HelixMold SIGNED

Project "HelixMold" data sheet

The following table provides information about the project.

 Partnership

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 Project objective

We propose to computationally design novel ligand binding and catalytically active proteins by harnessing the high thermodynamic stability of de novo helical proteins. Tremendous progress has been made in protein design. However, the ability to robustly introduce function into genetically encodable de novo proteins is an unsolved problem. We will follow a highly interdisciplinary computational-experimental approach to address this challenge and aim to: -Characterize to which extent we can harness the stability of parametrically designed helical bundles to introduce deviations from ideal geometry. Ensembles of idealized de novo helix bundle backbones will be generated using our established parametric design code and designed with constraints accounting for an envisioned functional site. This will be followed by detailed computational, biophysical, crystallographic and site-saturation mutagenesis analysis to isolate critical design features. -Develop a new computational design strategy, which expands on the Crick coiled-coil parametrization and allows to rationally build non-ideal helical protein backbones at specified regions in the desired structure. This will enable us to model backbones around binding/active sites. We will design sites to bind glyphosate, for which remediation is highly needed. By using non-ideal geometries and not relying on classic heptad repeating units, we will be able to access a much larger sequence to structure space than is usually available to nature, enabling us to build more specific and more stable binding/catalytically active proteins. -Investigate new strategies to design the first cascade reactions into de novo designs. This research will allow functionalization of de novo designed proteins with high thermostability, extraordinary resistance to harsh chemical environments and high tolerance for organic solvents and has the potential to revolutionize how proteins for biotechnological and biomedical applications are generated.