Computational Design and Experimental Validation of RNA-Based Biosensors for the Detection of Biomarker Proteins
- Restrictions on Access:
- Open Access.
- There is a need to detect medically relevant proteins such as biomarkers of human disease. Aptamers are nucleic acid-based recognition elements that have the ability to selectively bind to a large range of targets with high affinity, including proteins. Specifically, our objective is to engineer RNA-based riboswitches, which use aptamers to bind to a target ligand and thereby alter gene expression. Riboswitches can be potentially engineered as diagnostic sensors for protein targets. Compared to enzyme-linked immunosorbent assays (ELISAs) or surface plasmon resonance (SPR), riboswitch sensors have not been extensively tested for their ability to detect protein biomarkers of human disease. This thesis aims to validate a method of computationally designing riboswitches that can bind to and detect a specific protein of interest. Creating a riboswitch for a specific aptamer can be a slow process without computational modeling. As a result, the riboswitches in these experiments have been designed with the Riboswitch Calculator. Riboswitches, were designed to detect three proteins of interest: MS2 coat protein, C-reactive protein (CRP), and Interleukin-32. Aptamers for these protein targets were identified and used to design the riboswitch sequences. The riboswitches and expression cassettes were tested in a coupled transcription-translation in vitro gene expression system known as TX-TL. Two designed riboswitches successfully detected the MS2 coat protein with statistically significant (p<0.05) activation ratios (ARs) of 3.93 2.18 and 4.90 2.10. The riboswitches designed for CRP and IL-32 did not have statistically significant activation ratios in these experiments, due to insufficient folded protein levels within the TX-TL in vitro assay. The two successfully characterized riboswitches demonstrate that it is possible to computationally design a riboswitch to detect a specific protein in a cost-effective and efficient manner.
- Dissertation Note:
- B.S. Pennsylvania State University, 2018.
- Technical Details:
- The full text of the dissertation is available as an Adobe Acrobat .pdf file ; Adobe Acrobat Reader required to view the file.
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