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Søren C. Spina

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Review Open access Aug 2026

Current landscape of RNA chemistry and delivery in cancer immunotherapy

Small interfering RNAs (siRNAs) are programmable nucleic acids that play key roles in chemical biology and can selectively silence disease-associated genes through RNA interference (RNAi). These programmable nucleic acids have emerged as a powerful class of medicines and chemical biology tools that can rewire tumor-immune signaling, target immunosuppressive genes, stimulate immune responses, and boost the immune system against immune-mediated diseases. Recent success in the rapid synthesis and applications of siRNA highlights the potential of this technology to address previously “undruggable” targets across a range of genetic, metabolic, and oncologic diseases. Despite the potential of these siRNA-based therapies, including those used in cancer immunotherapy, challenges such as off-target effects during delivery, chemical degradation of siRNA in the body, and immunogenicity limit their efficacy. This review provides a comprehensive overview of the chemical biology and chemical modifications inherent to the design of robust siRNA therapies; the nucleic acid structure–function relationships that dictate the cellular mechanisms underlying siRNA-mediated gene silencing and efficacy; and the current clinical landscape and safety of approved siRNA therapeutics for immunotherapy. We examine the growing role of computationally guided design strategies and emerging machine-learning-based methods in optimizing siRNA chemical design, and outline how recent advances in siRNA chemical modification are expected to improve targeted gene modulation in the clinic. Additionally, we examine the role of delivery systems in enhancing siRNA potency, with an emphasis on tumor-targeted and tissue-specific approaches, as well as emerging combination therapies integrating siRNA with chemotherapy, immune checkpoint blockade, siRNA and mRNA co-delivery, and prodrug activation.

Hayden Tobias, Sarah Porter, Isabella M Marcelo et al. · 0 citations
Open access Jul 2026

Experimentally Tuned Protein-RNA Rosetta Score Function using Bayesian Optimization

Protein-RNA complexes drive fundamental cellular processes such as transcription and translation. Despite the prevalence and importance of protein-RNA interactions, the field lacks reliable and accessible methods to quantify the energetic favorability of these interactions. We propose an experimentally tuned protein-RNA score function that can be directly implemented into ROSETTA. Fine-tuning these score functions for predictive tasks requires repeated evaluations on a set of protein-RNA complexes, which can be computationally expensive given the number of parameters to tune. We used Bayesian Optimization to efficiently improve the energetic agreement between ROSETTA and experimentation. We observe significant interactions for specific RNA subclasses, serving as further confirmation of the physical validity of the score function. Beyond protein-RNA interaction prediction, we establish a framework to efficiently fine-tune ROSETTA score functions for any protein-class interaction using Bayesian Optimization. TOC FIGURE

Joe Bailey, Nathan Phan, Søren C. Spina et al. · 0 citations
Open access Jul 2026

ProteinDock: A physics-informed layer to improve protein-protein docking reliability

It is demonstrated that a truncated version of ProteinDock can be used to choose the optimal prediction among outputs from multiple deep learning-based tools, and shown that this strategy is a computationally efficient alternative to increasing the seed quantity for deep-learning predictions.

G. Rajagopal, Søren C. Spina, Joe Bailey et al. · 0 citations