Chemical ScienceBiophysicsPeer-reviewed journal article
De novo designed 3-helix bundle peptides and proteins with controlled topology and stability
Xiyue LengiD, Katherine I. AlbaneseiD, Lia R. Golub, Arthur A Norman, Jonathan ClaydeniD, Derek N. WoolfsoniD
Abstract
Computational protein design is advancing rapidly. However, approaches and methods are needed to increase success rates and to elaborate designs. Here we describe the combination of rational and computational design to deliver three-helix bundle (3HB) peptide assemblies and single-chain proteins with control over topology and thermal stability. First, we garner sequence-to-structure relationships from antiparallel 3HBs in the Protein Data Bank. This gives core-packing rules, including layers of hydrogen-bonded polar residues, which are combined with surface-charge patterning to design complementary sequences for acidic (A), basic (B), and neutral (N) helices. By altering the design of the N helix, two sets of synthetic peptides are generated for clockwise and anticlockwise arrangements of the three-helix assemblies. Solution-phase characterization shows that both ABN peptide mixtures form stable, heterotrimeric assemblies consistent with the targeted up-down-up topologies. Next, AlphaFold2 models for both designs are used to seed computational designs of single-chain proteins where the helices are connected by loop building. Synthetic genes for these express in E. coli to yield soluble, monomeric, and thermally stable proteins. By systematically introducing additional polar layers within the core, the thermal stability of these proteins is varied without compromising the specificity of the helix-helix interactions. Chemical and thermal denaturation reveals comparable thermodynamic parameters to those of highly stable natural proteins. Four X-ray crystal structures confirm that the design models and AlphaFold2 predictions match to sub-[A] accuracy.
- organism: E. coli
- assay: protein structure
- research focus: protein design
- approach: deep learning
- approach: statistical model
- Citations
- 4
- FWCI
- 1.20
- Field percentile
- top 23%
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