Zhang-Zhi Hu

The iProClass integrated database for protein functional analysis

Increasingly, scientists have begun to tackle gene functions and other complex regulatory processes by studying organisms at the global scales for various levels of biological organization, ranging from genomes to metabolomes and physiomes. Meanwhile, new bioinformatics methods have been developed for inferring protein function using associative analysis of functional properties to complement the traditional sequence homology-based methods. To fully exploit the value of the high-throughput system biology data and to facilitate protein functional studies requires bioinformatics infrastructures that support both data integration and associative analysis. The iProClass database, designed to serve as a framework for data integration in a distributed networking environment, provides comprehensive descriptions of all proteins, with rich links to over 50 databases of protein family, function, pathway, interaction, modification, structure, genome, ontology, literature, and taxonomy. In particular, the database is organized with PIRSF family classification and maps to other family, function, and structure classification schemes. Coupled with the underlying taxonomic information for complete genomes, the iProClass system (http://pir.georgetown.edu/iproclass/) supports associative studies of protein family, domain, function, and structure. A case study of the phosphoglycerate mutases illustrates a systematic approach for protein family and phylogenetic analysis. Such studies may serve as a basis for further analysis of protein functional evolution, and its relationship to the co-evolution of metabolic pathways, cellular networks, and organisms.

Framework for a Protein Ontology

Biomedical ontologies are emerging as critical tools in genomic and proteomic research, where complex data in disparate resources need to be integrated. A number of ontologies describe properties that can be attributed to proteins. For example, protein functions are described by the Gene Ontology (GO) and human diseases by SNOMED CT or ICD10. There is, however, a gap in the current set of ontologies – one that describes the protein entities themselves and their relationships. We have designed the PR otein O ntology (PRO) to facilitate protein annotation and to guide new experiments. The components of PRO extend from the classification of proteins on the basis of evolutionary relationships to the representation of the multiple protein forms of a gene (products generated by genetic variation, alternative splicing, proteolytic cleavage, and other post-translational modifications). PRO will allow the specification of relationships between PRO, GO and other ontologies in the OBO Foundry. Here we describe the initial development of PRO, illustrated using human and mouse proteins involved in the transforming growth factor-beta and bone morphogenetic protein signaling pathways.

Literature mining and database annotation of protein phosphorylation using a rule-based system

MOTIVATION A large volume of experimental data on protein phosphorylation is buried in the fast-growing PubMed literature. While of great value, such information is limited in databases owing to the laborious process of literature-based curation. Computational literature mining holds promise to facilitate database curation. RESULTS A rule-based system, RLIMS-P (Rule-based LIterature Mining System for Protein Phosphorylation), was used to extract protein phosphorylation information from MEDLINE abstracts. An annotation-tagged literature corpus developed at PIR was used to evaluate the system for finding phosphorylation papers and extracting phosphorylation objects (kinases, substrates and sites) from abstracts. RLIMS-P achieved a precision and recall of 91.4 and 96.4% for paper retrieval, and of 97.9 and 88.0% for extraction of substrates and sites. Coupling the high recall for paper retrieval and high precision for information extraction, RLIMS-P facilitates literature mining and database annotation of protein phosphorylation.

dbOGAP - An Integrated Bioinformatics Resource for Protein O-GlcNAcylation

BackgroundProtein O-GlcNAcylation (or O-GlcNAc-ylation) is an O-linked glycosylation involving the transfer of β-N-acetylglucosamine to the hydroxyl group of serine or threonine residues of proteins. Growing evidences suggest that protein O-GlcNAcylation is common and is analogous to phosphorylation in modulating broad ranges of biological processes. However, compared to phosphorylation, the amount of protein O-GlcNAcylation data is relatively limited and its annotation in databases is scarce. Furthermore, a bioinformatics resource for O-GlcNAcylation is lacking, and an O-GlcNAcylation site prediction tool is much needed.DescriptionWe developed a database of O-GlcNAcylated proteins and sites, dbOGAP, primarily based on literature published since O-GlcNAcylation was first described in 1984. The database currently contains ~800 proteins with experimental O-GlcNAcylation information, of which ~61% are of humans, and 172 proteins have a total of ~400 O-GlcNAcylation sites identified. The O-GlcNAcylated proteins are primarily nucleocytoplasmic, including membrane- and non-membrane bounded organelle-associated proteins. The known O-GlcNAcylated proteins exert a broad range of functions including transcriptional regulation, macromolecular complex assembly, intracellular transport, translation, and regulation of cell growth or death. The database also contains ~365 potential O-GlcNAcylated proteins inferred from known O-GlcNAcylated orthologs. Additional annotations, including other protein posttranslational modifications, biological pathways and disease information are integrated into the database. We developed an O-GlcNAcylation site prediction system, OGlcNAcScan, based on Support Vector Machine and trained using protein sequences with known O-GlcNAcylation sites from dbOGAP. The site prediction system achieved an area under ROC curve of 74.3% in five-fold cross-validation. The dbOGAP website was developed to allow for performing search and query on O-GlcNAcylated proteins and associated literature, as well as for browsing by gene names, organisms or pathways, and downloading of the database. Also available from the website, the OGlcNAcScan tool presents a list of predicted O-GlcNAcylation sites for given protein sequences.ConclusionsdbOGAP is the first public bioinformatics resource to allow systematic access to the O-GlcNAcylated proteins, and related functional information and bibliography, as well as to an O-GlcNAcylation site prediction tool. The resource will facilitate research on O-GlcNAcylation and its proteomic identification.

BioTagger-GM: A Gene/Protein Name Recognition System

OBJECTIVES Biomedical named entity recognition (BNER) is a critical component in automated systems that mine biomedical knowledge in free text. Among different types of entities in the domain, gene/protein would be the most studied one for BNER. Our goal is to develop a gene/protein name recognition system BioTagger-GM that exploits rich information in terminology sources using powerful machine learning frameworks and system combination. DESIGN BioTagger-GM consists of four main components: (1) dictionary lookup-gene/protein names in BioThesaurus and biomedical terms in UMLS Metathesaurus are tagged in text, (2) machine learning-machine learning systems are trained using dictionary lookup results as one type of feature, (3) post-processing-heuristic rules are used to correct recognition errors, and (4) system combination-a voting scheme is used to combine recognition results from multiple systems. MEASUREMENTS The BioCreAtIvE II Gene Mention (GM) corpus was used to evaluate the proposed method. To test its general applicability, the method was also evaluated on the JNLPBA corpus modified for gene/protein name recognition. The performance of the systems was evaluated through cross-validation tests and measured using precision, recall, and F-Measure. RESULTS BioTagger-GM achieved an F-Measure of 0.8887 on the BioCreAtIvE II GM corpus, which is higher than that of the first-place system in the BioCreAtIvE II challenge. The applicability of the method was also confirmed on the modified JNLPBA corpus. CONCLUSION The results suggest that terminology sources, powerful machine learning frameworks, and system combination can be integrated to build an effective BNER system.

Steroidogenic Factor-1 Is an Essential Transcriptional Activator for Gonad-specific Expression of Promoter I of the Rat Prolactin Receptor Gene

The expression of the prolactin receptor is under the control of two putative tissue-specific (PI, gonads; PII, liver) and one common (PIII) promoters (Hu, Z. Z., Zhuang, L., and Dufau, M. L. (1996) J. Biol. Chem. 271, 10242–10246). The three promoter regions were co-localized to the rat chromosomal locus 2ql6, in the order 5′-PIII-PI-PII-3′. To investigate the mechanisms of gonad-specific utilization of PI, the promoter domain, regulatory cis-elements, and trans-factors were identified in gonadal cells. The promoter domain localized to the 152-base pair 5′ of the transcriptional start site at −549 is highly active in gonadal cells but has minimal activity in hepatoma cells. It contains a steroidogenic factor 1 (SF-1) element (−668) that binds the SF-1 protein of nuclear extracts from gonadal cells and is essential for promoter activation. A CCAAT box (−623) contributes minimally to basal activity in the absence of the SF-1 element, and two adjacent TATA-like sequences act as inhibitory elements. Thus, PI belongs to a class of TATA-less/non-initiator gene promoters. These findings demonstrate an essential role for SF-1 in transcriptional activation of promoter I of the prolactin receptor gene, which may explain the tissue-specific expression of PI in the gonads but not in the liver and the mammary gland.

Transcriptional regulation of the generic promoter III of the rat prolactin receptor gene by C/EBPbeta and Sp1.

Three promoters are operative in the rat prolactin receptor gene as follows: promoter I (PI) and II (PII) are specific for the gonads and liver, respectively, and promoter III (PIII) is common to several tissues. To investigate the mechanisms controlling the activity of promoter III, its regulatory elements and transcription factors were characterized in gonadal and non-gonadal cells. The TATA-less PIII domain was localized to the region −437 to −179 (ATG +1) containing the 5′-flanking region and part of the non-coding first exon. Within the promoter domain, a functional CAAT-box/enhancer binding protein (C/EBP) (−398) and an Sp1 element (−386), which bind C/EBPβ and Sp1/Sp3, respectively, contribute individually to promoter activation in gonadal and non-gonadal cells. However, significant redundancy was demonstrated between these elements in non-gonadal cells. Additionally, an element within the non-coding exon 1 (−338) is also required for promoter activity. Activation of PIII by the widely expressed Sp1 and C/EBPβ factors explains its common utilization in multiple tissues. Moreover, whereas the rat and mouse PIII share similar structure and function, the mouse PI lacks the functional SF-1 element and hence is inactive. These findings indicate that promoter III is of central importance in prolactin receptor gene transcription across species.

Isolation and characterization of two novel rat ovarian lactogen receptor cDNA species.

Two novel lactogen receptor cDNA clones (2.1 and 1.2 kb) were isolated from a rat ovarian cDNA library. Nucleotide sequence of the 2.1 kb clone codes for a 610 aa receptor (nonglycosylated mol. wgt. 66,000 D) with an extracellular domain, a transmembrane region and an intracellular domain, and exhibited significant overall similarity with the rat liver receptor (310 aa) and both rabbit mammary and human hepatoma receptors (616 and 622 aa). However, the ovarian lactogen receptor sequence contains a unique cytoplasmic domain of 110 aa and consensus sequences for both a tyrosine phosphorylation site and an ATP/GTP type A binding site, and thus has potential for signal transduction and mitogenic activity. The 1.2 kb clone codes for a truncated binding form of 150 aa that is identical with the ovarian long form over only the first 130 residues, and lacks the transmembrane region. Differences between long and short forms of the ovarian lactogen receptors and the truncated liver species may result from alternative splicing. The prolactin holoreceptor gene(s) has the potential for producing several receptor subtypes that differ in tissue-specific expression, size, compartmentalization and mode of signal transduction, and may subserve the divergent functions of prolactin in its several target cells.

Database Note: iProLINK: an integrated protein resource for literature mining

The exponential growth of large-scale molecular sequence data and of the PubMed scientific literature has prompted active research in biological literature mining and information extraction to facilitate genome/proteome annotation and improve the quality of biological databases. Motivated by the promise of text mining methodologies, but at the same time, the lack of adequate curated data for training and benchmarking, the Protein Information Resource (PIR) has developed a resource for protein literature mining--iProLINK (integrated Protein Literature INformation and Knowledge). As PIR focuses its effort on the curation of the UniProt protein sequence database, the goal of iProLINK is to provide curated data sources that can be utilized for text mining research in the areas of bibliography mapping, annotation extraction, protein named entity recognition, and protein ontology development. The data sources for bibliography mapping and annotation extraction include mapped citations (PubMed ID to protein entry and feature line mapping) and annotation-tagged literature corpora. The latter includes several hundred abstracts and full-text articles tagged with experimentally validated post-translational modifications (PTMs) annotated in the PIR protein sequence database. The data sources for entity recognition and ontology development include a protein name dictionary, word token dictionaries, protein name-tagged literature corpora along with tagging guidelines, as well as a protein ontology based on PIRSF protein family names. iProLINK is freely accessible at http://pir.georgetown.edu/iprolink, with hypertext links for all downloadable files.

Hormonal regulation of LH receptor mRNA and expression in the rat ovary

Agonist‐induced changes in expression and mRNA levels of luteinizing hormone (LH) receptors were compared during stimulation of ovarian follicular maturation and luteinization by gonadotropic hormones. Three major species of LH receptor mRNA, 5.8, 2.6 and 2.3 kb, were present throughout differentiation and changed similarly, the 5.8 kb species being consistently more abundant than the smaller forms. The increased expression of plasma‐membrane LH receptors in preovulatory follicles and luteinized ovaries and their homologous down‐regulation during follicular and luteal desensitization were closely correlated with the steady‐state receptor mRNA levels. The reappearance of LH receptors following desensitization during the luteal stage was preceded by an increase in mRNA levels. These studies have demonstrated that the expression of LH receptors during follicular maturation, ovulation and desensitization is related to the prevailing levels of receptor mRNA in the ovary.