Over the course of the project, we focused on the parametric and machine learning based design of helical proteins with backbones custom fit to harbor binding sites for different ligands as well as an active sites for catalyzing enzymatic reactions. For the initial designs, we used computational protocols that are already in place to sample the folding space of alpha-helical proteins computationally. This allowed us to generate hundreds of thousands of potential starting backbones for proteins with a specific function. In subsequent steps, combinatorial amino acid sequences design calculations were used to get low energy amino acid compositions for the respective backbones. From the multitude of computational designs, several were experimentally tested, and some showed the desired activity. Of the ones that showed our envisioned activity, we could experimentally determine their three-dimensional structures using a technique called X-ray crystallography. The determined structures coincide very well with the design models and thus validated our design process. On the way to these goals, we had to achieve and establish a multitude of additional protocols/experiments. Thus, we have now a semi high-throughput pipeline for testing our designed proteins. In addition, we combined recent machine learning based backbone and sequence design approaches with our established parametric design approach, which led to a set of de novo designed biocatalysts for C-C bond cleavage and formation with activities exceeding previously reported designs by several orders of magnitude. Below is a bullet point list of achievements since the project start:
1. Establishing of a novel enzyme design pipeline, which uses diffusion based back bone design approaches to scaffold artificial catalytic motifs. The artificial catalytic motif approach is a direct result of previous parametric design approaches and has been combined with RosettaFold diffusion (RFdiffusion).
2. Design and characterization of more than 200 de novo proteins in total, which either complex copper to achieve nitrate reduction, or quercitin oxidation, amino oxidation and polysaccharide monooxygenation
3. Biochemical and biophysical characterization (CD, DSF, SAXS, MS, X-ray crystallography) of the designs.
4. Design and characterization of de novo proteins that complex ruthenium half-sandwich complexes and their biochemical and biophysical characterization.
5. Expansion of the parametric design code
6. Construction of a GNN which learns atom identities and their distances, angles and torsions to neighboring atoms assess the quality of 1. Loops connecting any two secondary structure elements of variable length; 2. Design models on a per-residue basis, which provides an additional level of design accuracy assessment on top of structure prediction networks like AlphaFold2/3.
7. An antiparallel single walled six helix bundle, a fold that does not occur in nature and catalyzes the as retro-aldol reaction.
8. Design of de novo three helix bundles that harbor a binding site for b-type heme between their symmetric dimer interface were designed and experimentally characterized
