From the Cover of Science to RNApedia: The Future of RNA Design Runs Through Structural Bioinformatics
Artigo "RNApedia: a database of structural protein–RNA interactions"
A study recently published in Science showed that artificial intelligence can now design RNA pseudoknots — structures that have historically been difficult to model — with performance comparable to expert human designers. RNApedia, the protein–RNA interaction database developed by LBS/UFMG, complements this advance by providing the structural data needed for AI to move from designing RNA structure to designing its function and molecular interactions.
A study recently published in Science revealed an impressive breakthrough: artificial intelligence methods are now able to design RNA pseudoknots — complex structures that have historically been difficult to model — achieving performance comparable to that of experienced human designers.
But designing an RNA that adopts the desired structure is only part of the challenge.
For synthetic RNAs to perform functions within biological systems, we also need to understand how these molecules recognize and interact with proteins.
This is precisely where RNApedia comes in, developed by our group at the Laboratório de Bioinformática e Sistemas (LBS/UFMG).
RNApedia currently brings together 56,133 protein–RNA pairs derived from experimental PDB structures, integrating structural and functional information, interatomic contacts, interface areas, RNA modifications, protein domains, and experimental affinity data.
This data can contribute to a new generation of AI models capable of advancing beyond RNA structure design toward something even more ambitious: designing its function and molecular interactions.
As we summarized in our analysis: AI is learning to build RNA architecture. The next challenge is learning how that architecture talks to the protein world. 🧬
🔗 Explore RNApedia: https://bioinfo.dcc.ufmg.br/rnapedia3/ 📄 Science article – De novo design of RNA pseudoknots with deep learning: https://www.science.org/doi/10.1126/science.aek4499
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