Research | Michael T. Wolfinger
My research examines how RNA structure shapes biological function, regulation, and design. The work ranges from RNA secondary and tertiary structure prediction to folding kinetics, RNA-protein recognition, synthetic RNA design, and structured viral RNAs.
What connects these areas is a simple observation: many RNA questions are no longer well served by sequence analysis alone. Structural inference, comparative evidence, chemical probing, molecular modelling, and selected machine-learning methods each capture part of the problem. My work is concerned with how these lines of evidence can be used in ways that remain scientifically defensible. RNA structure prediction has been a central theme throughout my work. I am interested in both secondary and tertiary structure, especially in settings where purely sequence-based inference reaches its limits. This includes the use of chemical probing data such as SHAPE, SHAPE-MaP, and DMS-based workflows, as well as comparative evidence in RNA families where structural conservation is clearer than primary-sequence conservation. Folding kinetics is another long-standing focus. Many regulatory RNAs and many designed RNAs cannot be understood from an equilibrium structure alone. They depend on how folding proceeds in time, which alternatives remain accessible, and which metastable states persist long enough to matter. This is particularly relevant in co-transcriptional folding, ligand-controlled systems, and synthetic constructs whose behaviour depends on local alternatives rather than a single dominant fold. These questions lead naturally into synthetic RNA design. I am interested in design problems where structure has to be engineered with a specific use in mind, whether that means preserving accessibility, controlling kinetic behaviour, or deciding which sequence constraints actually matter before experimental work begins. Comparative RNA virology has become a major application area. Much of this work deals with conserved structured elements in flaviviral and related genomes, including xrRNAs, untranslated regions, and long-range interactions. These systems are scientifically interesting in their own right, but they are also useful because they expose cases where structural constraints remain visible even when raw sequence similarity becomes weak. I also work on RNA-protein interactions and structure-guided modelling in systems where RNA recognition cannot be reduced to motif matching alone. This includes molecular modelling and simulation approaches that help connect predicted structures with plausible interaction geometries in biologically concrete systems. Selected projects use machine learning where it provides a clear technical advantage, for example in kinetic approximation or in well-scoped inference problems. In those cases, my interest is less in generic AI claims than in whether a method actually improves what can be said about a specific RNA system. The publication list gives the formal record of this work. Many papers are also discussed in more detail on the blog, where methodological and biological context can be developed more fully than in a publication list alone. My work depends on collaboration with experimental and computational partners across RNA bioinformatics, structural biology, virology, synthetic biology, and related areas. That includes close interaction within my own research group, as well as longer-standing collaborations with colleagues working on structural questions that cut across disciplinary boundaries. Some of the same expertise is also available in workshop or design-review format for groups that need focused external input on RNA structure, chemical probing interpretation, modelling, or computational design strategy. That work is described on the services page.Research Areas
Collaborative Work