8 research outputs found

    Identification of potential antimicrobials against <i>Salmonella typhimurium</i> and <i>Listeria monocytogenes</i> using Quantitative Structure-Activity Relation modeling

    No full text
    <div><p>The shelf-life of fresh carcasses and produce depends on the chemical and physical properties of antimicrobials currently used for treatment. For many years the gold standard of these antimicrobials has been Cetylpyridinium Chloride (CPC) a quaternary ammonium compound (QAC). CPC is very effective at removing bacterial pathogens from the surface of chicken but has not been approved for other products due to a toxic residue left behind after treatment. Currently there is also a rising trend in QAC resistant bacteria. In order to find new compounds that can combat both antimicrobial resistance and the toxic residue we have developed two Quantitative Structure-Activity Relationship (QSAR) models for <i>Salmonella typhimurium</i> and <i>Listeria monocytogenes</i>. These models have been shown to be accurate and reliable through multiple internal and external validation techniques. In processing these models we have also identified important descriptors and structures that may be key in producing a viable compound. With these models, development and testing of new compounds should be greatly simplified.</p></div

    Internal and external validation data for top S. typhimurium and L. monocytogenes Models.

    No full text
    <p>Internal and external validation data for top S. typhimurium and L. monocytogenes Models.</p

    Linear regression for top model against <i>L</i>. <i>monocytogenes</i>.

    No full text
    <p>Regression of experimentally validated and model predicted log(MIC) values for the top QSAR model (model 90) for <i>L</i>. <i>monocytogenes</i> selected through internal and external validations. The yellow dots represent the training set while the blue dots represent the test set.</p

    Descriptor distribution for top QSAR models.

    No full text
    <p>Distribution of chemical descriptors (black) and their respective average absolute coefficient magnitudes (black), in top models for <i>S</i>. <i>typhimurium</i> (A) and <i>L</i>. <i>monocytogenes</i> (B). Descriptor distribution normalized to the total number of models observed for each bacteria. (C) Descriptor distribution (black) and average absolute coefficient magnitudes (grey) based on previously created models against <i>E</i>. <i>coli</i>.[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0189580#pone.0189580.ref033" target="_blank">33</a>] Most descriptors are in canonical SMILES, using single letters to represent atoms in a molecule, β€œ*” denote any atom, β€œβ€˜β€œ represent potential atoms, β€œ()” represent branches in a molecule, numbers represent joining points in ring structures, β€œ=β€œ represent double bonds, and lower case letters are atoms involved in aromatic strucutres. (D) Distribution of descriptor types across all top models. Descriptors that were pertinent to multiple bins were included in all potential bins.</p

    Top 10 compounds against S. typhimurium in terms of log(MIC).

    No full text
    <p>Top 10 compounds against S. typhimurium in terms of log(MIC).</p

    Substructure amongst top compounds predicted by QSAR modelling.

    No full text
    <p>Major substructure detected by SIMCOMP2 software for the top similar structures from their prediction sets.</p

    Pairwise similarity comparisons between top 50 predicted compounds for three bacterial species.

    No full text
    <p>Pairwise similarity comparisons between top 50 predicted compounds for three bacterial species.</p

    Top 5 compounds according to average log(MIC) from all 3 predictive models.

    No full text
    <p>Top 5 compounds according to average log(MIC) from all 3 predictive models.</p
    corecore