Computational RNA Biology and Design for Therapeutics and Biotechnology

The COBRA Lab at the University of Alberta develops computational methods to understand how RNA sequence and structure influence function—and to translate that understanding into testable hypotheses and molecular designs. We combine physics-based modelling, advanced algorithms, structural abstraction, and AI/machine learning to support RNA research, therapeutic development, and biotechnology. Our expertise extends beyond predicting a single RNA structure. We investigate alternative folds and structural ensembles, including complex pseudoknots, to help partners understand RNA behaviour and prioritize candidates for experimental testing. Areas of support include:

• RNA structure prediction and analysis. Predict and compare RNA structures and structural ensembles, assess the potential effects of sequence changes, and incorporate experimental structural information where available. Applications include viral RNA elements, regulatory RNAs, and therapeutic RNA constructs.
• RNA interaction modelling. Investigate RNA–RNA and RNA–DNA interactions, target accessibility, and competing structural arrangements to support antisense oligonucleotide research and other sequence-targeted approaches.
• Computational RNA design. Develop and evaluate candidate sequences for desired structural properties, including applications in RNA therapeutics, vaccine development, aptamers, and functional RNA switches. Assess alternative designs and identify candidates for experimental validation.
• Custom bioinformatics and machine learning. Develop tailored algorithms, predictive models, and reproducible analysis workflows for biological sequence and structure data. Evaluate existing computational tools and adapt methods to project-specific scientific questions. Delivery and outputs: Projects are scoped collaboratively around the partner’s research question, available data, and validation needs. Depending on the project, deliverables may include structural analyses and visualizations, prioritized candidate sequences, customized software or analysis pipelines, and technical reports explaining findings, assumptions, and limitations. We work with experimental teams to connect computational predictions with practical validation strategies. Ideal partners: Biotechnology and pharmaceutical companies, academic laboratories, and translational research teams seeking specialized computational expertise in RNA structure, interactions, and design. We are particularly interested in challenges involving complex RNA structures, multiple functional conformations, or questions that existing prediction tools do not adequately address. Engagements may range from focused feasibility studies to longer-term collaborative research and development. Timelines and project capacity are determined during initial scoping based on scientific complexity, data readiness, and the level of method development required.