Using AlphaFold for Rapid and Accurate Fixed Backbone Protein Design

By Lewis Moffat, Joe G. Greener, David T. Jones. 2021

30 citations recorded by the source corpus.

The prediction of protein structure and the design of novel protein sequences and structures have long been intertwined. The recently released AlphaFold has heralded a new generation of accurate protein structure prediction, but the extent to which this affects protein design stands yet unexplored. Here we develop a rapid and effective approach for fixed backbone computational protein design, leveraging the predictive power of AlphaFold. For several designs we demonstrate that not only are the AlphaFold predicted structures in agreement with the desired backbones, but they are also supported by the structure predictions of other supervised methods as well as ab initio folding. These results suggest that AlphaFold, and methods like it, are able to facilitate the development of a new range of novel and accurate protein design methodologies.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Masked Inverse Folding with Sequence Transfer for Protein Representation Learning (2023, methodology): “More recent methods attempt to invert deep-learning protein structure prediction models (Jendrusch et al., 2021; Moffat et al., 2021; 2022; Anishchenko et al., 2021; Norn et al., 2021; Wang et al., 2021).”
The impact of AlphaFold2 one year on (2022, unknown): “If the database does grow to 130 million entries, is it feasible (or even desirable) to regenerate that amount of data?”
AlphaDesign: A de novo protein design framework based on AlphaFold (2021, methodology): “Designs are then improved using gradient-based [71–73], gradient-free [69, 74, 76] or neural-network based [75] optimisation.”
De novo protein design by inversion of the AlphaFold structure prediction network (2022, methodology): “The first attempts started with an initialized sequence from a generative model and applied a greedy algorithm to optimize the designs for various loss functions (Jendrusch et al., 2021; Moffat et al., 2021).”
Hallucinating protein assemblies (2022, methodology): “…prespecification or experimental confirmation of the structures of the protomers, provided that a suitable loss function could be formulated ( 18 – 20 We set out to broadly explore the space of cyclic protein homo-oligomers by developing a method for hallucinating such structures that places no…”