Initial testing of CC interfaces designed in Chen et. al (Nature, 2019) revealed that certain designed interfaces can become stuck together in unfavorable configurations, preventing the formation of more desirable nanostructures. No binding occurs when these same protein chains were prepared separately and then mixed. Therefore, we focused on designing kinetically reversible heterodimers, that bind in the desirable conformation. We developed a computation pipeline to choose more polar interfaces (27336 interfaces were screened) and experimentally tested the binding of computationally chosen designs using a high throughput split-luciferase assay (50 constructs were tested). We obtained a reversible CC named mALb8.
We rigidly fused CCs to other designed proteins (Brunette et. al, Nature 2015) to form the CC-LEGO blocks. We computationally screened ~490.000 possible arrangements. Due to the expected higher success ratio, most of the CC-Lego blocks were only tested in the context of the higher order assemblies, not individually.
The CC-LEGO blocks were designed into cages using the novel WORMS methodology (Hsia & Mout, Nature Communications, 2021). A promising cage (I05-37) was tested by Dr. Joshua Lubner (Baker Lab). The electron micrograph imaging (and it’s 3D reconstruction) shows an excellent match with the design structure.
We have also focused on developing larger scale structures. Using the rigid fusion methodology, we have created fibres that span several micrometres in length. We have solved the structure of one of the fibre using Cryo-EM. This would not have been possible without the rigidly attached designed repeat proteins that served as markers for the single particle reconstitution. Additionally, we have demonstrated a practical application of the fibers, by attaching heterodimeric binders.
The work has so far been published in four scientific papers (an * indicates shared first or corresponding authorship):
• KOEHLER LEMAN, LYSKOV, LEWIS, …, LJUBETIČ, …, GRAY, BONNEAU. Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks. Nature communications. 29 Nov. 2021
• HUNT, …, LJUBETIČ, …, VEESLER, JEWETT, BAKER. Multivalent designed proteins neutralize SARS-CoV-2 variants of concern and confer protection against infection in mice. Science translational medicine, 2022
• LINDER, LA FLEUR, CHEN, LJUBETIČ, BAKER, KANNAN, SEELIG. Interpreting neural networks for biological sequences by learning stochastic masks. Nature machine intelligence, 2022
• DAVE, MASSARANO, KATZIR, STRMŠEK, LJUBETIČ*, SEMENTA*. EMBO beyond biology: connecting peptide, protein, and DNA design with systems chemistry. Chem, 2022.
At least two further papers are in preparation.
The work has so far been presented at 12 scientific conferences (7 lectures and 5 posters).
Several outreach events were organized, including 4 radio interviews and a Rosetta Workshop “De novo design of proteins using Rosetta and Alphafold 2” (
https://sites.google.com/view/rosettacrashcourse(si apre in una nuova finestra)) with over 60 applicants, proving that there is a lot of interest in protein design at the National Institute of Chemistry (Slovenia) and wider region.
Knowledge transfer to the EU has been successful, for example dr. Ljubetič has installed Rosetta, Alphafold2, ProteinMPNN and the other deep learning software on the computing cluster of National Institute of Chemistry and made them available to all researchers there.