The Znosko Lab

Ribonucleic acid (RNA) plays a central role in cellular function by translating genetic information into proteins and by performing a wide range of regulatory and catalytic activities. Although advances in sequencing technologies have revealed the sequences of countless biologically important RNAs, our understanding of how RNA structure governs function remains limited. This gap is largely due to the scarcity of definitive secondary and tertiary structural information and the difficulty of experimentally characterizing RNA folding at the pace of sequence discovery.

Reliable prediction of RNA secondary and tertiary structure requires a quantitative understanding of the energetic and structural contributions of individual RNA motifs. The Znosko Laboratory addresses this need by investigating the thermodynamics and structural features that govern RNA folding and stability. Our work focuses on defining the energetic parameters and structural properties of discrete RNA motifs and understanding how these elements collectively determine RNA structure and function.

By providing experimentally derived thermodynamic and structural data, our research contributes to the development and improvement of RNA structure prediction models and supports efforts to elucidate RNA structure–function relationships and identify RNA as a therapeutic target. In parallel, we are exploring the use of machine learning, large language models, and artificial intelligence–driven approaches, implemented through Python-based computational tools, to assist with RNA structure prediction and data analysis. These emerging methods are used in conjunction with experimental measurements to enhance model accuracy and to guide hypothesis generation.

The Znosko Laboratory employs an interdisciplinary approach that integrates chemical and biochemical methods with UV–visible spectroscopy, nuclear magnetic resonance spectroscopy, computational modeling, molecular visualization, and AI-enabled analytical tools.

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Research Vision: Our long-term goal is to establish a quantitative, experimentally grounded framework for understanding RNA folding and function. By integrating biophysical measurements with predictive modeling and emerging AI-based approaches, we aim to advance accurate RNA structure prediction and enable the rational targeting of RNA in biology and medicine.