New Anti-Microbial Molecules for Use in Protecting Food Sources from E.coli Bacteria

Short Communication

Austin Biochem. 2018; 3(1): 1016.

New Anti-Microbial Molecules for Use in Protecting Food Sources from E.coli Bacteria

Butler B and Darsey JA*

Department of Chemistry, University of Arkansas at Little Rock, USA

*Corresponding author: Darsey JA, Department of Chemistry, Center for Molecular Design and Development and University of Arkansas at Little Rock, USA

Received: May 25, 2018; Accepted: June 07, 2018; Published: June 14, 2018


The purpose of this research project is to discover new, naturally occurring, molecules that can be used to inhibit the growth of different bacterial pathogens on food. Specifically, this project focuses on the inhibition of E. coli (Escherichia coli) growth on food, the computational modeling of the structure of environmentally safe drug molecules that can be used to address bacterial growth, and lead to longer shelf lives of foods. The larger impact of the inhibition of bacterial growth on food would be an estimated ten percent increase in the amount of food globally every day that bacterial growth is affected. In cooperation with the Safe Foods Corporation’s existing compounds, which are used to process one hundred and ten million pounds of food world-wide from day to day, this research project should have a significant impact on both local and global food supplies and feed an additional ten million people.

Keywords: E. Coli; Bacterial growth; Safe foods; LogIC50; Molecules


Escherichia coli, or E. coli is the most prevalent and well-known organism in the gram-negative bacteria, proteobacteria, phylum (Figure 1). E. coli is one of the most-studied “free -living” microorganisms, in fact, more than seven hundred serotypes of E. coli have been identified [1]. Some strains of E. coli are not harmful and live within human gastro-intestinal systems promoting good digestive health [1,2]. Some strains are used as markers for water contamination [3]. Some strains pose a large threat due to their side-effects. These dangerous pathogenic strains of E. coli can cause toxin producing infections with many different symptoms like severe stomach cramps, bloody diarrhea, and vomiting [3]. Some strains of E. coli can cause urinary tract infections, pneumonia, and other illnesses [3]. Those infected with E. coli usually start to present symptoms three to four days after they eat contaminated foods [3]. Because E. coli can contaminate a large variety of foods as well as water it is always possible for it to cause outbreaks. To handle an outbreak the Food and Drug Administration (FDA) follows a specific procedure. First, they collect pathogen samples from sick people, food itself, and from locations where food is handled. Next, they identify Pathogens through Whole Genome Sequencing to figure out the strain of the pathogen. Then, they compare the pathogen samples to the sequenced pathogen to see how closely they match, and next they act to prevent more people from getting sick [4]. The best way to deal with E. coli is to prevent it from contaminating foods in the first place, and that is what the Safe Foods Corporation seeks to do. Safe Foods develops antimicrobials and equipment options to allow their clients to reduce the risk of food-borne pathogen growth in their production/processing plants. This project, in cooperation with Safe Foods [5], seeks to use computational modeling of different anti-microbial molecules that are shown to inhibit E. coli activity, as well as quantum-mechanical calculations to expediate the process of effective natural antimicrobial discovery and an overall increase in the shelf-life of food. We will use the results of the calculations such as the total energy, dipole moment, and molecular orbital gaps in relation to one another and to IC50 values (values that represent the concentration of an agent to inhibit fifty-percent of a incoming target pathogen’s activity) to gain insight into the effectiveness of each molecule against E. coli based on the correlation of the results in scatter plots. The correlation will be observed using the R² values, good correlation is anything above 0.8, but ideally, we would like to see correlation of 0.9 or higher.