ArticleProtein science : a publication of the Protein Society2023
De novo protein design by inversion of the AlphaFold structure prediction network.
Article in Protein science : a publication of the Protein Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
- HighFold4: extending AlphaFold3 to accurate cyclic peptide conformation prediction via custom chemical connectivity.Briefings in bioinformatics · 2026Article
- In silico pharmacological evaluation of a multi-epitope vaccine candidate targeting the G1 and G2 glycoproteins of Oropouche virus via TLR3 interaction.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Generative AI for controllable protein sequence design: A survey.npj drug discovery · 2026Review
- Efficient generation of epitope-targeted antibodies with Germinal.Nature biotechnology · 2026Article
- Tools For Building Artificial Biological Nanostructures.ACS nano · 2026Review
- Limitations of the refolding pipeline for de novo protein design.Protein science : a publication of the Protein Society · 2026Article
- Review
- Evaluation of AI-Predicted GH11 Xylanase Models Against a Previously Unreported Experimental Structure: Implications for Conformational Accuracy and Ligand Binding.International journal of molecular sciences · 2026Article
- Protein engineering: status report.Protein engineering, design & selection : PEDS · 2026Review
- Assessing the validity of leucine zipper constructs predicted by AlphaFold.Protein science : a publication of the Protein Society · 2026Article
- Protein Structure Prediction Methods.Advances in experimental medicine and biology · 2026Review
- Mass spectrometry integrates protein design into structural biology method development.QRB discovery · 2026Review
- From CASP13 to the Nobel Prize: DeepMind's AlphaFold Journey in Revolutionizing Protein Structure Prediction and Beyond.Current protein & peptide science · 2026Review
- Large scale prospective evaluation of co-folding across 557 Mac1-ligand complexes and three virtual screens.bioRxiv : the preprint server for biology · 2025Article
- Stable de novo protein design via joint conformational landscape and sequence optimization.Nature communications · 2025Article
- Training bias and sequence alignments shape protein-peptide docking by AlphaFold and related methods.Protein science : a publication of the Protein Society · 2025Article
- Design of proteins by parallel tempering in the sequence space.Protein science : a publication of the Protein Society · 2025Article
- Article
- Efficient generation of epitope-targetedbioRxiv : the preprint server for biology · 2025Article
- AlphaDesign: a de novo protein design framework based on AlphaFold.Molecular systems biology · 2025Article
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5 authors.
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No grant is acknowledged in the PubMed record.
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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