New Research Identifies Faster Detection

Current assays are time consuming and labour intensive. Professor Martin Hegner's research shows a more efficient and practical system in detecting the viruses by using micro-sized cantilevers to directly detect viruses binding to membrane proteins.

Micro-cantilevers, which look like springboards are 5 millimetres long and 1 micrometre thick, bend in response to different forces. By measuring changes in the frequencies at which these tiny planks vibrate, researchers have turned them into super-sensitive virus-weighing scales.

Membrane proteins are the most important target for present-day drug discovery programmes. The interactions between transmembrane protein receptors and their ligands are responsible for viral detection and central to medical research. However, measuring these interactions is challenging due to the special architecture and consistency of transmembrane proteins in liquids.

For the first time, Professor of Physics Martin Hegner and his international team have discovered how to perform these measurements in physiological conditions using nanotechnology devices. Their work shows that nanomechanical sensors based on resonating silicon micro-cantilevers can measure such interactions rapidly in such conditions.

The researchers used the protein receptor, FhuA of Escherichia coli known to bind to the T5 virus. Professor Hegner and his colleagues coated the cantilever surfaces with a molecular layer of FhuA proteins sensitised to recognise molecules from the environment. When the array was submerged in a T5 containing fluid, the researchers detected the virus binding to FhuA by measuring shifts in the vibrational frequency of the cantilevers.

Commenting on the significance of the discovery, Professor Hegner said: "These findings could lead to more specific blood tests and also will enable portable diagnostic devices in a hospital environment for a range of testing not just viruses, but also genomic markers and marker proteins."; Source: Trinity College Dublin