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Dr. Kristen L. Rhinehardt, North Carolina A&T State University

Computational modeling of nucleic and peptide aptamer interactions – IL6,S100β and GFAP protein biomarkers for traumatic brain injury

Aptasensors utilize aptamers as bioreceptors. Aptamers are highly efficient, with high specificity and reusability. These features, combined with their small size, make them ideal for diagnostics of complex diseases such as traumatic brain injury (TBI). TBI, particularly in the mild form, can be fatal and is often misdiagnosed. An aptasensor for TBI is viable as there are many associated biomarkers. Identification and confirmation of potential biomarker-aptamer binding combinations as well as knowledge of the orientation and location of the aptamer and biomarker during binding could be gained through computational modeling. Present research focused on computational molecular level modeling of nucleic and peptide aptamer interactions, in particular, IL6, S100β, and GFAP biomarkers for TBI. Relevant nucleic and peptide aptamer interactions were studied and analyzed through detailed molecular dynamics modeling. Computational structure prediction and SYBYL-X molecular modeling techniques were employed to construct unknown protein structures and potential aptamers from primary sequences for TBI biomarkers IL6, S100β and GFAP. Simulation results showed potential aptamers for each of these biomarkers studied. Binding peptide aptamers for GFAP and S100β protein biomarkers were identified. Three DNA aptamer possibilities have been identified and modeled for IL6. Visual and quantitative analysis of molecular level interactions aided in ranking these molecules to identify the most viable aptamers.

Surface plasmon resonance imaging (SPRi) experiments corroborated the results from simulated IL6 aptamers; results and comparisons from SPRi experiments for IL6 aptamers are presented and provide a further validation of computational findings. The present research demonstrates the ability of computational molecular dynamics (MD) simulations to obtain molecular level insights for biomarker-aptamer binding including details on the orientation, and location of binding between the biomarker and aptamer that can be instrumental in biosensor development. Detailed MD modeling analysis however is computationally expensive. A preliminary investigation of a reduced order coarse grain modeling approach for aptamer-biomarker interactions was conducted, and provides a feasible computational modeling methodology at reduced computational cost to serve as a pre-screener for potential aptamers. Computational modeling framework established in the present research provides a viable approach for detailed understanding of biomarker-aptamer interactions and binding; one that can be followed for other such aptasensor interaction studies.