Ellagic acid offers promise as a lead compound for anticancer therapeutics by virtue of its ability to inhibit key oncogenic signaling cascades and HDACs.

Allium cepa (onion) is a diploid plant with one of the largest nuclear genomes among all diploids. Onion is an example of an under-researched crop which has a complex heterozygous genome. There are no allergenic proteins and genomic data available for onions. This study was conducted to establish a transcriptome catalogue of onion bulb that will enable us to study onion related genes involved in medicinal use and allergies. Transcriptome dataset generated from onion bulb using the Illumina HiSeq 2000 technology showed a total of 99,074,309 high quality raw reads (~20 Gb). Based on sequence homology onion genes were categorized into 49 different functional groups. Most of the genes however, were classified under 'unknown' in all three gene ontology categories. Of the categorized genes, 61.2% showed metabolic functions followed by cellular components such as binding, cellular processes; catalytic activity and cell part. With BLASTx top hit analysis, a total of 2,511 homologous allergenic sequences were found, which had 37–100% similarity with 46 different types of allergens existing in the database. From the 46 contigs or allergens, 521 B-cell linear epitopes were identified using BepiPred linear epitope prediction tool. This is the first comprehensive insight into the transcriptome of onion bulb tissue using the NGS technology, which can be used to map IgE epitopes and prediction of structures and functions of various proteins.

Linear and non-linear QSAR studies have been performed in present investigation with multiple linear regressions (MLR) analysis and Support vector machine (SVM) using different kernels. Three relevant descriptors out of fifteen descriptors calculated are identified as LOGP values, G3e and Rte+. Their relationship with biological activity IC50 have provided structural insights in interpretation and serializing the results into a pragmatic approachable technique. QSAR models obtained show statistical fitness and good predictability. SVM using Gaussian kernel function was found more efficient in prediction of IC50 of training set of thirty small molecules HIV-1 capsid inhibitors. Y-scrambling, PRESS and test set were used as validation parameters. SVM was found superior to training set prediction and internal validations and found inferior to external test set (11 molecules) predictions. Wherein MLR analysis it was vice-versa. Mechanistic interpretation of selected descriptors from both the models actuates further research.

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