ABSTRACT
A unique series of pyrazolyl-chalcone derivatives was synthesized via the method of Claisen-Schmidt condensation. The desired chalcone derivatives 7a-d and 9a-f were obtained in good yields by reacting the 4-acetyl-5-thiophene-pyrazole with the appropriate heteroaryl aldehyde derivatives. The novel chalcones have undergone complete elemental analysis, 1H-NMR, 13C-NMR, mass spectrometry, and IR characterization. The three human cancer cell lines MCF7 (human Caucasian breast adenocarcinoma), PC3 (prostatic cancer) and PACA2 (pancreatic carcinoma) as well as the normal cell line BJ1 (normal skin fibroblasts) were tested in vitro for the anti-cancer properties of the newly synthesized chalcone derivatives. When compared to the reference medicine doxorubicin (IC50 = 52.1 µM), compound 9e showed the most promise derivative (IC50 = 27.6 µM) against PACA2 cells, while compound 7d demonstrated anticancer efficacy (IC50 = 42.6 µM against MCF7 cells compared to the reference drug doxorubicin (IC50 = 48 µM). Using breast and pancreatic cell lines, the gene expression, DNA damage, and DNA fragmentation percentages for compounds 7d and 9e were evaluated. Moreover, the molecular docking study of compounds 7d and 9e was assessed. The binding affinities of compound 9e toward P53 mutant Y220C was -22 kcal per mole, while those of compound 7d towards Bcl2 and CDK4 were -27.81 and -26.9 kcal per mole, respectively, compared to the standard values (-15.82, -33.96 and -29.9 kcal per mole).
ABSTRACT
Nowadays, the manipulation and analysis of genomic data stored in publicly accessible repositories have become a daily task in genomics and bioinformatics laboratories. Due to the enormous advancement in the field of genome sequencing and the emergence of many projects, bioinformaticians have pushed for the creation of a variety of programs and pipelines that will automatically analyze such big data, in particular the pipelines of gene annotation. Dealing with annotation files using easy and simple programs is very important, particularly for non-developers, enhancing the genomic data analysis acceleration. One of the first tasks required to work with genomic annotation files is to extract different features. In this regard, we have developed GAD ( https://github.com/bio-projects/GAD ) using Python to be a fast, easy, and controlled script that has a high ability to handle annotation files such as GFF3 and GTF. GAD is a cross-platform graphical interface tool used to extract genome features such as intergenic regions, upstream, and downstream genes. Besides, GAD finds all names of ambiguous sequence ontology, and either extracts them or considers them as genes or transcripts. The results are produced in a variety of file formats, such as BED, GTF, GFF3, and FASTA, supported by other bioinformatics programs. The GAD can handle large sizes of different genomes and an infinite number of files with minimal user effort. Therefore, our script could be integrated into various pipelines in all genomic laboratories to accelerate data analysis.
