Human Protein Reference Database (HPRD) () was developed to serve as a comprehensive collection of protein features, post-translational modifications (PTMs) and protein–protein interactions. Since the original report, this database has increased to >20 000 proteins entries and has become the largest database for literature-derived protein–protein interactions (>30 000) and PTMs (>8000) for human proteins. We have also introduced several new features in HPRD including: (i) protein isoforms, (ii) enhanced search options, (iii) linking of pathway annotations and (iv) integration of a novel browser, GenProt Viewer (), developed by us that allows integration of genomic and proteomic information. With the continued support and active participation by the biomedical community, we expect HPRD to become a unique source of curated information for the human proteome and spur biomedical discoveries based on integration of genomic, transcriptomic and proteomic data.

No abstract

Quantitative proteomics can be used as a screening tool for identification of differentially expressed proteins as potential biomarkers for cancers. Candidate biomarkers from such studies can subsequently be tested using other techniques for use in early detection of cancers. Here we demonstrate the use of stable isotope labeling with amino acids in cell culture (SILAC) method to compare the secreted proteins (secretome) from pancreatic cancer-derived cells with that from non-neoplastic pancreatic ductal cells. We identified 145 differentially secreted proteins (>1.5-fold change), several of which were previously reported as either up-regulated (e.g. cathepsin D, macrophage colony stimulation factor, and fibronectin receptor) or down-regulated (e.g. profilin 1 and IGFBP-7) proteins in pancreatic cancer, confirming the validity of our approach. In addition, we identified several proteins that have not been correlated previously with pancreatic cancer including perlecan (HSPG2), CD9 antigen, fibronectin receptor (integrin ␤1), and a novel cytokine designated as predicted osteoblast protein (FAM3C). The differential expression of a subset of these novel proteins was validated by Western blot analysis. In addition, overexpression of several proteins not described previously to be elevated in human pancreatic cancer (CD9, perlecan, SDF4, apoE, and fibronectin receptor) was confirmed by immunohistochemical labeling using pancreatic cancer tissue microarrays suggesting that these could be further pursued as potential biomarkers. Lastly the protein expression data from SILAC were compared with mRNA expression data obtained using

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.

Molecular mechanisms underlying the interaction between malarial sporozoites and putative receptor(s) on the salivary glands of Anopheles gambiae remain largely unknown. In previous studies, a salivary gland protein of ~100 kDa was identified as a putative target based on recognition of the protein by a monoclonal antibody (mAb) 2A3 that caused a >/= 70% reduction in the average number of sporozoites per infected salivary gland when fed to mosquitoes. Using affinity purification we purified the target of this mAb from extracts of female A. gambiae salivary glands and it was found to be a novel protein by tandem mass spectrometric analysis. Biochemical and molecular characterization of the 100 kDa protein showed that this molecule, designated Saglin, exists as a disulphide-bonded homodimer of 50 kDa subunits. The ability to form homodimers was retained even in the recombinant Saglin expressed in mammalian cells (HEK293). The amino acid sequence of Saglin contains a signal peptide suggesting that Saglin is a secreted protein. If Saglin is indeed involved in the process of invasion of A. gambiae salivary glands by sporozoites of Plasmodium, it could provide a novel target for future investigations aimed at interruption of malaria transmission.

Understanding the development of the malaria parasite within the mosquito vector at the molecular level should provide novel targets for interrupting parasitic life cycle and subsequent transmission. Availability of the complete genomic sequence of the major African malaria vector, Anopheles gambiae, allows discovery of such targets through experimental as well as computational methods. In the female mosquito, the salivary gland tissue plays an important role in the maturation of the infective form of the malaria parasite. Therefore, we carried out a proteomic analysis of salivary glands from female An. gambiae mosquitoes. Salivary gland extracts were digested with trypsin using two complementary approaches and analyzed by LC-MS/MS. This led to identification of 69 unique proteins, 57 of which were novel. We carried out a functional annotation of all proteins identified in this study through a detailed bioinformatics analysis. Even though a number of cDNA and Edman degradation-based approaches to catalog transcripts and proteins from salivary glands of mosquitoes have been published previously, this is the first report describing the application of MS for characterization of the salivary gland proteome. Our approach should prove valuable for characterizing proteomes of parasites and vectors with sequenced genomes as well as those whose genomes are yet to be fully sequenced.

Background: A large number of animal and plant genomes have been completely sequenced over the last decade and are now publicly available. Although genomes can be rapidly sequenced, identifying protein-coding genes still remains a problematic task. Availability of protein sequence data allows direct confirmation of protein-coding genes. Mass spectrometry has recently emerged as a powerful tool for proteomic studies. Protein identification using mass spectrometry is usually carried out by searching against databases of known proteins or transcripts. This approach generally does not allow identification of proteins that have not yet been predicted or whose transcripts have not been identified.

Protein phosphorylation occurs in certain sequence/structural contexts that are still incompletely understood. The amino acids surrounding the phosphorylated residues are important in determining the binding of the kinase to the protein sequence. Upon phosphorylation these sequences also determine the binding of certain domains that specifically bind to phosphorylated sequences. Thus far, such ‘motifs’ have been identified through alignment of a limited number of well identified kinase substrates. Results Experimentally determined phosphorylation sites from Human Protein Reference Database were used to identify 1,167 novel serine/threonine or tyrosine phosphorylation motifs using a computational approach. We were able to statistically validate a number of these novel motifs based on their enrichment in known phosphopeptides datasets over phosphoserine/threonine/tyrosine peptides in the human proteome. There were 299 novel serine/threonine or tyrosine phosphorylation motifs that were found to be statistically significant. Several of the novel motifs that we identified computationally have subsequently appeared in large datasets of experimentally determined phosphorylation sites since we initiated our analysis. Using a peptide microarray platform, we have experimentally evaluated the ability of casein kinase I to phosphorylate a subset of the novel motifs discovered in this study. Our results demonstrate that it is feasible to identify novel phosphorylation motifs through large phosphorylation datasets. Our study also establishes peptide microarrays as a novel platform for high throughput kinase assays and for the validation of consensus motifs. Finally, this extended catalog of phosphorylation motifs should assist in a systematic study of phosphorylation networks in signal transduction pathways.