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posted by janrinok on Friday November 25 2016, @05:38AM   Printer-friendly
from the fighting-back dept.

Each year more than 2 million people develop antibiotic resistance in the United States, and researchers hope their work will help identify new antibiotics to effectively treat diseases.

"This is the first time that we are using Big Data to look into microbial chemistry and characterize antibiotics and other drug candidates," said Hosein Mohimani, a computer scientist at the University of California San Diego and the paper's first author. "Although proteomics researchers have been routinely using huge spectral datasets to find important peptides, all traditional proteomics tools fail when it comes to new drug discovery. "

The algorithms the researchers developed scour mass-spectrometry data to discover so-called peptidic natural products (PNPs) -- widely used bioactive compounds that include many antibiotics.

Mass spectrometry allows researchers to identify the chemical structure of a substance by separating its ions according to their mass and charge. By running mass spectrometry data against a database of chemical structures of known antibiotics, the researchers were able to detect known compounds in substances that had never been analyzed before.

This is the first time that this kind of Big Data analysis was possible. The researchers were able to get around the well-known issue of false positives by using statistical analysis to determine the significance of each match between spectra and the antibiotics database. "We got the idea from particle physics," Mohimani said. Researchers used a statistical approach called the Markov Chain Monte Carlo to compute the probability of rare events and to throw false positives out.

Abstract.


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  • (Score: -1, Offtopic) by Anonymous Coward on Friday November 25 2016, @10:38AM

    by Anonymous Coward on Friday November 25 2016, @10:38AM (#432817)

    Due to excessive bad posting from this IP or Subnet, comment posting has temporarily been disabled. If it's you, fucking awesome

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