New method IDs up to twice as many proteins and peptides in mass spectrometry data

November 09, 2015

An international team of researchers developed a method that identifies up to twice as many proteins and peptides in mass spectrometry data than conventional approaches. 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 describes their results in the Nov. 9 issue of 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 UC San Diego, 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 collected 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, which analyze pairs of overlapping peptides spectra produced during mass spectrometry experiments. The spectra are produced when an enzyme digests proteins, breaking them down into subcomponents, including peptides. The algorithms detect patterns that the pairs have in common and then look for these patterns in other spectra. This considerably speeds up the process of identifying peptides and by extension, proteins.

Traditional methods compare spectra either against databases or against spectra that have already been identified.

In the Nature Methods study, researchers showed that they were able to identify twice as many peptides in human samples when compared to traditional methods. They also observed 40 percent more protein-on-protein interactions. "The results are more stable and easier to reproduce" when compared to traditional methods, Bandeira said.

Next steps include speeding up the process, which takes roughly twice as long as traditional methods. The method also will 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.
-end-
In addition to Bandeira, authors are Jian Wang of the Center for Computational Mass Spectrometry and the Department of Computer Science and Engineering at UC san Diego; Monika Tucholska, James. D.R. Knight, Jean-Phillipe Lambert and Brett Larsen of the Lunefeld-Tanenbaum Research Institute, Sinai Health Systems, in Toronto, Canada; Stephen Tate of SCIEX, Canada, a company that delivers cutting-edge computational solutions to the mass spectrometry community; and Anne-Claude Gingras, of the Lunefeld-Tanenbaum Research Institute and the Department of Molecular Genetics at the University of Toronto.

MSPLIT-DIA: sensitive peptide identification for data-independent acquisition
Jian Wang, Monika Tucholska, James D R Knight, Jean-Philippe Lambert, Stephen Tate, Brett Larsen, Anne-Claude Gingras and Nuno Bandeira
Nature Methods (2015) doi:10.1038/nmeth.3655, Published online 09 November 2015
http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.3655.html

This work was supported by the US National Institutes of Health (grant 2 P41 GM103484-06A1 from the National Institute of General Medical Sciences to N.B. and J.W.). Bandeira is an Alfred P. Sloan Research Fellow. Gingras is the Canada Research Chair in Functional Proteomics and the Lea Reichmann Chair in Cancer Proteomics. Lambert was supported by a postdoctoral fellowship from CIHR and by a TD Bank Health Research Fellowship at the Lunenfeld-Tanenbaum Research Institute.

University of California - San Diego

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