Computer biologists at Carnegie Mellon University have developed a software tool that enables high-speed "match play" to identify the bioactive molecules and microbial genes they generate to evaluate them as possible antibiotics and other therapeutic agents.
Working with colleagues at the University of California, San Diego, and six other institutes, Hossein Mohiman, an assistant professor in the Department of Computational Biology at CMU, and Liu Cao, a Ph.D. A student at the department showed that their metamaterial device could detect bioactive molecules at least 100 times faster than previously possible.
The researchers discussed their findings, including the biological interest of seven previously unknown molecules from various environments – the human intestine, the deep ocean, and the International Space Station – in their research. Cell systems.
New technicians for extracting DNA directly from the environment have shown interest in microbial communities, including those that co-exist with healthy individuals. Some microbes produce molecules that protect their host and are therefore candidates to become a therapeutic drug. In the last decade, microbiologists have created numerous large databases of microbial DNA.
Microbial communities consist of hundreds or thousands of different types of microbes and millions of different molecular products, and each microbe will die quickly, if studied individually. So identifying molecules that can be a candidate for drugs and isolating the microbes that produce them requires some innovative thinking.
Cao and Mohiman decided to use an approach called genome extraction. This involves looking at the clusters of genes and determining what molecules these genes produce. It's like looking at an auto assembly line and trying to determine which car to build, Mohiman said.
However, predicting the molecular product of a gene cluster is full of errors, Cao said. To work around this gap, he and Mohiman borrowed electrical engineering called Viterbi decoding, which helps engineers in "noisy" radio channel messages. This allowed them to create errors for a tolerant search engine that could find matches between microbial DNA databases and databases that identify molecular products in their mass spectrum.
Cao and Mohiman, working with microbiologists at many institutes, have used their methods to discover ribosomally synthesized and post-translationally modified peptides (or RiPPs) in the family of natural products that have been documented in the pharmaceutical and food industries.
About 20,000 clusters of genes have been found to classify RiPPs, but so far only a handful of RiPPs have been linked to one of these clusters. Using MetaMiner to find the spectrum of millions of molecular products and compare the clusters of genes in the eight datasets, researchers were able to identify 31 known RiPPs and seven previously unknown RiPPs in about two weeks.
"Usually you would be happy to see one match," Mohimyan said. Getting these results with a manual method may take decades.
The new algorithm effectively finds candidates for antibiotics
Computing & # 39; Match Matches & # 39; Prescribes Potential Antibiotics (2019, October 16)
Read October 16, 2019
This document is subject to copyright. For the purposes of private law and research other than a fair case, no
Part may be transmitted without written permission. Content is provided for informational purposes only.