Abstract Title
Development of a Machine Learning Approach to Predict Effects of Mutational Combinations on RNA Structure
Additional Funding Sources
This project is supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award No. R25GM123927, the National Science Foundation via the Research Experience for Undergraduates Site: Materials for Society (Award Nos. DMR 1658076 and 1950305), and Boise State University.
Abstract
Ribonucleic acid, or RNA, is a cellular structure largely known for carrying instructions from DNA for protein synthesis. This is an important function and with limited access to tools that allow us to predict the structure of RNA, we have turned to RNA enzyme’s known as self cleaving ribozymes to provide a model system for better insight into the relationship between RNA’s structure and function. These self cleaving ribozymes catalyze the cleavage, or cutting, of its own phosphodiester backbone. There are different self cleaving ribozymes, all of which have their own distinct structure and cleavage sites. In this research, we carried out biochemical cleavage reactions on mutated libraries of RNA, we then used high throughput sequencing to determine the resilience of each mutation to the ribozyme structure and function. Here we show relative activity heat maps that show the experimentally measured effects of every single and double mutation regarding the cleavage activity of the HDV and Hammerhead ribozymes. When a second mutation rescues the activity reduction of the single mutation, a base-pair region of RNA is indicated. It’s these ‘rescue’ interactions that provide insight into the structure of the RNA molecule. Additionally, we depict the ribozyme’s secondary structure as a heat map of the measured activity of a single mutation for each nucleotide. This allows us to see if the mutation can be tolerated at each position. We are currently in the process of developing regression analysis models of known sequences with 1-2 mutations to help us predict sequences with 3-4 mutations. This information is pushing us one step closer to being able to predict the structure and activity of previously uncharacterized structured RNA.
Development of a Machine Learning Approach to Predict Effects of Mutational Combinations on RNA Structure
Ribonucleic acid, or RNA, is a cellular structure largely known for carrying instructions from DNA for protein synthesis. This is an important function and with limited access to tools that allow us to predict the structure of RNA, we have turned to RNA enzyme’s known as self cleaving ribozymes to provide a model system for better insight into the relationship between RNA’s structure and function. These self cleaving ribozymes catalyze the cleavage, or cutting, of its own phosphodiester backbone. There are different self cleaving ribozymes, all of which have their own distinct structure and cleavage sites. In this research, we carried out biochemical cleavage reactions on mutated libraries of RNA, we then used high throughput sequencing to determine the resilience of each mutation to the ribozyme structure and function. Here we show relative activity heat maps that show the experimentally measured effects of every single and double mutation regarding the cleavage activity of the HDV and Hammerhead ribozymes. When a second mutation rescues the activity reduction of the single mutation, a base-pair region of RNA is indicated. It’s these ‘rescue’ interactions that provide insight into the structure of the RNA molecule. Additionally, we depict the ribozyme’s secondary structure as a heat map of the measured activity of a single mutation for each nucleotide. This allows us to see if the mutation can be tolerated at each position. We are currently in the process of developing regression analysis models of known sequences with 1-2 mutations to help us predict sequences with 3-4 mutations. This information is pushing us one step closer to being able to predict the structure and activity of previously uncharacterized structured RNA.