Molecular Systems BiologyBioinformaticsPeer-reviewed journal article
AlphaDesign: A de novo protein design framework based on AlphaFold
Michael JendruschiD, Alessio Ling Jie YangiD, Elisabetta CacaceiD, Jacob BobonisiD, Carlos Geert Pieter VoogdtiD, Sarah KaspariD, Kristian SchweimeriD, Cecilia Perez-BorrajeroiD, Karine LapougeiD, Jacob ScheurichiD, Kim RemansiD, Janosch HennigiD, Athanasios TypasiD, Jan O. KorbeliD, S. Kashif SadiqiD
Abstract
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 -helices to {beta}-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.
- assay: protein structure
- research focus: protein design
- approach: deep learning
- approach: experimental assay
- Citations
- 36
- FWCI
- 10.16
- Field percentile
- top 1%
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