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.

doi:10.1101/833905

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bioRxiv · 10.1101/833905

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.

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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.

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Shroff, R., Cole, A. W., Morrow, B. R., Diaz, D. J., Donnell, I., Gollihar, J., Ellington, A. D., Thyer, R.. 2019-11-08. A structure-based deep learning framework for protein engineering. https://doi.org/10.1101/833905

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