Efficacy of Lemon Myrtle Essential Oil as a Bio-Fungicide in Inhibiting Citrus Green Mould.

The effectiveness of lemon myrtle (LM) (Backhousia citriodora) essential oil (EO) was investigated to combat Penicillium digitatum by in vitro agar diffusion and vapour assay and in artificially infected oranges. The main constituent of LM EO was revealed as citral when analysed in gas chromatography-mass spectrometry. Pure citral was also included in the experiment for comparison. The in vitro fungal growth was significantly inhibited by LM EO at 1, 2, 3, 4 and 5 µL per disc while complete growth inhibition by both the pure citral and LM EO occurred at 4 and 5 µL per disc. Inoculated fruits treated by dipping in 1000 µL L-1 LM EO solutions for 5, 10, 15, 30 and 120 s showed significantly lower fungal wounds compared to control. While longer dipping times led to some rind injuries, fruits with a 5 and 10 s dip were found free from any injury. The evaluation after dipping and storage confirmed that the fruits maintained the sensory attributes and were not compromised by the incorporation of the essential oil. The results of this study indicate that LM EO can be a promising alternative to synthetic fungicides for preserving the quality of citrus fruits during storage.

Protein Molecular Dynamics Simulations with Approximate QM: What Can We Learn?

Classical force fields are essential for computer simulations of proteins and are typically parameterized to reproduce secondary and tertiary structure of isolated proteins. However, while protein-protein interactions are ubiquitous in nature, they are not considered in parameterization efforts and are far less understood than isolated proteins. A better characterization of intermolecular interactions is widely recognized as a key to revolutionizing drug and therapeutic developments with high-throughput computational screening. Urgently needed is a critical assessment of the performance of modern protein force fields against first-principles electronic structure methods and experiments. In a daring step toward this goal, we here describe a comparison of peptide folding dynamics as predicted by a molecular mechanics force field on the one hand and by an approximate electronic structure quantum mechanical (QM) method based on density-functional tight-binding (DFTB) on the other. We further compare the dynamics from straightforward DFTB simulations with a near-linear scaling version of DFTB for massively parallel computation based on the fragment molecular orbital (FMO-DFTB) method. We illustrate differences between the phenomenology of the folding dynamics from these three methods for a small model peptide, as well as charge polarization and dynamic fluctuations, point out possible correlations and implications for force field developers, and discuss the lessons learned that might become applicable to future predictive high-throughput computer screening for personalized neoantigen cancer therapy.