content/early/2019/11/08/833905.full
AB  - While deep learning methods exist to guide protein optimization, examples of novel proteins generated with these techniques require a priori mutational data. Here we report a 3D convolutional neural network that associates amino acids with neighboring chemical microenvironments at state-of-the-art accuracy. This algorithm enables identification of novel gain-of-function mutations, and subsequent experiments confirm substantive phenotypic improvements in stability-associated phenotypes in vivo across three diverse proteins.