Development of Human Protein Reference Database as an Initial Platform for Approaching Systems Biology in Humans

McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland 21287, USA.
Genome Research (Impact Factor: 14.63). 10/2003; 10(10):2363-71. DOI: 10.1101/gr.1680803


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 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.

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    • "The list of all human proteins was downloaded from Human Protein Resource Database (HPRD, release 9) (Peri et al., 2003). This database is widely used in protein–protein network interaction studies and it is also well annotated in terms of protein sequence information. "
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    ABSTRACT: Low-complexity regions are sub-sequences of biased composition in a protein sequence. The influence of these regions over protein evolution, specific functions and highly interactive capacities is well known. Although protein sequence entropy has been largely studied, its relationship with low-complexity regions and the subsequent effects on protein function remains unclear. In this work we propose a theoretical and empirical model integrating the sequence entropy with local complexity parameters. Our results indicate that the protein sequence entropy is related with the protein length, the entropies inside and outside the low-complexity regions as well as their number and average size. We found a small but significant increment in the sequence entropy of hubs proteins. In agreement with our theoretical model, this increment is highly dependent of the balance between the increment of protein length and average size of the low-complexity regions. Finally, our models and proteins analysis provide evidence supporting that modifications in the average size is more relevant in hubs proteins than changes in the number of low-complexity regions. Copyright © 2015. Published by Elsevier Ltd.
    Journal of Theoretical Biology 07/2015; 382. DOI:10.1016/j.jtbi.2015.06.049 · 2.12 Impact Factor
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    • "), the Biomolecular Interaction Network Database (Bader et al., 2003), the Biological General Repository for Interaction Data Sets (Stark et al., 2006), the Database of Interacting Proteins (Salwinski et al., 2004), the Human Protein Reference Database (Peri et al., 2003), IntAct (Aranda et al., 2010), the Molecular Interaction database (Chatr-aryamontri et al., 2007), the mammalian PPI database of the Munich Information Center on Protein Sequences (Pagel et al., 2005), PDZBase (a PPI database for PDZ domains; Beuming et al., 2005), and Reactome (Vastrik et al., 2007). The databases were downloaded from their corresponding web sites in October, 2011. "
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    ABSTRACT: The multifactorial nature of traumatic brain injury (TBI), especially the complex secondary tissue injury involving intertwined networks of molecular pathways that mediate cellular behavior, has confounded attempts to elucidate the pathology underlying the progression of TBI. Here, systems biology strategies are exploited to identify novel molecular mechanisms and protein indicators of brain injury. To this end, we performed a meta-analysis of four distinct high-throughput gene expression studies involving different animal models of TBI. By using canonical pathways and a large human protein-interaction network as a scaffold, we separately overlaid the gene expression data from each study to identify molecular signatures that were conserved across the different studies. At 24 hr after injury, the significantly activated molecular signatures were nonspecific to TBI, whereas the significantly suppressed molecular signatures were specific to the nervous system. In particular, we identified a suppressed subnetwork consisting of 58 highly interacting, coregulated proteins associated with synaptic function. We selected three proteins from this subnetwork, postsynaptic density protein 95, nitric oxide synthase 1, and disrupted in schizophrenia 1, and hypothesized that their abundance would be significantly reduced after TBI. In a penetrating ballistic-like brain injury rat model of severe TBI, Western blot analysis confirmed our hypothesis. In addition, our analysis recovered 12 previously identified protein biomarkers of TBI. The results suggest that systems biology may provide an efficient, high-yield approach to generate testable hypotheses that can be experimentally validated to identify novel mechanisms of action and molecular indicators of TBI. © 2014 Wiley Periodicals, Inc.
    Journal of Neuroscience Research 02/2015; 93(2). DOI:10.1002/jnr.23503 · 2.59 Impact Factor
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    • "Furthermore, because a given protein may have diverse functions not only tightly linked with its subcellular distribution but also with the proteins with which it interacts, information concerning the subcellular localization and interactions among proteins are important for recreating the model networks that may reflect details of the intricate biological processes at the spatial level[6], [7]. Several public databases are available for analysis of protein interactions, including Network of Functional Coupling (FunCoup) and Human Protein Reference Database (HPRD)[8], [9]. In this study, using FunCoup, we constructed an initial interactome on the basis of the supposed proteins that are encoded by differentially expressed genes between the R14- and S-antigen-specific T cell lines. "
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    ABSTRACT: Human uveitis is a type of T cell-mediated autoimmune disease that often shows relapse-remitting courses affecting multiple biological processes. As a cytoplasmic process, autophagy has been seen as an adaptive response to cell death and survival, yet the link between autophagy and T cell-mediated autoimmunity is not certain. In this study, based on the differentially expressed genes (GSE19652) between the recurrent versus monophasic T cell lines, whose adoptive transfer to susceptible animals may result in respective recurrent or monophasic uveitis, we proposed grouping annotations on a subcellular layered interactome framework to analyze the specific bioprocesses that are linked to the recurrence of T cell autoimmunity. That is, the subcellular layered interactome was established by the Cytoscape and Cerebral plugin based on differential expression, global interactome, and subcellular localization information. Then, the layered interactomes were grouping annotated by the ClueGO plugin based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. The analysis showed that significant bioprocesses with autophagy were orchestrated in the cytoplasmic layered interactome and that mTOR may have a regulatory role in it. Furthermore, by setting up recurrent and monophasic uveitis in Lewis rats, we confirmed by transmission electron microscopy that, in comparison to the monophasic disease, recurrent uveitis in vivo showed significantly increased autophagy activity and extended lymphocyte infiltration to the affected retina. In summary, our framework methodology is a useful tool to disclose specific bioprocesses and molecular targets that can be attributed to a certain disease. Our results indicated that targeted inhibition of autophagy pathways may perturb the recurrence of uveitis.
    PLoS ONE 08/2014; 9(8):e104404. DOI:10.1371/journal.pone.0104404 · 3.23 Impact Factor
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