Researchers at the University of California, San Diego and Carnegie Mellon University (CMU) have developed VarQuest, a computational approach for peptidic natural products (PNP) identification.
The researchers demonstrated that VarQuest could identify about 10 times more PNPs than all previous studies.
VarQuest can process massive amounts of mass spectrometry data in a single run, and therefore can be applied in high-throughput discovery pipelines. It also can identify known PNPs in addition to unknown novel variants, which are sometimes more clinically effective.
In addition, VarQuest revealed an unexpected diversity of PNPs that may indicate evolutionary adaptation of various bacterial species to changing environment and competition.
"Natural product discovery is turning to a big data field, and the field has to get prepared for this transformation, in terms of making sense of the big data," says CMU professor Hosein Mohimani. He notes VarQuest represents the first step toward understanding the big data already collected in the field.
From UCSD News (CA)
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