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A semi-automated method to test for enzymatic inhibition through the complexation of ribonuclease A with statistical copolymers
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Selvarajan, Sabarish. A semi-automated method to test for enzymatic inhibition through the complexation of ribonuclease A with statistical copolymers. Retrieved from https://doi.org/doi:10.7282/t3-q73n-qf49

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TitleA semi-automated method to test for enzymatic inhibition through the complexation of ribonuclease A with statistical copolymers
NameSelvarajan, Sabarish (author); Gormley, Adam (chair); Labazzo, Kristen (member); Izgu, Enver (member); Boustany, Nada N (member); Rutgers University; School of Graduate Studies
Date Created2023
Other Date2023-05 (degree)
SubjectPolymer chemistry, Automation, Enzymatic inhibition, Machine learning, NLS, PET-RAFT, Ribonuclease
Extent127 pages : illustrations
DescriptionRibonuclease A (RNase A) is an abundant nucleic acid metabolic enzyme found in almost every eukaryotic and prokaryotic cell type that catalyzes the breakdown of RNA molecules. It is a stable and potent enzyme that hinders the handling of nucleic acids in processes such as gene therapy, gene delivery and for in-vitro diagnostics. To overcome this problem, a semi-automated platform was created to measure the capability of heteropolymers to inhibit the activity of enzyme. The proposed assay implements a linear, reporter oligonucleotide sequence (FRET donor-acceptor pair) that emits energy via fluorescence when cleaved by the enzyme. A preset polymer library was created to later be conjugated with the enzyme and the catalytic capacity was measured for each polymer-enzyme complex using the reporter oligonucleotide as the substrate. Once sufficient data was acquired, an automated data analysis method was developed to measure the enzymatic reaction constant (k) which would be used to determine the polymer’s percentage inhibition, a measure of the polymer’s capability to reduce RNase A’s native activity, the means of which were still uncertain. This could be accomplished by destabilizing the enzyme’s higher order structure, reducing the enzyme’s substrate affinity at the active site, or even through incorporating steric hindrances which, although undesired, will prevent the appropriate enzyme-substrate complexing. The information obtained from the semi-automated experimentation and automated data analysis was then introduced to the pre-existing machine learning algorithm to decipher underlying patterns in terms of polymer composition and chain length. A key takeaway from this process was the importance of experimental consistency, precision, and lack of confounds. The level of confidence in the procured data plays an important role in determining how accurate the ML pipeline is at predicting subsequent generations of polymers compositions that are better suited for the intended purpose.
NoteM.S.
NoteIncludes bibliographical references
Genretheses
Persistent URLhttps://doi.org/doi:10.7282/t3-q73n-qf49
LanguageEnglish
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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