Compute detection of viruses, say researchers
A new multinational study with input from Cambridge University has shown how the process of distinguishing viruses and bacteria could be accelerated through the use of computational methods.
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.
Co-author 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.”
So instead of designing molecular probes that bind strongly to one place on the target DNA researchers should, counterintuitively, design probes that bind weakly all over the target DNA.
Making such relatively short probe sequences is, at present, a standard procedure and the sequences can be ordered online.
The experimental part of the project started with experiments in Cambridge, showing that the method can work in principle on a mixture of viral DNA and colloids coated with short complementary strands.
Then the simulations took over to predict what combination of probe sequences would give the highest selectivity.
This part of the project has so far only been tested in computer models. The next step is to carry out experiments on real mixtures of viral and bacterial DNA.
While the initial work was conducted before the COVID-19 pandemic the current emergency illustrates the need for robust and highly selective methods to quickly identify specific viruses – particularly in ‘low-tech’ environments.
The research was funded in part by the Royal Society and the European Research Council.