Sequencing of a genome becomes cheaper, but it is difficult to understand the resulting data. Researchers have now found a new way to extract useful information from sequenced DNA.
By cataloging subtle evolutionary signatures shared between genes of genes in bacteria, the team was able to detect hundreds of previously unknown protein interactions. This method is now applied to the human genome and can provide new insights on how human proteins interact.
The project is a collaboration between researchers at the University of Washington School of Medicine and Harvard University. Their report is found in the July 1
"Protein-protein interactions are fundamental to biological function. It is noteworthy that they can now be predicted a lot by the large amounts of genomic sequence data that have been generated in recent years," said senior author David Baker, professor of biochemistry at University of Washington School of Medicine.
Cells are packed with proteins, many of which must physically interact to function. This may mean that you come together to copy DNA or to form long fibers like those found in the muscles. In many cases, however, scientists do not know which proteins interact. Discovering new mating can be slow, laborious and costly.
In a better way, a team of four computing biologists studied a phenomenon called co-development, where changes in a gene are associated with changes in another. This may indicate that two genes are linked in some important way.
For example, if a gene mutates to produce a protein of altered form, one second can be developed to produce a protein of a form complementary to the first, thereby preserving the ability of the two proteins to interact.
In recent years, researchers have found evidence of some of these subtle molecular interactions in an organism's DNA.
"Co-development has been useful in understanding how specific proteins interact, but we can now use it as a tool for detection," says lead author Qian Cong, a postdoctoral fellow at the UW School of Medicine.
The research group compared more than 4000 genes from E. coli to DNA sequences from over 40,000 other bacterial genes.This large layer of genetic information allowed the researchers to use a tailor-made statistical model to assess co-evolution between each E. coli gene.
After several Analysis rows were 1,618 pairs to have the strongest evidence of co-development, By comparing their results with a small set of already characterized protein-protein interactions, the researchers achieved significantly higher accuracy than previous experimental screening methods.
Among the newly discovered interactions were some that hinted at new biological insights, one of which, an interaction between one per oteintoxin and its antitoxin can help explain the scientists speculate why some E. coli dominate their microbial niche. Another newly found mating suggests that a protein called PstB, which was known to play a role in metabolism, can also help coordinate protein synthesis and mineral transport.
"It is rare in biology that a software tool makes predictions that promise enough to test, but that's exactly what's happening here," Cong says. There are literally hundreds of follow-up trials that can be performed in laboratories around the world. ] The team also cut the genome of Mycobacterium tuberculosis, a disease bacterium that is remotely related to E. coli. The identified 911 protein-protein interactions with high confidence 95 percent of these had never been described before. Seventy involve proteins that can contribute to the virulence of M. tuberculosis, the researchers report. These results can open new pathways to developing drugs against the lethal pathogen.
We will apply this tool to more pathogens and the human genome, Cong says. "Our success will depend on how much work other researchers put into commenting on which parts of the genome are genes and which parts are different."
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"Protein Interaction Networks Revealed by Protein Combination" Science (2019). science.sciencemag.org/lookup/ … 1126 / science.aaw6718
Pattern in DNA reveals hundreds of unknown protein pairs (2019, July 11)
July 11, 2019
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