Poster Presentation 51st Lorne Proteins Conference 2026

De novo design of protein inhibitors and emergence of a scalable AI-driven protein design pipeline (#415)

Cyntia Taveneau 1 , Rebecca S Bamert 1 2 , Nathan H Chai 2 , Jovita D Silva 2 , Honglin (Kevin) Chen 3 4 , Brooke K Hayes 2 , Roland W Calvert 2 , Jacob Purcell 2 , Daniel J Curwen 5 , Fabian Munder 6 , Lisa L Martin 7 , Jeremy J Barr 8 , Sefi Rosenbluh 2 , Mohamed Fareh 3 4 , Andrew Perry 1 2 , Rhys Grinter 1 6 9 , Gavin J Knott 1 2
  1. AI Protein Design Program AIPDP, Monash University, Clayton, VIC, Australia
  2. Department of Biochemistry & Molecular Biology, Monash Biomedicine Discovery Institute, Clayton, VIC, Australia
  3. Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
  4. Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, VIC, Australia
  5. School of Chemistry, Monash University, Clayton, VIC, Australia
  6. Department of microbiology, Monash University, Clayton, VIC, Australia
  7. School of Chemistry, Monash University, Clayton, Victoria, Australia
  8. School of Biological Sciences, Monash University, Clayton, VIC, Australia
  9. Department of Biochemistry and Pharmacology, Bio21 Molecular Science and Biotechnology Institute, Parkville, VIC, Australia

Precise control over CRISPR systems is critical for both biological discovery and therapeutic translation, yet natural anti-CRISPR proteins are sparse, particularly for RNA-targeting Type VI Cas13 enzymes[1-3]. To address this gap, we designed potent protein inhibitors of Cas13, demonstrating that artificial intelligence–guided protein design can replace discovery-based approaches for generating functional regulators of complex biomolecular machines[4].

Using structure-guided diffusion models combined with sequence optimization, we designed small, stable protein inhibitors targeting the HEPN nuclease site of Leptotrichia buccalis Cas13a, a system for which no natural inhibitors are known. From a focused experimental screen, multiple designs exhibited strong inhibitory activity, with lead candidates achieving low-nanomolar potency in vitro. Biochemical and structural analyses confirmed competitive inhibition of the catalytic site without disruption of guide RNA binding. These AI-designed anti-CRISPRs function in bacterial phage-defence assays and in mammalian cells, restoring gene expression while maintaining favourable biophysical properties.

Beyond the immediate biological outcome, this work served as a foundational demonstration for the establishment of the AI-Protein Design Program (AIPDP) at Monash University. Building on this initial success, AIPDP has evolved into a scalable, modular pipeline integrating binder design, experimental screening, and mechanistic validation, supporting diversification across protein architectures, binding modes, and applications[5].

  1. Meeske, A.J., et al., A phage-encoded anti-CRISPR enables complete evasion of type VI-A CRISPR-Cas immunity. Science, 2020. 369(6499): p. 54–59. 2. Katz, M.A., et al., Diverse viral cas genes antagonize CRISPR immunity. Nature, 2024. 634(8034): p. 677–683. 3. Wandera, K.G., et al., AcrVIB1 inhibits CRISPR-Cas13b immunity by promoting unproductive crRNA binding accessible to RNase attack. Mol Cell, 2025. 85(6): p. 1162–1175 e7. 4. Taveneau, C., et al., De novo design of potent CRISPR-Cas13 inhibitors. BiorXiv, 2024. 5. Monash BDI AI-Protein Design Program website. Available from: https://www.monash.edu/discovery-institute/research/ai-protein-design-program.