The UTC Graduate School is pleased to announce that Godfred Sabbih will present Doctoral research titled, A COMPUTATIONAL APPROACH FOR THE DISCOVERY AND CHARACTERIZATION OF OLIGONUCLEOTIDE APTAMERS FOR PROTEIN TARGETS on 03/10/2023 at 10:00am – 10:40am in ECS 340A. Everyone is invited to attend.
Computational Science
Chair: Dr Michael Danquah
Co-Chair:
Abstract:
Aptamers are short polynucleotide chains that can bind with very high affinities to a broad range of chemical and biomolecular targets, including proteins, cells, viruses, microorganisms, toxins, and chemical compounds. They are analogous to antibodies with applications in therapeutic delivery, biosensors, and diagnostics, but possess the advantages of easy production and lower immunogenicity than antibodies. They are produced by the experimental method known as the systematic evolution of ligands by exponential enrichment (SELEX). SELEX is a powerful experimental method for selecting oligonucleotide aptamers for chemical or biomolecular targets of interest. The SELEX protocol has seen several modifications since its development in 1990, with the primary goal of improving its efficiency and speed. Recent advances to the original SELEX protocol, such as negative SELEX, capillary electrophoresis SELEX, and cell SELEX have increased the efficacy, speed, and target diversity of the original protocol significantly. However, regardless of these advances in the protocol, significant challenges continue to exist, impacting its widespread application for rapid generation of aptamers. Amongst the challenges, reagent consumption, efficiency, and time demand are the major issues for which the present study addressed through in-silico approaches. The developed in-silico approach can be used to augment experimental SELEX protocols for rapid screening of aptamer candidates, providing an integrated method that can speed up the selection process as well as biophysical characterization of aptamer candidates in the discovery process. Concepts and algorithms from machine learning, molecular docking, RNA structure prediction, conventional/enhanced molecular dynamics simulations, and free energy calculations made it possible to develop the in-silico workflow. The work reported in this thesis covers the computational tools, algorithms, and methods developed and implemented to select high-affinity aptamers for SARS-CoV-2 nucleocapsid and Staphylococcus aureus IsdA proteins with excellent binding properties based on small-molecule Fluorescence Resonance Energy Transfer (FRET) measurements.