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A topological refactoring design strategy yields highly stable granulopoietic proteins
Julia Skokowa1, Birte Hernandez Alvarez2, Murray Coles2
1Division of Translational Oncology, Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, 72076, Tübingen, Germany. julia.skokowa@med.uni-tuebingen.de.
Nature Communications
|May 26, 2022
Summary
De novo protein design using topological refactoring created novel granulopoietic proteins with nanomolar activity. These engineered proteins exhibit high stability and effectively stimulate neutrophil differentiation in vitro and in vivo.
Area of Science:
- Biochemistry
- Protein Engineering
- Structural Biology
Background:
- Protein therapeutics often suffer from production, stability, and solubility issues.
- De novo protein design offers a promising strategy to overcome these limitations.
Purpose of the Study:
- To demonstrate the utility of topological refactoring for designing novel granulopoietic proteins.
- To create engineered proteins with improved therapeutic properties compared to native granulocyte-colony stimulating factor (G-CSF).
Main Methods:
- Applied topological refactoring to the G-CSF structure by rearranging its sequence and optimizing for a new fold.
- Designed and tested four novel protein candidates.
- Evaluated protein activity, thermostability, protease resistance, structural accuracy, and in vitro/in vivo efficacy.
Main Results:
- Two out of four designed proteins exhibited nanomolar activity.
- The most active design showed high thermostability, protease resistance, and atomic accuracy to its designed structure.
- Engineered proteins, despite sequence and structural differences from native G-CSF, specifically differentiated human hematopoietic stem cells into neutrophils.
- Demonstrated significant and specific in vivo activity.
Conclusions:
- Topological refactoring is an effective strategy for de novo protein design.
- This approach can yield highly stable and active protein therapeutics.
- The method is broadly applicable to various protein targets independent of sequence or structural context.

