Rohit Roy: Thermodynamic prediction of RNA cellular activity from sequence

Abstract: Despite advances in structure prediction from sequence, predicting cellular activity requires conformational ensembles that capture propensities to form functionally active states. Such ensembles remain difficult to measure and even harder to predict. In our study we systematically altered the HIV-1 transactivation response element (TAR) RNA sequence to change its propensity to adopt a functional versus inactive secondary structure and quantified these propensities using proton chemical exchange saturation transfer (1H CEST) NMR without isotopic labelling. Minor sequence changes shifted the active-state propensity by 500-fold, quantitatively predicting 125- to 300-fold changes in binding to the RNA-binding region of Tat and cellular transactivation. These propensities could be inferred from secondary-structure prediction algorithms and incorporated into a thermodynamic framework to quantitatively predict how sequence changes alter protein-binding affinity and cellular activity in this well-characterized system. Our findings establish a quantitative thermodynamic framework that links the RNA sequence to cellular activity through conformational ensembles, setting the stage for more generalized predictions as computational ensemble modelling continues to advance. Speaker: Rohit is a postdoctoral researcher in Phil Holliger’s group at the MRC Laboratory of Molecular Biology in Cambridge, UK. His current work focuses on developing high-throughput sequencing-based approaches to rapidly measure antibody antigen interactions, with the goal of linking antibody sequence to binding and function. He completed his PhD in Computational Biology at Duke & Columbia University in Hashim Al-Hashimi’s group, where he combined computational modelling, structural biology, and NMR spectroscopy to investigate rare, biologically relevant RNA conformational states. More broadly, he is interested in sequence-structure-function relationships and quantitative approaches to measuring biomolecular interactions.