Opportunity Overview
The pivotal challenge is the structural behaviour of RNAs. In proteins, we generally observe a well-defined structure that can be used for drug development. In contrast, RNAs exhibit multiple structures and shift between them dynamically. Describing RNA structure therefore must consider the entire structural ensemble, which pushes state-of-the-art experimental and computational methods to their limit.
Here, we propose a proof-of-principle study to describe the structural ensemble of the RNA frameshifting element (FSE) from SARS-CoV2, then identify binding pockets across the ensemble, and finally find ligands that impact the functional structural changes of FSE. The FSE has multiple conformations that have been identified as functionally important, allowing for functional interference using ligands binding to the RNA.
The structural ensembles for the FSE wild type and some of its variants and mutants will be explored using the energy landscape framework, a computational approach that can resolve RNA structural ensembles in detail. One drawback of this methodology currently is the relatively high computational cost. To overcome this shortcoming, a new methodology will be developed based on machine learning to accelerate a crucial step within the framework.
After binding sites and potential ligands are identified based on the RNA structural ensemble, computational and experimental validation will be sought for predicted RNA-ligand binding. On the computational side, this will include analysis of the changes to the structural ensemble...
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Start FreeSolicitation Details
| Issuing agency | BBSRC |
|---|---|
| Country | United Kingdom |
| Category | Pharmaceuticals |
| Published | December 01, 2025 |
| Procurement stage | Active solicitation |
| Response due | December 01, 2029 |
| Status | Open — accepting responses |
| Official source | View original notice |
| Last verified | August 10, 2026 |
Source: UK Research and Innovation (UKRI) — Open Government Licence v3.0.
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