Computing Power Aids Fight Against Food Poisoning
3-D docking pose of potential GalU inhibitor. Credit: North Carolina State University.
In a proof-of-concept study, researchers from North Carolina State University have pinpointed new compounds that may be effective in containing the virulence – or ability to produce disease – of Listeria, a well-known bacterium that can cause severe food poisoning and even death.
Listeria are bacteria most commonly found in soil. Humans come into contact with Listeria via contaminated meat or milk products and can contract listeriosis, which can lead to severe illness or death – particularly in very young, elderly and/or immunocompromised populations.
Denis Fourches, assistant professor of computational chemistry, postdoctoral researcher Melaine Kuenemann and Paul Orndorff, professor emeritus of microbiology, knew that inhibiting a particular enzyme of Listeria – known as glucose-1-phosphate uridylyltransferase (GalU) – led to dramatic modifications of the bacterial cell surface. These chemical modifications in turn rendered the Listeria much less virulent – in other words, less able to cause illness.
The researchers turned their attention to identifying potential compounds that could inhibit the function of GalU. Using computers and cheminformatics methods, they characterized, analyzed and virtually screened more than 88,000 compounds with the potential to inhibit GalU. Computer models found 37 compounds promising enough to be tested in vitro. Of the 37, three were deemed effective enough to warrant further study, although many of the other, less active compounds yielded key information about how their chemical structures relate to their activity in inhibiting the enzyme’s function.
“We can derive several predictive structure-activity relationships based on those 37 compounds and these relationships will help us design even more effective GalU inhibiting compounds,” Fourches says. “We plan to use our computers to virtually generate thousands of new analogues, virtually screen them, and select another batch of up to 50 molecules to be tested experimentally in the future. This is true research at the interface of disciplines.”
Interestingly, inhibiting GalU also served to make the Listeria more vulnerable to cefotaxime, an antibiotic to which the bacteria are naturally resistant.
“While our ultimate objective is to get away from antibiotics altogether, in the near term the antibiotic susceptibility opens up the possibility of combinatorial therapies that could include a GalU inhibitor and a known antibiotic such as cefotaxime,” Orndorff says. “Ultimately, we believe if the GalU inhibitor is effective enough, the host (human or animal) should be able to eliminate the listerial population without antibiotics. For farmers working toward antibiotic-free farms, this could be a wonderful solution.”
“This proof-of-concept study shows that small molecules can actually be developed to shut down the activity of one specific bacterial enzyme, leading to the suppression of virulence,” Fourches says. “This is clearly a new avenue for fighting drug-resistant bacteria.”
This article has been republished from materials provided by North Carolina State University. Note: material may have been edited for length and content. For further information, please contact the cited source.
In silico Predicted Glucose-1-phosphate Uridylyltransferase (GalU) Inhibitors Block a Key Pathway Required for Listeria Virulence. Melaine A. Kuenemann, Patricia A. Spears, Paul E. Orndorff, and Denis Fourches. Molecular Informatics 8 March 2018, DOI: 10.1002/minf.201800004.
Algorithm Speeds Up Medical Image Analysis 1000 TimesNews
Medical image registration is a common technique that involves overlaying two images, such as magnetic resonance imaging (MRI) scans, to compare and analyze anatomical differences in great detail. Researchers have described a machine-learning algorithm that can register brain scans and other 3-D images more than 1,000 times more quickly using novel learning techniques.
Antarctic Worm and Machine Learning Help Identify Cerebral Palsy EarlierNews
A research team has released a study in the peer-reviewed journal BMC Bioinformatics showing that DNA methylation patterns in circulating blood cells can be used to help identify spastic cerebral palsy (CP) patients. The technique which makes use of machine learning, data science and even analysis of Antarctic worms, raises hopes for earlier targeted CP therapies.
Herpesvirus and Alzheimer's Link: High abundance of Herpes genes in postmortem Alzheimer's brain tissueNews
Data from three different brain banks to suggest that human herpesviruses are more abundant in the brains of Alzheimer's patients and may play a role in regulatory genetic networks that are believed to lead to the disease.READ MORE