New, improved method for identifying proteins in MS data

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  • Published: Nov 16, 2015
  • Author: Jon Evans
  • Source: University of California, San Diego
  • Channels: HPLC / Laboratory Informatics / Base Peak / Chemometrics & Informatics

An international team of researchers has developed a method able to identify up to twice as many proteins and peptides in mass spectrometry data as conventional approaches.

Termed MSPLIT-DIA (mixture-spectrum partitioning using libraries of identified tandem mass spectra), the method can be applied to a range of fields, including clinical settings and fundamental biology research for cancer and other diseases. The key to the new method's improved performance is its ability to compare data to so-called spectral libraries – essentially a pattern-matching exercise – rather than individual spectra or a database of sequences.

The team reports this study in Nature Methods. "You can integrate our method with existing pipelines to increase performance by up to three- to four-fold," said Nuno Bandeira, a professor at the Jacobs School of Engineering and the Skaggs School of Pharmacy at the University of California, San Diego and the study's senior author.

The advance is particularly important because many research teams are now switching to an approach known as data-independent acquisition, which captures a wealth of raw data instead of running data analysis on a few elements at random (selected from a distribution based on peptide intensities). The amount of data produced by this approach has created a bottleneck for existing computational tools. "We needed a new data analysis method," said Bandeira.

Bandeira's research group focuses on building spectral network algorithms that analyze pairs of overlapping peptide spectra generated during mass spectrometry experiments. The algorithms detect patterns that the pairs have in common and then look for these patterns in other spectra, which considerably speeds up the process of identifying the peptides and their parent proteins. Traditional methods compare spectra either against databases or against spectra that have already been identified.

In the Nature Methods paper, the researchers report being able to identify twice as many peptides in human samples compared with traditional methods. They also observed 40% more protein-on-protein interactions. "The results are more stable and easier to reproduce" when compared with traditional methods, Bandeira said.

Next steps include speeding up the process, which currently takes roughly twice as long as traditional methods. The method will also have to be fine-tuned for the next generation of mass spectrometers. "We also want to start seeing how we can capitalize on this to analyze a full cohort of data," Bandeira said.

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