Rational Design of Memory-Based Sensors: the Case of Molecular Calorimeters.

Thermodynamic characterization is crucial for understanding molecular interactions. However, methodologies for measuring heat changes in small open systems are extremely limited. We document a new approach for designing molecular sensors, that function as calorimeters: sensors based on memory. To design a memory-based sensor, we take advantage of the unique kinetic properties of nucleic acid scaffolds. Particularly, we elaborate on the differences in folding and unfolding rates in nucleic acid quadruplexes. DNA-based i-motifs unfold fast in response to small heats but do not fold back when the system is equilibrated with surroundings. We translated this behavior into a molecular memory function that enables the measurement of heat changes in open environments. The new sensors are biocompatible, operate homogeneously, and measure small heats released over long time periods. As a proof-of-concept, we demonstrate how the molecular calorimeters report heat changes generated in water/propanol mixing and in ligand/protein binding.

Quantitation without Calibration: Response Profile as an Indicator of Target Amount.

Quantitative assessment of biomarkers is essential in numerous contexts from decision-making in clinical situations to food quality monitoring to interpretation of life-science research findings. However, appropriate quantitation techniques are not as widely addressed as detection methods. One of the major challenges in biomarker's quantitation is the need to have a calibration for correlating a measured signal to a target amount. The step complicates the methodologies and makes them less sustainable. In this work we address the issue via a new strategy: relying on position of response profile rather than on an absolute signal value for assessment of a target's amount. In order to enable the capability we develop a target-probe binding mechanism based on a negative cooperativity effect. A proof-of-concept example demonstrates that the model is suitable for quantitative analysis of nucleic acids over a wide concentration range. The general principles of the platform will be applicable toward a variety of biomarkers such as nucleic acids, proteins, peptides, and others.