Optimization of a Reusable, DNA Pseudoknot-Based Electrochemical Sensor for Sequence-Specific DNA Detection in Blood Serum
- 18 December 2008
- journal article
- research article
- Published by American Chemical Society (ACS) in Analytical Chemistry
- Vol. 81 (2) , 656-661
- https://doi.org/10.1021/ac802011d
Abstract
We describe in detail a new electrochemical DNA (E-DNA) sensing platform based on target-induced conformation changes in an electrode-bound DNA pseudoknot. The pseudoknot, a DNA structure containing two stem-loops in which the first stem’s loop forms part of the second stem, is modified with a methylene blue redox tag at its 3′ terminus and covalently attached to a gold electrode via the 5′ terminus. In the absence of a target, the structure of the pseudoknot probe minimizes collisions between the redox tag and the electrode, thus reducing faradaic current. Target binding disrupts the pseudoknot structure, liberating a flexible, single-stranded element that can strike the electrode and efficiently transfer electrons. In this article we report further characterization and optimization of this new E-DNA architecture. We find that optimal signaling is obtained at an intermediate probe density (∼1.8 × 1013 molecules/cm2 apparent density), which presumably represents a balance between steric and electrostatic blocking at high probe densities and increased background currents arising from transfer from the pseudoknot probe at lower densities. We also find that optimal 3′ stem length, which appears to be 7 base pairs, represents a balance between pseudoknot structural stability and target affinity. Finally, a 3′ loop comprised of poly(A) exhibits better mismatch discrimination than the equivalent poly(T) loop, but at the cost of decreased gain. Optimization over this parameter space significantly improves the signaling of the pseudoknot-based E-DNA architecture, leading to the ability to sensitively and specifically detect DNA targets even when challenged in complex, multicomponent samples such as blood serum.Keywords
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