Ginseng has gained its popularity as an adaptogen since ancient days because of its triterpenoid saponins, known as ginsenosides. These triterpenoid saponins are unique and classified as protopanaxatriol and protopanaxadiol saponins based on their glycosylation patterns. They play many protective roles in humans and are under intense research as various groups continue to study their efficacy at the molecular level in various disorders. Ginsenosides Rb1 and Rg1 are the most abundant ginsenosides present in ginseng roots, and they confer the pharmacological properties of the plant, whereas ginsenoside Rg3 is abundantly present in Korean Red Ginseng preparation, which is highly known for its anticancer effects. These ginsenosides have a unique mode of action in modulating various signaling cascades and networks in different tissues. Their effect depends on the bioavailability and the physiological status of the cell. Mostly they amplify the response by stimulating phosphotidylinositol-4,5-bisphosphate 3-kinase/protein kinase B pathway, caspase-3/caspase-9-mediated apoptotic pathway, adenosine monophosphate-activated protein kinase, and nuclear factor kappa-light-chain-enhancer of activated B cells signaling. Furthermore, they trigger receptors such as estrogen receptor, glucocorticoid receptor, and N-methyl-d-aspartate receptor. This review critically evaluates the signaling pathways attenuated by ginsenosides Rb1, Rg1, and Rg3 in various tissues with emphasis on cancer, diabetes, cardiovascular diseases, and neurodegenerative disorders.

Cell-penetrating peptides (CPPs) can enter cells as a variety of biologically active conjugates and have various biomedical applications. To offset the cost and effort of designing novel CPPs in laboratories, computational methods are necessitated to identify candidate CPPs before in vitro experimental studies. We developed a two-layer prediction framework called machine-learning-based prediction of cell-penetrating peptides (MLCPPs). The first-layer predicts whether a given peptide is a CPP or non-CPP, whereas the second-layer predicts the uptake efficiency of the predicted CPPs. To construct a two-layer prediction framework, we employed four different machine-learning methods and five different compositions including amino acid composition (AAC), dipeptide composition, amino acid index, composition-transition-distribution, and physicochemical properties (PCPs). In the first layer, hybrid features (combination of AAC and PCP) and extremely randomized tree outperformed state-of-the-art predictors in CPP prediction with an accuracy of 0.896 when tested on independent data sets, whereas in the second layer, hybrid features obtained through feature selection protocol and random forest produced an accuracy of 0.725 that is better than state-of-the-art predictors. We anticipate that our method MLCPP will become a valuable tool for predicting CPPs and their uptake efficiency and might facilitate hypothesis-driven experimental design. The MLCPP server interface along with the benchmarking and independent data sets are freely accessible at www.thegleelab.org/MLCPP .

Anticancer peptides (ACPs) are promising therapeutic agents for targeting and killing cancer cells. The accurate prediction of ACPs from given peptide sequences remains as an open problem in the field of immunoinformatics. Recently, machine learning algorithms have emerged as a promising tool for helping experimental scientists predict ACPs. However, the performance of existing methods still needs to be improved. In this study, we present a novel approach for the accurate prediction of ACPs, which involves the following two steps: (i) We applied a two-step feature selection protocol on seven feature encodings that cover various aspects of sequence information (composition-based, physicochemical properties and profiles) and obtained their corresponding optimal feature-based models. The resultant predicted probabilities of ACPs were further utilized as feature vectors. (ii) The predicted probability feature vectors were in turn used as an input to support vector machine to develop the final prediction model called mACPpred. Cross-validation analysis showed that the proposed predictor performs significantly better than individual feature encodings. Furthermore, mACPpred significantly outperformed the existing methods compared in this study when objectively evaluated on an independent dataset.

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Ginsenosides Re and Rg1 were transformed by recombinant β-glucosidase (Bgp1) to ginsenosides Rg2 and Rh1, respectively. The bgp1 gene consists of 2,496 bp encoding 831 amino acids which have homology to the glycosyl hydrolase families 3 protein domain. Using 0.1 mg enzyme ml(-1) in 20 mM sodium phosphate buffer at 37°C and pH 7.0, the glucose moiety attached to the C-20 position of ginsenosides Re and Rg1, was removed: 1 mg ginsenoside Re ml(-1) was transformed into 0.83 mg Rg2 ml(-1) (100% molar conversion) after 2.5 h and 1 mg ginsenoside Rg1 ml(-1) was transformed into 0.6 mg ginsenoside Rh1 ml(-1) (78% molar conversion) in 15 min. Using Bgp1 enzyme, almost all initial ginsenosides Re and Rg1 were converted completely to ginsenosides Rg2 and Rh1. This is the first report of the conversion of ginsenoside Re to ginsenoside Rg2 and ginsenoside Rg1 to ginsenoside Rh1 using the recombinant β-glucosidase.

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Cockroaches are surrogate hosts for microbes that cause many human diseases. In spite of their generally destructive nature, cockroaches have recently been found to harbor potentially beneficial and medically useful substances such as drugs and allergens. However, genomic information for the American cockroach (Periplaneta americana) is currently unavailable; therefore, transcriptome and gene expression profiling is needed as an important resource to better understand the fundamental biological mechanisms of this species, which would be particularly useful for the selection of novel antimicrobial peptides. Thus, we performed de novo transcriptome analysis of P. americana that were or were not immunized with Escherichia coli. Using an Illumina HiSeq sequencer, we generated a total of 9.5 Gb of sequences, which were assembled into 85,984 contigs and functionally annotated using Basic Local Alignment Search Tool (BLAST), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) database terms. Finally, using an in silico antimicrobial peptide prediction method, 86 antimicrobial peptide candidates were predicted from the transcriptome, and 21 of these peptides were experimentally validated for their antimicrobial activity against yeast and gram positive and -negative bacteria by a radial diffusion assay. Notably, 11 peptides showed strong antimicrobial activities against these organisms and displayed little or no cytotoxic effects in the hemolysis and cell viability assay. This work provides prerequisite baseline data for the identification and development of novel antimicrobial peptides, which is expected to provide a better understanding of the phenomenon of innate immunity in similar species.

Panax ginseng C. A. Meyer is a perennial herb from the Araliaceae family. Traditionally used as a medicinal plant in Oriental medicine for more than thousand years. Ginsenosides are the major therapeutic components in ginseng roots. Roots of the ginseng plant have more commercial value and based on the age. No genomic data available till now. In this study, transcriptome analysis for hairy root, 14 year root, 4 year root get insight in to ginsenoside pathway and genes responsible for long survival and stress. Totally 6,757 Expressed Sequence Tags (EST) was obtained from cDNA libraries. Clustering of those ESTs returned 1,037 contigs and 3,445 singlets for a total of 4,482 putative unigenes. Use of bioinformatics methods 85% of EST sequence was well annotated towards reeds one dimensional concept. The unique transcripts were functionally classified by using Gene Ontology (GO) hierarchy, Kyoto Encyclopedia of Genes and Genomes (KEGG), KEGG orthology and structural domain data from biological database. Isoprenoid and putative ginsenoside pathway genes were discussed. EST dataset provides a wide outlook of the genes expressed in hairy roots, 14 years root and 4 years root. The dataset contains more than 1,365 EST sequences related to plant secondary metabolism and 745 sequences related to stresses. This study will improve the genetic engineering of ginseng plant and ginsenosides rich plant development. One dimensional data will lead to the two and three dimensional data.