Compute detection of viruses, says Sidney Fellow and other researchers

The researchers, led by the University of Edinburgh, with colleagues from Cambridge, London, Slovenia and China, used a combination of theoretical and experimental methods to develop a strategy to detect the DNA of infectious diseases. 

The current coronavirus pandemic highlights the need for fast and accurate detection of infectious diseases. Importantly, viral infections like coronavirus and bacterial infections like those associated with antimicrobial resistance (AMR) need to be distinguished.

This is usually done by using a complementary sequence that binds selectively to the genome of interest. Normally, this is done by targeting a single, long DNA sequence that is unique to the pathogen.

However, the researchers believe that much higher selectivities can be achieved by simultaneously targeting many shorter sequences that occur with a higher frequency in the pathogen of interest than in the DNA of other organisms that may be present in the patient samples.

Sidney Fellow and co-author of the study Professor Erika Eiser from Cambridge’s Cavendish Laboratory said: “This approach exploits a phenomenon called ‘multivalency’, and the extensive numerical calculations, based on real bacterial and viral DNA sequences show that this approach should significantly outperform current approaches.

“Even though the individual shorter sequences bind more weakly to the target DNA than a single, longer sequence, the strength of the multivalent binding increases much faster than linearly with the number of short sequences.”