According to a new study published in the journal PLOS Computational Biology, researchers can improve on conventional protein analysis methods by using tiny nanopores, which ‘scan’ the proteins as they pass through them.
For years, scientists have sought to use nanopores for the highly efficient analysis of proteins, and now it appears a team of researchers from the University of California San Diego and the University of Notre Dame have finally achieved that goal.
The team’s report described how proteins in a sample are identified based on the unique electrical signal each type of protein generates as it passes through a nanopore. Once the study team’s process is refined, it should allow for large amounts of different kinds of proteins to be analyzed very quickly.
While nanopores are quite useful in analyzing individual molecules, they are less efficient when attempting to define large numbers of (much bigger) proteins in intricate mixtures. Consequently, the standard approach to testing complex protein mixtures involves using mass spectrometry and other methods.
Past efforts to use nanopores in this manner have been confounded by signal noise in the data. Even though some aspects of the technology may have been functional, the inability to separate junk data from valuable data kept nanopore-based protein analysis from becoming a usable method.
"I have been working for almost 10 years now on top-down mass spectrometry, and in comparison with protein identification by top-down mass spectrometry, which by now is almost a mature area, it looked like there was no hope that nanopores could produce a comparable signal,” study author Pavel Pevzner, a computer science and engineering professor at UC San Diego, said in a news release.
The study team was ultimately able to leverage a machine-learning method that could sift through the mountains of data generated by proteins passing en masse through a nanopore sieve, and accurately discriminate useful data from junk data.
“By applying machine learning techniques, we were able to identify distinct signals that could lead to large-scale nanopore protein analysis,” Pevzner said.
The study team said the key to their discovery was a ‘random forest analysis tool’ – a tool that sets up a large number of decision trees and delivers a classification or mean prediction of the individual trees.
“All of a sudden, the structure of the signal emerged,” said Mikhail Kolmogorov, a graduate student working under the tutelage of Pevzner.
In their report, the study team claimed that their method is already accurate enough to compare results with small protein databases.
Progress was also recently announced on another nanopore-based analytical technique known as “flossing” – a method that involves passing a DNA molecule through a nanopore several times and scanning it each time in order to generate more accurate readings. Electronic BioSciences, the San Francisco company behind the technique, recently announced the development of a nanopore structure that would enable detection resolution down to the single DNA nucleotide.
The company said it has been evaluating this enhanced setup on synthesized DNA and the current data indicates individual bases in a DNA strand. The company added that it intends to publish details of testing a single-nucleotide resolution nanopore in a peer-reviewed journal and release the system for use within three years.
Furthermore, the company said it is also planning to market a nanopore-based immunoassay within the next two years. Basically, the device would incorporate the company's signature glass nanopore configuration, but it would be covered with antibodies to evaluate binding and activity of antigens and their antibodies. The instrument could be either a single pore with a single antibody or several pores each with various antibodies attached, the company said.
Nanopore Technology Makes Leap from DNA Sequencing to Identifying Proteins
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