Human Protein Reference Database (HPRD) is an object database that integrates a wealth of information relevant to the function of human proteins in health and disease. Data pertaining to thousands of protein-protein interactions, posttranslational modifications, enzyme/substrate relationships, disease associations, tissue expression, and subcellular localization were extracted from the literature for a nonredundant set of 2750 human proteins. Almost all the information was obtained manually by biologists who read and interpreted >300,000 published articles during the annotation process. This database, which has an intuitive query interface allowing easy access to all the features of proteins, was built by using open source technologies and will be freely available at http://www.hprd.org to the academic community. This unified bioinformatics platform will be useful in cataloging and mining the large number of proteomic interactions and alterations that will be discovered in the postgenomic era.

The rapid pace at which genomic and proteomic data is being generated necessitates the development of tools and resources for managing data that allow integration of information from disparate sources. The Human Protein Reference Database (http://www.hprd.org) is a web-based resource based on open source technologies for protein information about several aspects of human proteins including protein-protein interactions, post-translational modifications, enzyme-substrate relationships and disease associations. This information was derived manually by a critical reading of the published literature by expert biologists and through bioinformatics analyses of the protein sequence. This database will assist in biomedical discoveries by serving as a resource of genomic and proteomic information and providing an integrated view of sequence, structure, function and protein networks in health and disease.

Plasma is one of the best studied compartments in the human body and serves as an ideal body fluid for the diagnosis of diseases. This report provides a detailed functional annotation of all the plasma proteins identified to date. In all, gene products encoded by 3778 distinct genes were annotated based on proteins previously published in the literature as plasma proteins and the identification of multiple peptides from proteins under HUPO's Plasma Proteome Project. Our analysis revealed that 51% of these genes encoded more than one protein isoform. All single nucleotide polymorphisms involving protein-coding regions were mapped onto the protein sequences. We found a number of examples of isoform-specific subcellular localization as well as tissue expression. This database is an attempt at comprehensive annotation of a complex subproteome and is available on the web at http://www.plasmaproteomedatabase.org.

Herbal remedies are increasingly being recognised in recent years as alternative medicine for a number of diseases including cancer. Curcuma longa L., commonly known as turmeric is used as a culinary spice in India and in many Asian countries has been attributed to lower incidences of gastrointestinal cancers. Curcumin, a secondary metabolite isolated from the rhizomes of this plant has been shown to have significant anticancer properties, in addition to antimalarial and antioxidant effects. We sequenced the transcriptome of the rhizome of the 3 varieties of Curcuma longa L. using Illumina reversible dye terminator sequencing followed by de novo transcriptome assembly. Multiple databases were used to obtain a comprehensive annotation and the transcripts were functionally classified using GO, KOG and PlantCyc. Special emphasis was given for annotating the secondary metabolite pathways and terpenoid biosynthesis pathways. We report for the first time, the presence of transcripts related to biosynthetic pathways of several anti-cancer compounds like taxol, curcumin, and vinblastine in addition to anti-malarial compounds like artemisinin and acridone alkaloids, emphasizing turmeric's importance as a highly potent phytochemical. Our data not only provides molecular signatures for several terpenoids but also a comprehensive molecular resource for facilitating deeper insights into the transcriptome of C. longa.

The repurposing of FDA approved drugs is presently receiving attention for COVID-19 drug discovery. Previous studies revealed the binding potential of several FDA-approved drugs towards specific targets of SARS-CoV-2; however, limited studies are focused on the structural and molecular basis of interaction of these drugs towards multiple targets of SARS-CoV-2. The present study aimed to predict the binding potential of six FDA drugs towards fifteen protein targets of SARS-CoV-2 and propose the structural and molecular basis of the interaction by molecular docking and dynamic simulation. Based on the literature survey, fifteen potential targets of SARS-CoV-2, and six FDA drugs (Chloroquine, Hydroxychloroquine, Favipiravir, Lopinavir, Remdesivir, and Ritonavir) were selected. The binding potential of individual drug towards the selected targets was predicted by molecular docking in comparison with the binding of the same drugs with their usual targets. The stabilities of the best-docked conformations were confirmed by molecular dynamic simulation and energy calculations. Among the selected drugs, Ritonavir and Lopinavir showed better binding towards the prioritized targets with minimum binding energy (kcal/mol), cluster-RMS, number of interacting residues, and stabilizing forces when compared with the binding of Chloroquine, Favipiravir, and Hydroxychloroquine, later drugs demonstrated better binding when compared to the binding with their usual targets. Remdesvir showed better binding to the prioritized targets in comparison with the binding of Chloroquine, Favipiravir, and Hydroxychloroquine, but showed lesser binding potential when compared to the interaction between Ritonavir and Lopinavir and the prioritized targets. The structural and molecular basis of interactions suggest that the FDA drugs can be repurposed towards multiple targets of SARS-CoV-2, and the present computational models provide insights on the scope of repurposed drugs against COVID-19.