Overview
Modeling played a central role in the siREN development process, guiding design choices and reducing experimental uncertainty, cost and time. Our challenge required selecting appropriate siRNAs, ensuring their thermodynamic and structural compatibility with the RNAi machinery, and developing a stable, targeted lipid nanoparticle (LNP) delivery system. To address this, we developed and employed complementary computational tools across multiple scales.
- Machine learning for efficacy prediction: We developed and trained a machine learning model on our harmonized dataset (siRBench) to predict candidate siRNA silencing efficacy before experimental validation.
- Thermodynamic stability analysis: We performed thermodynamic analyses using RNAfold Web Server, DuplexFold, and HDOCK to predict siRNA folding and hybridization ΔG values and estimate the stability of siRNA-Ago2 binding.
- Structural modeling: We used AlphaFold to predict the Ago2–siRNA–mRNA ternary complex structure, providing mechanistic insights into strand positioning and binding interactions.
- Delivery system optimization: We modeled lipid nanoparticle components using Avogadro2, analyzed membrane stability with CHARMM-GUI, and employed ROSETTA InterfaceAnalyzer to select antibodies for ROR1 targeting.
The combination of these models enabled us to understand and optimize crucial aspects of siREN: from predicting the best-performing siRNAs, to designing their delivery within a stable, functionalized nanoparticle. Through a combination of data-driven predictions and molecular simulations, our modeling not only supports experimental design but also provides a framework for RNAi-based therapeutic development.
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