ArticleProtein science : a publication of the Protein Society2022
Assessing and enhancing foldability in designed proteins.
Article in Protein science : a publication of the Protein Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Article
- From CASP13 to the Nobel Prize: DeepMind's AlphaFold Journey in Revolutionizing Protein Structure Prediction and Beyond.Current protein & peptide science · 2026Review
- Article
- Structure-guided design of a bivalent SARS-CoV-2 mRNA vaccine with NTD stabilizing mutations enhances broad immunity.Frontiers in immunology · 2025Article
- Addressing epistasis in the design of protein function.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- De novo design of knotted tandem repeat proteins.Nature communications · 2023Article
- AlphaFold, allosteric, and orthosteric drug discovery: Ways forward.Drug discovery today · 2023Review
- De novo protein design by inversion of the AlphaFold structure prediction network.Protein science : a publication of the Protein Society · 2023Article
- AlphaFold2 and its applications in the fields of biology and medicine.Signal transduction and targeted therapy · 2023Review
- Assessing and enhancing foldability in designed proteins.Protein science : a publication of the Protein Society · 2022Article
- What Have We Learned from Design of Function in Large Proteins?Biodesign research · 2022Review
- A Method for Assessing the Robustness of Protein Structures by Randomizing Packing Interactions.Frontiers in molecular biosciences · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advances in protein-design methodology have led to a dramatic increase in reliability and scale. With these advances, dozens and even thousands of designed proteins are automatically generated and screened. Nevertheless, the success rate, particularly in design of functional proteins, is low and fundamental goals such as reliable de novo design of efficient enzymes remain beyond reach. Experimental analyses have consistently indicated that a major reason for design failure is inaccuracy and misfolding relative to the design conception. To address this challenge, we describe complementary methods to diagnose and ameliorate suboptimal regions in designed proteins: first, we develop a Rosetta atomistic computational mutation scanning approach to detect energetically suboptimal positions in designs (available on a web server https://pSUFER.weizmann.ac.il); second, we demonstrate that AlphaFold2 ab initio structure prediction flags regions that may misfold in designed enzymes and binders; and third, we focus FuncLib design calculations on suboptimal positions in a previously designed low-efficiency enzyme, improving its catalytic efficiency by 330-fold. Furthermore, applied to a de novo designed protein that exhibited limited stability, the same approach markedly improved stability and expressibility. Thus, foldability analysis and enhancement may dramatically increase the success rate in design of functional proteins.
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