A structure-based deep learning framework for protein engineering
Shroff, R.; Cole, A. W.; Morrow, B. R.; Diaz, D. J.; Donnell, I.; Gollihar, J.; Ellington, A. D.; Thyer, R.
Show abstract
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.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Direct prediction of intrinsically disordered protein conformational properties from sequence 96%
- Megabodies expand the nanobody toolkit for protein structure determination by single-particle cryo-EM 96%
- Engineered allostery in light-regulated LOV-Turbo enables precise spatiotemporal control of proximity labeling in living cells 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.