In the May issue of PLOS Computational Biology, scientists from UC San Diego and the University of Notre Dame report on a study that could open up the field for nanopore-based protein identification – and eventually proteomic profiling of large numbers of proteins in complex mixtures of different types of molecules.
According to UC San Diego computer science and engineering professor Pavel Pevzner, senior author on the paper, the new approach identifies proteins by analyzing the distinct electrical signals produced when the molecules pass through a nanopore (which acts like a sieve). In theory, said Pevzner, nanopores could allow researchers to characterize large numbers of proteins in complex mixtures.
While nanopores work extremely well in analyzing single molecules, they are less effective when trying to characterize large numbers of proteins in complex mixtures. As a result, the currently preferred approach to screening complex mixtures involves using other techniques, notably mass spectrometry. (Pevzner and computer science professors Vineet Bafna and Nuno Bandeira are principal investigators of the NIH-funded Center for Computational Mass Spectrometry at UC San Diego.)
As recently as 2016, leading nanopore developers were pessimistic about being able to apply nanopores to large-scale protein profiling in the near term. “We aren’t even close to doing that at the moment,” Oxford Nanopore co-founder Hagan Bayley told GenomeWeb, adding that he “wouldn’t say it’s an impossible goal, but it is a bit of a stretch.”
UC San Diego’s Pevzner, however, believes that a breakthrough is at hand. “The key is to use machine learning to analyze information generated by proteins when they translocate through a nanopore,” said Pevzner. “By applying…