Krishna Tulsi, a member of Lamiaceae family, is a herb well known for its spiritual, religious and medicinal importance in India. The common name of this plant is ‘Tulsi’ (or ‘Tulasi’ or ‘Thulasi’) and is considered sacred by Hindus. We present the draft genome of Ocimum tenuiflurum L (subtype Krishna Tulsi) in this report. The paired-end and mate-pair sequence libraries were generated for the whole genome sequenced with the Illumina Hiseq 1000, resulting in an assembled genome of 374 Mb, with a genome coverage of 61 % (612 Mb estimated genome size). We have also studied transcriptomes (RNA-Seq) of two subtypes of O. tenuiflorum, Krishna and Rama Tulsi and report the relative expression of genes in both the varieties. The pathways leading to the production of medicinally-important specialized metabolites have been studied in detail, in relation to similar pathways in Arabidopsis thaliana and other plants. Expression levels of anthocyanin biosynthesis-related genes in leaf samples of Krishna Tulsi were observed to be relatively high, explaining the purple colouration of Krishna Tulsi leaves. The expression of six important genes identified from genome data were validated by performing q-RT-PCR in different tissues of five different species, which shows the high extent of urosolic acid-producing genes in young leaves of the Rama subtype. In addition, the presence of eugenol and ursolic acid, implied as potential drugs in the cure of many diseases including cancer was confirmed using mass spectrometry. The availability of the whole genome of O.tenuiflorum and our sequence analysis suggests that small amino acid changes at the functional sites of genes involved in metabolite synthesis pathways confer special medicinal properties to this herb.

Prostate cancer is the second most dangerous cancer type worldwide. While various treatment options are present i.e. agonists and antagonists, their utilization leads to adverse effects and due to this resistance developing, ultimately the outcome is remission. So, to overcome this issue, we have undertaken an in-silico investigation to identify promising and unique flavonoid candidates for combating prostate cancer. Using GOLD software, the study assessed the effectiveness of 560 natural secondary polyphenols against CDKN2. Protein Data Bank was used to retrieve the 3D crystal structure of CDKN2 (PDB Id: 4EK3) and we retrieved the structure of selected secondary polyphenols from the PubChem database. The compound Diosmetin shows the highest GOLD score with the selected Protein i.e. CDKN2 which is 58.72. To better understand the 2-dimensional and 3-dimensional interactions, the interacting amino acid residues were visualised using Discovery Studio 3.5 and Maestro 13.5. Using Schrodinger-Glide, the Diosmetin and CDKN2 were re-docked, and decoy ligands were docked to CDKN2, which was used to further ascertain the study. The ligands with the highest Gold score were forecasted for pharmacokinetics characteristics, and the results were tabulated and analysed. Utilising the Gromacs software and Desmond packages, 100 ns of Diosmetin molecular dynamics simulations were run to evaluate the structural persistence and variations of protein-ligand complexes. Additionally, our investigation revealed that Diosmetin had a better binding affinity with CDKN2 measuring 58.72, and it also showed remarkable stability across a 100-ns simulation. Thus, following in-vitro and in-vivo clinical studies, diosmetin might lead to the Prostate regimen.Communicated by Ramaswamy H. Sarma.

Protein domain families are usually classified on the basis of similarity of amino acid sequences. Selection of a single representative sequence for each family provides targets for structure determination or modeling and also enables fast sequence searches to associate new members to a family. Such a selection could be challenging since some of these domain families exhibit huge variation depending on the number of members in the family, the average family sequence length or the extent of sequence divergence within a family. We had earlier created 3PFDB database as a repository of best representative sequences, selected from each PFAM domain family on the basis of high coverage. In this study, we have improved the database using more efficient strategies for the initial generation of sequence profiles and implement two independent methods, FASSM and HMMER, for identifying family members. HMMER employs a global sequence similarity search, while FASSM relies on motif identification and matching. This improved and updated database, 3PFDB+ generated in this study, provides representative sequences and profiles for PFAM families, with 13 519 family representatives having more than 90% family coverage. The representative sequence is also highlighted in a two-dimensional plot, which reflects the relative divergence between family members. Representatives belonging to small families with short sequences are mainly associated with low coverage. The set of sequences not recognized by the family representative profiles, highlight several potential false or weak family associations in PFAM. Partial domains and fragments dominate such cases, along with sequences that are highly diverged or different from other family members. Some of these outliers were also predicted to have different secondary structure contents, which reflect different putative structure or functional roles for these domain sequences. Database URL:http://caps.ncbs.res.in/3pfdbplus/