Discovery of Novel Gain-of-Function Mutations Guided by Structure-Based Deep Learning.
By Raghav Shroff, Austin W Cole, D. Diaz, Barrett R. Morrow, Isaac Donnell, Ankur Annapareddy, J. Gollihar, A. Ellington, Ross Thyer. 2020
115 citations recorded by the source corpus.
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.
Subfields: Computational Theory and Mathematics, Molecular Biology
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In Computational Theory and Mathematics
- Alexander Rives
- Robert Verkuil
- Russ B. Altman
- Adam Lerer
- Jason Liu
- Po-Ssu Huang
- Tom Sercu
- Zeming Lin
In Molecular Biology
- Namrata Anand
- Raphael R. Eguchi
- Alexander Derry
- Po-Ssu Huang
- Brian L. Hie
- Alexander Rives
- Robert Verkuil
- Russ B. Altman
Citations in the corpus
- Learning inverse folding from millions of predicted structures (2022, methodology): “More recently, convolution-based deep learning methods have also been applied to predict amino acid propensities given the surrounding local structural environments (Anand-Achim et al., 2021; Boomsma & Frellsen, 2017; Shroff et al., 2020; Li et al., 2020; Qi & Zhang, 2020; Zhang et al., 2020; Chen e”
- Protein sequence design with a learned potential (2022, methodology): “…learning methods for design thus far have focused on mutation prediction 25 – 30 , rotamer repacking of native sequences 31 , or amino acid sequence design without modeling side-chain conformers 32 – 35 , with some experimental validation including circular dichroism data 33 and fl uorescence 26 .”