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bioRxiv · 10.1101/2021.11.28.470242

Rethinking protein drug design with highly accurate structure prediction of anti-CRISPR proteins

Park, H.-m.·Park, Y.·Vankerschaver, J.·Van Messem, A.·De Neve, W.·Shim, H.
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Abstract

Protein therapeutics play an important role in controlling the functions and activities of disease-causing proteins in modern medicine. Despite protein therapeutics having several advantages over traditional small-molecule therapeutics, further development has been hindered by drug complexity and delivery issues. However, recent progress in deep learning-based protein structure prediction approaches such as AlphaFold opens new opportunities to exploit the complexity of these macro-biomolecules for highly-specialised design to inhibit, regulate or even manipulate specific disease-causing proteins. Anti-CRISPR proteins are small proteins from bacteriophages that counter-defend against the prokaryotic adaptive immunity of CRISPR-Cas systems. They are unique examples of natural protein therapeutics that have been optimized by the host-parasite evolutionary arms race to inhibit a wide variety of host proteins. Here, we show that these Anti-CRISPR proteins display diverse inhibition mechanisms through accurate structural prediction and functional analysis. We find that these phage-derived proteins are extremely distinct in structure, some of which have no homologues in the current protein structure domain. Furthermore, we find a novel family of Anti-CRISPR proteins which are structurally homologous to the recently-discovered mechanism of manipulating host proteins through enzymatic activity, rather than through direct inference. Using highly accurate structure prediction, we present a wide variety of protein-manipulating strategies of anti-CRISPR proteins for future protein drug design.

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Park, H.-m., Park, Y., Vankerschaver, J., Van Messem, A., De Neve, W., Shim, H.. 2021-11-28. Rethinking protein drug design with highly accurate structure prediction of anti-CRISPR proteins. https://doi.org/10.1101/2021.11.28.470242

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