Yushir R. Maharaj

Bio: Yushir R. Maharaj is an academic researcher from University of KwaZulu-Natal. The author has contributed to research in topics: Virtual screening & Pharmacophore. The author has an hindex of 3, co-authored 4 publications receiving 58 citations.

Effects of fermentation temperature on the composition of beer volatile compounds, organoleptic quality and spent yeast density

Abstract: Production of good quality beer is dependent largely on the fermentation temperature and yeast strains employed during the brewing process, among others. In this study, effects of fermentation temperatures and yeast strain type on beer quality and spent yeast density produced after wort fermentation by two commercial yeast strains were investigated. Beer samples were assessed for colour, clarity and foam head stability using standard methods, whilst the compositions and concentration of Beer Volatile Compounds (BVCs) produced were assessed using GC-MS. The spent yeast density, measured as dry cell weight, ranged between 1.84 - 3.157 mg/ml for both yeast strains with the highest yield obtained at room temperature fermentation. A peak viable population of 2.56 x 10 7 cfu/ml was obtained for strain A, also during fermentation at room temperature. The foam head of the beers produced at 22.5oC was most stable, with foam head ratings of 2.66 and 2.50 for yeast strain A and B, respectively. However, there was no significant ( p = 0.242) difference in colour intensity between the beers produced at the different fermentation temperatures. Eight different BVCs were detected in all beer samples and were found to affect the organoleptic properties of the beer produced. Further optimizations are required to determine the effects of other parameters on beer quality.

Identification of novel gyrase B inhibitors as potential anti-TB drugs: homology modelling, hybrid virtual screening and molecular dynamics simulations.

Abstract: Using an integrated computational approach involving homology modelling, pharmacophore/structure-based virtual screening, molecular dynamics simulations and per-residue energy contribution, 10 compounds were proposed as potential TB inhibitors. Via validated docking calculations, binding free energy calculations showed that the proposed compounds presented better binding affinity with DNA gyrase B when compared to novobiocin. The compiled in silico approach employed in this study may serve as a useful tool in the process of the design and development of drugs, not only against TB, but also for a wide range of biological systems.

Target-Bound Generated Pharmacophore Model to Improve the Pharmacophore-Based Virtual Screening: Identification of G-Protein Coupled Human CCR2 Receptors Inhibitors as Anti-Inflammatory Drugs

Abstract: Pharmacophore-based virtual screening is being widely used to discover new drug candidates. Building a pharmacophore model based on a known inhibitor that is unbound to the target could be misleading and result in mining for the wrong hits. Results presented herein confirm that pharmacophore models based on unbound and bound ligand confirmations produce significantly, structurally different compound libraries and, consequently, change the outcome of the virtual screening. To further verify our findings, molecular dynamics and extensive post-dynamic analysis are performed for the best retrieved hits from each approach; these are the unbound and bound ligand pharmacophore-generated libraries. In this report, the proposed target-bound pharmacophore model is used to discover potential G-protein coupled CCR2 receptor inhibitors as potential anti-inflammatory drugs. Herein, various molecular modeling approaches are adopted including homology modeling, molecular docking, lipid bilayer molecular dynamics simulations and per-residue interaction energy decomposition analysis. The current study highlights some critical aspects in the pharmacophore-based virtual screening as a powerful tool in the drug discovery and development machinery.

Advanced method optimization for volatile aroma profiling of beer using two-dimensional gas chromatography time-of-flight mass spectrometry.

Abstract: The complex mixture of volatile organic compounds (VOCs) present in the headspace of Trappist and craft beers was studied to illustrate the efficiency of thermal desorption (TD) comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) for highlighting subtle differences between highly complex mixtures of VOCs Headspace solid-phase microextraction (HS-SPME), multiple (and classical) stir bar sorptive extraction (mSBSE), static headspace (SHS), and dynamic headspace (DHS) were compared for the extraction of a set of 21 representative flavor compounds of beer aroma A Box-Behnken surface response methodology experimental design optimization (DOE) was used for convex hull calculation (Delaunay's triangulation algorithms) of peak dispersion in the chromatographic space The predicted value of 05 for the ratio between the convex hull and the available space was 10% higher than the experimental value, demonstrating the usefulness of the approach to improve optimization of the GC×GC separation Chemical variations amongst aligned chromatograms were studied by means of Fisher Ratio (FR) determination and F-distribution threshold filtration at different significance levels (α=005 and 001) and based on z-score normalized area for data reduction Statistically significant compounds were highlighted following principal component analysis (PCA) and hierarchical cluster analysis (HCA) The dendrogram structure not only provided clear visual information about similarities between products but also permitted direct identification of the chemicals and their relative weight in clustering The effective coupling of DHS-TD-GC×GC-TOFMS with PCA and HCA was able to highlight the differences and common typical VOC patterns among 24 samples of different Trappist and selected Canadian craft beers

Phenolic, volatile, and sensory profiles of beer enriched by macerating quince fruits

Abstract: Quince fruit (Cydonia oblonga Miller) is not very appreciated for their sensory properties for fresh consumption but it is a fruit rich in functional compounds. The aim of this study was to take advantage of this fruit to improve the functional profile of one of the most popular alcoholic beverages around the world, beer. For this purpose, one quince cultivar, Vranja, and two clones ZM9 and PUM were used to brew beer and study its physico-chemical, aromatic (headspace solid phase micro-extraction, HS-SPME), functional (phenolic compounds and antioxidant capacity) and sensory (descriptive analysis with trained panel) properties. The addition of quince increased the total polyphenol content, the total hydroxicinnamic acids, concentration of main volatile compounds related with fruity sensory descriptors, and led to higher intensities of floral and fruity sensory attributes. Therefore, the quince beer had a better functional composition and better sensory characteristics than the control beer.

Pervaporation methodology for improving alcohol-free beer quality through aroma recovery

Abstract: Two different beers, a Special beer (5.5% ABV) and a Reserve beer (6.5% ABV) were pervaporated in order to recover aromas to be added to a low-alcohol beer (less than 1% ABV) and an alcohol-free beer (less than 0.1% ABV) to improve their sensory quality. Sensory analysis confirmed that this was accomplished. Through the pervaporation process, three flavor constituents of beer (isobutyl alcohol, ethyl acetate and isoamyl acetate) were analyzed in detail. Selectivities were roughly predicted by an easy model based on the Hildebrand solubility parameters for the polymer and the species in the solution. According to the model, a polymer will transmit a species almost perfectly if their solubility parameters coincide. This model helps to calculate the relative selectivities from solubility parameters and can provide guidance when choosing the membrane for specific separation requirements in food processing or other separation problems where pervaporation can be of great help.

Effect of Saccharomyces and non-Saccharomyces native yeasts on beer aroma compounds.

Abstract: Recently, the increase in microbreweries and the consequent production of craft beers have reached exponential growth. The interest in non-conventional yeasts for innovation and a unique selling feature in beer fermentation is increasing. This work studied the autochthonous Saccharomyces and non-Saccharomyces yeasts, isolated from various food sources, with the ability to modify and improve the fermentative and aromatic profiles during alcoholic fermentation. The ability to ferment maltose and produce desirable aroma compounds were considered as the key characters for the screening selection. A synthetic beer wort was developed for this purpose, to simulate beer wort composition. A total of forty-seven yeast strains belonging to different genera were analysed according to their fermentation profile, volatile compounds production and sensory analysis. Three native strains of Saccharomyces cerevisiae, Zygoascus meyerae and Pichia anomala were selected to evaluate their aromatic profile in single and mixed fermentations. The strains produced 4-vinylguaiacol, β-phenylethyl alcohol, and isoamyl alcohol at levels significantly above the sensory threshold, making them interesting for wheat and blond craft beer styles. The native Hanseniaspora vineae was also included in a co-fermentation treatment, resulting in a promising yeast to produce fruity beers.

Per-residue energy decomposition pharmacophore model to enhance virtual screening in drug discovery: a study for identification of reverse transcriptase inhibitors as potential anti-HIV agents

Abstract: A novel virtual screening approach is implemented herein, which is a further improvement of our previously published "target-bound pharmacophore modeling approach". The generated pharmacophore library is based only on highly contributing amino acid residues, instead of arbitrary pharmacophores, which are most commonly used in the conventional approaches in literature. Highly contributing amino acid residues were distinguished based on free binding energy contributions obtained from calculation from molecular dynamic (MD) simulations. To the best of our knowledge; this is the first attempt in the literature using such an approach; previous approaches have relied on the docking score to generate energy-based pharmacophore models. However, docking scores are reportedly unreliable. Thus, we present a model for a per-residue energy decomposition, constructed from MD simulation ensembles generating a more trustworthy pharmacophore model, which can be applied in drug discovery workflow. This work is aimed at introducing a more rational approach to the field of drug design, rather than comparing the validity of this approach against those previously reported. We recommend additional computational and experimental work to further validate this approach. This approach was used to screen for potential reverse transcriptase inhibitors using the pharmacophoric features of compound GSK952. The complex was subjected to docking, thereafter, MD simulation confirmed the stability of the system. Experimentally determined inhibitors with known HIV-reverse transcriptase inhibitory activity were used to validate the protocol. Two potential hits (ZINC46849657 and ZINC54359621) showed a significant potential with regard to free binding energy. Reported results obtained from this work confirm that this new approach is favorable in the future of the drug design industry.