bioRxivBioengineeringPreprint
Seed-Guided De Novo Design Expands the Structural Diversity of Antitoxin Protein Binders
Dustin BrittoniD, Dia A. GhoseiD, Jackson C. HalpiniD, Foster BirnbaumiD, Kruthi Gundu, Shaunak RavaliD, Malvina PapanastasiouiD, Steven A. CarriD, Amy E. KeatingiD
Abstract
De novo design of protein binders targeting extended, multi-site interaction surfaces remains difficult for current generative methods, which often produce limited structural diversity and predominantly helical topologies. Here, we advance diffusion-based binder design by guiding protein backbone generation through "seeds," which are PDB-derived fragments selected for geometric complementarity to the target surface. To test this approach, we computationally generated seed-guided binders of the bacterial toxin RelE. RelB, the native antitoxin of RelE, engages two distinct interfaces with high surface complementarity, making it an appropriate test case. Seed-guided RFdiffusion produced backbones with substantially higher structural diversity and more target contacts than RFdiffusion alone. Experimental screening of 1,402 designs in a high-throughput bacterial survival assay identified multiple functional binders, including variants with nanomolar to low-micromolar affinity and one design with RelE neutralization comparable to RelBpep. Computational structure prediction and mutational analyses support that the designed interfaces rely on seed-derived contacts and adopt binding modes distinct from RelB. Molecular dynamics simulations and hydrogen-deuterium exchange experiments further suggest that one high-affinity design undergoes a conformational change upon binding. Notably, successful designs exhibited reduced cross-reactivity to RelE orthologs compared with RelBpep, suggesting that the extensive interfaces generated through seed-guided design can enable enhanced selectivity. These results establish motif scaffolding of surface-complementing seeds as an effective strategy for overcoming current limitations of de novo generative models, enabling the design of proteins that can engage challenging interface sites.
- organism: bacteria (other)
- assay: protein structure
- research focus: protein design
- approach: deep learning
- approach: diffusion model
- Citations
- 0
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