Smart Approach for the Design of Highly Selective Aptamer-Based Biosensors

Aptamers are chemically synthesized single-stranded DNA or RNA oligonucleotides widely used nowadays in sensors and nanoscale devices as highly sensitive biorecognition elements. With proper design, aptamers are able to bind to a specific target molecule with high selectivity. To date, the systematic evolution of ligands by exponential enrichment (SELEX) process is employed to isolate aptamers. Nevertheless, this method requires complex and time-consuming procedures. In silico methods comprising machine learning models have been recently proposed to reduce the time and cost of aptamer design. In this work, we present a new in silico approach allowing the generation of highly sensitive and selective RNA aptamers towards a specific target, here represented by ammonium dissolved in water. By using machine learning and bioinformatics tools, a rational design of aptamers is demonstrated. This "smart" SELEX method is experimentally proved by choosing the best five aptamer candidates obtained from the design process and applying them as functional elements in an electrochemical sensor to detect, as the target molecule, ammonium at different concentrations. We observed that the use of five different aptamers leads to a significant difference in the sensor's response. This can be explained by considering the aptamers' conformational change due to their interaction with the target molecule. We studied these conformational changes using a molecular dynamics simulation and suggested a possible explanation of the experimental observations. Finally, electrochemical measurements exposing the same sensors to different molecules were used to confirm the high selectivity of the designed aptamers. The proposed in silico SELEX approach can potentially reduce the cost and the time needed to identify the aptamers and potentially be applied to any target molecule

Flexible Screen-Printed Amperometric Sensors Functionalized With Spray-Coated Carbon Nanotubes and Electrodeposited Cu Nanoclusters for Nitrate Detection

In this work, we present a novel, sensitive, easy-to-fabricate, flexible amperometric sensor constituted by screen-printed silver (Ag) electrodes functionalized with a copper (Cu) film electrodeposited on top of a spray coated network of single-walled carbon nanotubes (SWCNTs). The Cu/SWCNTs/Ag electrode showed excellent catalytic activity towards the electro-reduction of nitrate ions at neutral pH with a significant increase in cathodic peak currents in comparison with the electrode without SWCNTs (Cu/Ag). The developed Cu/SWCNTs/Ag sensor showed a wide linear detection range from 0.5 μM to 6.0 mM (0.31 mg/l to 372.02 mg/l) with good sensitivity (18.39 μA/mM) and a calculated limit of detection (LOD) of 0.166 nM (10.29 μg/l). It also showed a good selectivity (maximum standard deviation (SD) was 3.25 μA) towards different interfering ions (Fe 2+, Na+, Cu 2+, SO2 4−, CH 3 COO−, Cl−, NO− 2 and HCO− 3 ), as well as good reproducibility, mechanical durability, time and temperature stability. In real sample analysis (tap and river water), the sensor exhibited good agreement with the compared outcome of high-performance liquid chromatography (HPLC) measurements, proving to be a promising analytical tool for the detection of nitrate in water