A structure-based deep learning framework for protein engineering
By Raghav Shroff, Austin W Cole, Barrett R. Morrow, D. Diaz, Isaac Donnell, J. Gollihar, A. Ellington, Ross Thyer. 2019
14 citations recorded by the source corpus.
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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Citations in the corpus
- Learning from Protein Structure with Geometric Vector Perceptrons (2020, methodology): “A number of CPD methods (Anand et al., 2020; Zhang et al., 2019; Shroff et al., 2019) and the MQA methods 3DCNN (Derevyanko et al., 2018) and Ornate (Pag ` es et al., 2019) exemplify the power of this approach.”
- Protein sequence design with a learned potential (2020, methodology): “5%), and similar performance to [61] (52.”