AlphaDesign: A de novo protein design framework based on AlphaFold

By Michael Jendrusch, Jan O. Korbel, S. Sadiq. 2021

79 citations recorded by the source corpus.

De novo protein design is a longstanding fundamental goal of synthetic biology, but has been hindered by the difficulty in reliable prediction of accurate high-resolution protein structures from sequence. Recent advances in the accuracy of protein structure prediction methods, such as AlphaFold (AF), have facilitated proteome scale structural predictions of monomeric proteins. Here we develop AlphaDesign, a computational framework for de novo protein design that embeds AF as an oracle within an optimisable design process. Our framework enables rapid prediction of completely novel protein monomers starting from random sequences. These are shown to adopt a diverse array of folds within the known protein space. A recent and unexpected utility of AF to predict the structure of protein complexes, further allows our framework to design higher-order complexes. Subsequently a range of predictions are made for monomers, homodimers, heterodimers as well as higher-order homo-oligomers - trimers to hexamers. Our analyses also show potential for designing proteins that bind to a pre-specified target protein. Structural integrity of predicted structures is validated and confirmed by standard ab initio folding and structural analysis methods as well as more extensively by performing rigorous all-atom molecular dynamics simulations and analysing the corresponding structural flexibility, intramonomer and interfacial amino-acid contacts. These analyses demonstrate widespread maintenance of structural integrity and suggests that our framework allows for fairly accurate protein design. Strikingly, our approach also reveals the capacity of AF to predict proteins that switch conformation upon complex formation, such as involving switches from α-helices to β-sheets during amyloid filament formation. Correspondingly, when integrated into our design framework, our approach reveals de novo design of a subset of proteins that switch conformation between monomeric and oligomeric state.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

AlphaFold2 and its applications in the fields of biology and medicine (2023, background): “Jendrusch et al 110 developed a computational framework for de novo protein design that embeds AF2 as an oracle within an optimizable design process.”
De novo protein design by deep network hallucination (2020, methodology): “Our hallucination approach can be readily extended to design new proteins using the recently developed RoseTTAFold 26 and AlphaFold2 27 networks 28,29 .”
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?”
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).”
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).”