Computational drug discovery for COVID-19 (COVID19CADD)

Fri, 01 Jan 2021 00:00:00 GMT

Title: Computational drug discovery for COVID-19 (COVID19CADD) Abstract: The COVID-19 pandemic, caused by the novel virus SARS-CoV-2, is most likely here to stay with us and notwithstanding the rapid deployment of vaccines, there is a need to develop antivirals against this virus. This is because new variants of the virus will emerge against which current vaccines might be less effective, there will be people that cannot be vaccinated or areas of the world where deployment of vaccination programs are not as efficient as in other more developed countries. For this reason there is a significant effort to develop antivirals effective against this virus. Virtual screening is a set of computational tools within the Computer-Aided Drug Design toolbox that is available in order to perform initial filtering of small molecules in order to identify hits that show potential and merit being studied further as part of a drug development pipeline. In this study we make use of two Ligand-Based Virtual Screening (LBVS) techniques – Molecular Fingerprint Similarity Searches and Ultrafast Shape Recognition with CREDO Atom Types (USRCAT) – to search for small molecules that are similar to a set of query molecules that have been identified as having an inhibitory effect against the Main Protease (Mpro) of SARS-CoV-2. Our experiments have resulted in a list of 42 and 195 hits identified from Fingerprint and USRCAT searches, respectively. These results were validated by the calculation of the Enrichment Factor, which resulted in scores (well) above a value of 1, with mean EF1% values of 7.56 and 2.57 for Fingerprint and USRCAT searches, respectively. These values can be studied using other in silico tools such as molecular docking or in vitro studies. Description: M.Sc.(Melit.)

RNA-seq analysis of an acute myeloid leukaemia cell line treated with phenolic compounds

Fri, 01 Jan 2021 00:00:00 GMT

Title: RNA-seq analysis of an acute myeloid leukaemia cell line treated with phenolic compounds Abstract: It is hard to overstate the revolutionary changes brought about by next-generation sequencing to the realm of biomedical research. RNA sequencing (RNA-seq) is an application of such methods used to quantify the gene expression of a biological sample at a given moment. Measurable variances in gene expression bring about differing cellular characteristics, including the hallmarks of cancer. An RNA-seq pipeline was developed to analyse gene expression data of the HL-60 Acute Myeloid Leukaemia (AML) cell line. Differential gene expression analysis was performed on three samples treated with phenolic compounds derived from extra virgin olive oil against a negative control to determine whether the treatment was successful in inducing differentiation. Each of the treated samples was taken at a different time interval after treatment (1 hr, 6 hr and 12 hr) so that the temporal aspect of differentiation may be observed. This study aims to determine whether the phenolic treatment was effective in inducing differentiation in HL-60 cells, and to determine the phenolics-induced effects on the transcriptome over time. We hypothesised that the treated cells would exhibit a significant down-regulation of genes associated with cell proliferation, and a significant up-regulation of genes associated with myeloid differentiation and apoptosis when compared to untreated cells. Over time we expect that the magnitude of the difference in expression, and the statistical significance of its presence will increase over time. Results show signs of myeloid differentiation across the treated samples, with a down-regulation of genes (p<0.001, adjusted for multiple testing) related to proliferative action (CCL2, FOSB), transmigration (JAML, FOSB, CEACAM6) and apoptotic inhibition (S100A4), all of which are characteristic of cancerous cells, while up-regulated genes have assorted metabolic functions (UTP14C, ALDH1L2, ADM2, SLC7A11, CBS). Principal component analysis of normalised gene counts suggest a distinctive transcriptomic composition of the 1 hr sample, contrasting with the similarities between the 6 hr and 12 hr samples Description: M.Sc.(Melit.)

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Computational drug discovery for COVID-19 (COVID19CADD)

RNA-seq analysis of an acute myeloid leukaemia cell line treated with phenolic compounds