De novo protein design by inversion of the AlphaFold structure prediction network
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Abstract
De novo protein design enhances our understanding of the principles that govern protein folding and interactions, and has the potential to revolutionize biotechnology through the engineering of novel protein functionalities. Despite recent progress in computational design strategies, de novo design of protein structures remains challenging, given the vast size of the sequence-structure space. AlphaFold2 (AF2), a state-of-the-art neural network architecture, achieved remarkable accuracy in predicting protein structures from amino acid sequences. This raises the question whether AF2 has learned the principles of protein folding sufficiently for de novo design. Here, we sought to answer this question by inverting the AF2 network, using the prediction weight set and a loss function to bias the generated sequences to adopt a target fold. Initial design trials resulted in de novo designs with an overrepresentation of hydrophobic residues on the protein surface compared to their natural protein family, requiring additional surface optimization. In silico validation of the designs showed protein structures with the correct fold, a hydrophilic surface and a densely packed hydrophobic core. In vitro validation showed that 7 out of 39 designs were folded and stable in solution with high melting temperatures. In summary, our design workflow solely based on AF2 does not seem to fully capture basic principles of de novo protein design, as observed in the protein surface's hydrophobic vs. hydrophilic patterning. However, with minimal post-design intervention, these pipelines generated viable sequences as assessed experimental characterization. Thus, such pipelines show the potential to contribute to solving outstanding challenges in de novo protein design.
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Accessing accurate protein structures paves the way for an enhanced understanding of protein function, providing tools for modulating or engineering proteins.
“Accessing accurate protein structures paves the way for an enhanced understanding of protein function, providing researchers with the tools necessary for modulating these proteins or engineering new ones (8–10).”
From AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences (2024) · cites this paper as [10]
The Introduction (page not available) says that accurate structure prediction can provide a tool for protein design, and the Conclusion describes using such a tool to design new functional proteins. These passages support the engineering portion of the claim, but they do not say that access to accurate structures enhances understanding of protein function or provides tools for modulating existing proteins. (LLM, conf=0.82)
Experimental — accuracy not yet measuredAlphaDesign, an inverted AlphaFold, has been used for de novo protein design with some success.
“An inverted version of AlphaFold, called AlphaDesign, has been used for de novo protein design with some success53”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [53]
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- Authors
- Goverde, C. A., Wolf, B., Khakzad, H., Rosset, S., & Correia, B. E.
- Affiliations
- SIB Swiss Institute of Bioinformatics (CH)
- École Polytechnique Fédérale de Lausanne (CH)
- Corresponding author
- Correia, Bruno E.
- Venue
- Protein Science
- Publisher
- Wiley
- Published
- 2023-05-23
- Volume
- 32
- Issue
- 6
- Pages
- e4653-e4653
- ISSN
- 0961-8368 (ISSN-L)
- 1469-896X
- PMC
- PMC10204179
- OpenAlex
- W4376131109
- Cited by
- 103
- References
- 56
- Citations by year
- 2026: 29 · 2025: 24 · 2024: 34 · 2023: 16
- FWCI
- 9.15
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Protein Science (publishedVersion, other-oa)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Funders
- European Research Council (716058)
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- de novo protein design, AlphaFold2, network inversion, protein folding, hydrophobic surfaces, hydrophobic core, protein stability, in vitro folding, protein engineering
- MeSH terms
- Amino Acid Sequence, Furylfuramide, Proteins, Protein Engineering, Protein Folding
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