Big data for chemistry: New method helps identify antibiotics in mass spectrometry datasets


  • Author: Statistics Views (source: University of California, San Diego, Jacobs School of Engineering)
  • Date: 25 November 2016
  • Copyright: Image appears courtesy of Getty Images

An international team of computer scientists has for the first time developed a method to find antibiotics hidden in huge but still unexplored mass spectrometry datasets. They detailed their new method, called DEREPLICATOR, in the Oct. 31 issue of Nature Chemical Biology.

thumbnail image: Big data for chemistry: New method helps identify antibiotics in mass spectrometry datasets

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.

Researchers also were able to discover new variants of known antibiotics. They did that by first predicting the fragmentation pattern of a chemical structure by using chemical expertise and machine learning. They compared these predictions against experimental data and looked for patterns.

The study was made possible by the bioinformatics expertise in the research group of Professor Pavel Pevzner, in the Department of Computer Science and Engineering at UC San Diego who developed viable methods to sequence bacteria and metagenomes. They are now adapting these methods to discover the metabolites they produce. In collaboration with Anton Korobeynikov and Alexander Shlemov at Saint Petersburg State University, the researchers are planning to speed up the method and apply it for discovering novel antibiotics from metagenomes.

To read the full article, follow the link here

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