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At least 73 records · Page 4Linked to original sources
PubMed2004

BioBuilder as a database development and functional annotation platform for proteins.

BACKGROUND: The explosion in biological information creates the need for databases that are easy to develop, easy to maintain and can be easily manipulated by annotators who are most likely to be biologists. However, deployment of scalable and extensible databases is not an easy task and generally requires substantial expertise in database development. RESULTS: BioBuilder is a Zope-based software tool that was developed to facilitate intuitive creation of protein databases. Protein data can be entered and annotated through web forms along with the flexibility to add customized annotation features to protein entries. A built-in review system permits a global team of scientists to coordinate their annotation efforts. We have already used BioBuilder to develop Human Protein Reference Database http://www.hprd.org, a comprehensive annotated repository of the human proteome. The data can be exported in the extensible markup language (XML) format, which is rapidly becoming as the standard format for data exchange. CONCLUSIONS: As the proteomic data for several organisms begins to accumulate, BioBuilder will prove to be an invaluable platform for functional annotation and development of customizable protein centric databases. BioBuilder is open source and is available under the terms of LGPL.

Computational Biology↗
PubMed2003

Inferring functional relationships of proteins from local sequence and spatial surface patterns.

We describe a novel approach for inferring functional relationship of proteins by detecting sequence and spatial patterns of protein surfaces. Well-formed concave surface regions in the form of pockets and voids are examined to identify similarity relationship that might be directly related to protein function. We first exhaustively identify and measure analytically all 910,379 surface pockets and interior voids on 12,177 protein structures from the Protein Data Bank. The similarity of patterns of residues forming pockets and voids are then assessed in sequence, in spatial arrangement, and in orientational arrangement. Statistical significance in the form of E and p-values is then estimated for each of the three types of similarity measurements. Our method is fully automated without human intervention and can be used without input of query patterns. It does not assume any prior knowledge of functional residues of a protein, and can detect similarity based on surface patterns small and large. It also tolerates, to some extent, conformational flexibility of functional sites. We show with examples that this method can detect functional relationship with specificity for members of the same protein family and superfamily, as well as remotely related functional surfaces from proteins of different fold structures. We envision that this method can be used for discovering novel functional relationship of protein surfaces, for functional annotation of protein structures with unknown biological roles, and for further inquiries on evolutionary origins of structural elements important for protein function.

Amino Acid Sequence↗
PubMed2000

PIR: a new resource for bioinformatics.

UNLABELLED: The Protein Information Resource (PIR) has greatly expanded its Web site and developed a set of interactive search and analysis tools to facilitate the analysis, annotation, and functional identification of proteins. New search engines have been implemented to combine sequence similarity search results with database annotation information. The new PIR search systems have proved very useful in providing enriched functional annotation of protein sequences, determining protein superfamily-domain relationships, and detecting annotation errors in genomic database archives. AVAILABILITY: http://pir.georgetown.edu/. CONTACT: mcgarvey@nbrf.georgetown.edu

Animals↗
PubMed2001

Structural genomics: opportunities and challenges.

Following the complete genome sequencing of an increasing number of organisms, structural biology is engaging in a systematic approach of high-throughput structure determination called structural genomics to create a complete inventory of protein folds/structures that will help predict functions for all proteins. First results show that structural genomics will be highly effective in finding functional annotations for proteins of unknown function.

Crystallography, X-Ray↗
PubMed2001

Annotation transfer for genomics: measuring functional divergence in multi-domain proteins.

Annotation transfer is a principal process in genome annotation. It involves "transferring" structural and functional annotation to uncharacterized open reading frames (ORFs) in a newly completed genome from experimentally characterized proteins similar in sequence. To prevent errors in genome annotation, it is important that this process be robust and statistically well-characterized, especially with regard to how it depends on the degree of sequence similarity. Previously, we and others have analyzed annotation transfer in single-domain proteins. Multi-domain proteins, which make up the bulk of the ORFs in eukaryotic genomes, present more complex issues in functional conservation. Here we present a large-scale survey of annotation transfer in these proteins, using scop superfamilies to define domain folds and a thesaurus based on SWISS-PROT keywords to define functional categories. Our survey reveals that multi-domain proteins have significantly less functional conservation than single-domain ones, except when they share the exact same combination of domain folds. In particular, we find that for multi-domain proteins, approximate function can be accurately transferred with only 35% certainty for pairs of proteins sharing one structural superfamily. In contrast, this value is 67% for pairs of single-domain proteins sharing the same structural superfamily. On the other hand, if two multi-domain proteins contain the same combination of two structural superfamilies the probability of their sharing the same function increases to 80% in the case of complete coverage along the full length of both proteins, this value increases further to > 90%. Moreover, we found that only 70 of the current total of 455 structural superfamilies are found in both single and multi-domain proteins and only 14 of these were associated with the same function in both categories of proteins. We also investigated the degree to which function could be transferred between pairs of multi-domain proteins with respect to the degree of sequence similarity between them, finding that functional divergence at a given amount of sequence similarity is always about two-fold greater for pairs of multi-domain proteins (sharing similarity over a single domain) in comparison to pairs of single-domain ones, though the overall shape of the relationship is quite similar. Further information is available at http://partslist.org/func or http://bioinfo.mbb.yale.edu/partslist/func.

Computational Biology↗
PubMed2004

SURFACE: a database of protein surface regions for functional annotation.

The SURFACE (SUrface Residues and Functions Annotated, Compared and Evaluated, URL http://cbm.bio.uniroma2.it/surface/) database is a repository of annotated and compared protein surface regions. SURFACE contains the results of a large-scale protein annotation and local structural comparison project. A non-redundant set of protein chains is used to build a database of protein surface patches, defined as putative surface functional sites. Each patch is annotated with sequence and structure-derived information about function or interaction abilities. A new procedure for structure comparison is used to perform an all-versus-all patches comparison. Selection of the results obtained with stringent parameters offers a similarity score that can be used to associate different patches and allows reliable annotation by similarity. Annotation exerted through the comparison of regions of protein surface allows the highlighting of similarities that cannot be recognized by other methods of sequence or structure comparison. A graphic representation of the surface patches, functional annotations and the structural superpositions is available through the web interface.

Algorithms↗
PubMed2004

Using shared genomic synteny and shared protein functions to enhance the identification of orthologous gene pairs.

MOTIVATION: The identification of orthologous gene pairs is generally based on sequence similarity. Gene pairs that are mutually 'best hits' between the genomes being compared are asserted to be orthologs. Although this method identifies most orthologous gene pairs with high confidence, it will miss a fraction of them, especially genes in duplicated gene families. In addition, the approach depends heavily on the completeness and quality of gene annotation. When the gene sequences are not correctly represented the approach is unlikely to find the correct ortholog. To overcome these limitations, we have developed an approach to identify orthologous gene pairs using shared chromosomal synteny and the annotation of protein function. RESULTS: Assembled mouse and human genomes were used to identify the regions of conserved synteny between these genomes. 'Syntenic anchors' are conserved non-repetitive locations between mouse and human genomes. Using these anchors, we identified blocks of sequences that contain consistently ordered anchors between the two genomes (syntenic blocks). The synteny information has been used to help us identify orthologous gene pairs between mouse and human genomes. The approach combines the mutual selection of the best tBlastX hits between human and mouse transcripts, and inferring gene orthologous relationships based on sharing syntenic anchors, collocating in the same syntenic blocks and sharing the same annotated protein function. Using this approach, we were able to find 19,357 orthologous gene pairs between human and mouse genomes, a 20% increase in the number of orthologs identified by conventional approaches.

Algorithms↗
PubMed2006

A text-mining analysis of the human phenome.

A number of large-scale efforts are underway to define the relationships between genes and proteins in various species. But, few attempts have been made to systematically classify all such relationships at the phenotype level. Also, it is unknown whether such a phenotype map would carry biologically meaningful information. We have used text mining to classify over 5000 human phenotypes contained in the Online Mendelian Inheritance in Man database. We find that similarity between phenotypes reflects biological modules of interacting functionally related genes. These similarities are positively correlated with a number of measures of gene function, including relatedness at the level of protein sequence, protein motifs, functional annotation, and direct protein-protein interaction. Phenotype grouping reflects the modular nature of human disease genetics. Thus, phenotype mapping may be used to predict candidate genes for diseases as well as functional relations between genes and proteins. Such predictions will further improve if a unified system of phenotype descriptors is developed. The phenotype similarity data are accessible through a web interface at http://www.cmbi.ru.nl/MimMiner/.

Chromosome Mapping↗
PubMed2006

DWARF--a data warehouse system for analyzing protein families.

BACKGROUND: The emerging field of integrative bioinformatics provides the tools to organize and systematically analyze vast amounts of highly diverse biological data and thus allows to gain a novel understanding of complex biological systems. The data warehouse DWARF applies integrative bioinformatics approaches to the analysis of large protein families. DESCRIPTION: The data warehouse system DWARF integrates data on sequence, structure, and functional annotation for protein fold families. The underlying relational data model consists of three major sections representing entities related to the protein (biochemical function, source organism, classification to homologous families and superfamilies), the protein sequence (position-specific annotation, mutant information), and the protein structure (secondary structure information, superimposed tertiary structure). Tools for extracting, transforming and loading data from public available resources (ExPDB, GenBank, DSSP) are provided to populate the database. The data can be accessed by an interface for searching and browsing, and by analysis tools that operate on annotation, sequence, or structure. We applied DWARF to the family of alpha/beta-hydrolases to host the Lipase Engineering database. Release 2.3 contains 6138 sequences and 167 experimentally determined protein structures, which are assigned to 37 superfamilies 103 homologous families. CONCLUSION: DWARF has been designed for constructing databases of large structurally related protein families and for evaluating their sequence-structure-function relationships by a systematic analysis of sequence, structure and functional annotation. It has been applied to predict biochemical properties from sequence, and serves as a valuable tool for protein engineering.

Amino Acid Sequence↗
PubMed2026

Genomic and Structural Analysis of Gamete Recognition Proteins in a Broadcast Spawning Echinoderm Mesocentrotus franciscanus.

Gamete recognition proteins are expressed on the surfaces of sperm and eggs, where they mediate interactions between gametes. The genetic basis for gamete recognition proteins, as well as their structure and interactions, have yet to be fully resolved. Using a new high-quality de novo genome assembly for the sea urchin Mesocentrotus franciscanus, we investigated the genomic structure, expression, and protein forms of several gamete recognition proteins: sperm bindin, egg receptor for sperm (HSP110), and egg bindin receptor (EBR1), as well as the receptor for egg jelly (REJ) and its paralogs. To inform future population genetic and evolutionary studies, we resolve the genomic structure of the large EBR1 protein, identifying fewer tandem CUB-TSP1 repeats in EBR1 compared to the initial characterization of this protein. As expected for an egg receptor for sperm, EBR1 is highly expressed in female reproductive tissues (eggs and female gonad), compared to other tissues. In contrast, HSP110 shows similar levels of expression across male and female reproductive tissues, as well as across non-reproductive tissues and development stages. HSP110 might be a pleiotropic gene that in part influences fertilization. Using protein structural modeling and functional domain predictions, we propose hypotheses about potential interactions among EBR1, bindin, and HSP110 proteins that may provide insight into sperm-egg interactions in sea urchins. Resolving the genomic structure of genes encoding gamete recognition proteins, in combination with functional annotations and protein structural modeling, enables deeper investigation into the consequences of variation in gamete recognition proteins and the evolution of reproductive isolation.

Mesocentrotus franciscanus↗
PubMed2005

Protein surface analysis for function annotation in high-throughput structural genomics pipeline.

Structural genomics (SG) initiatives are expanding the universe of protein fold space by rapidly determining structures of proteins that were intentionally selected on the basis of low sequence similarity to proteins of known structure. Often these proteins have no associated biochemical or cellular functions. The SG success has resulted in an accelerated deposition of novel structures. In some cases the structural bioinformatics analysis applied to these novel structures has provided specific functional assignment. However, this approach has also uncovered limitations in the functional analysis of uncharacterized proteins using traditional sequence and backbone structure methodologies. A novel method, named pvSOAR (pocket and void Surface of Amino Acid Residues), of comparing the protein surfaces of geometrically defined pockets and voids was developed. pvSOAR was able to detect previously unrecognized and novel functional relationships between surface features of proteins. In this study, pvSOAR is applied to several structural genomics proteins. We examined the surfaces of YecM, BioH, and RpiB from Escherichia coli as well as the CBS domains from inosine-5'-monosphate dehydrogenase from Streptococcus pyogenes, conserved hypothetical protein Ta549 from Thermoplasm acidophilum, and CBS domain protein mt1622 from Methanobacterium thermoautotrophicum with the goal to infer information about their biochemical function.

Adenine Nucleotides↗
PubMed2002

Prediction of protein function using protein-protein interaction data.

Assigning functions to novel proteins is one of the most important problems in the post-genomic era. Several approaches have been applied to this problem, including analyzing gene expression patterns, phylogenetic profiles, protein fusions and protein-protein interactions. We develop a novel approach that applies the theory of Markov random fields to infer a protein's functions using protein-protein interaction data and the functional annotations of its interaction protein partners. For each function of interest and a protein, we predict the probability that the protein has that function using Bayesian approaches. Unlike in other available approaches for protein annotation where a protein has or does not have a function of interest, we give a probability for having the function. This probability indicates how confident we are about the prediction. We apply our method to predict cellular functions (43 categories including a category "others") for yeast proteins defined in the Yeast Proteome Database (YPD), using the protein-protein interaction data from the Munich Information Center for Protein Sequences (MIPS, http://mips.gsf.de). We show that our approach outperforms other available methods for function prediction based on protein interaction data.

Amino Acid Sequence↗
PubMed2002

Protein interaction verification and functional annotation by integrated analysis of genome-scale data.

Assays capable of determining the properties of thousands of genes in parallel present challenges with regard to accurate data processing and functional annotation. Collections of microarray expression data are applied here to assess the quality of different high-throughput protein interaction data sets. Significant differences are found. Confidence in 973 out of 5342 putative two-hybrid interactions from S. cerevisiae is increased. Besides verification, integration of expression and interaction data is employed to provide functional annotation for over 300 previously uncharacterized genes. The robustness of these approaches is demonstrated by experiments that test the in silico predictions made. This study shows how integration improves the utility of different types of functional genomic data and how well this contributes to functional annotation.

Animals↗
PubMed2004

Conserved spatially interacting motifs of protein superfamilies: application to fold recognition and function annotation of genome data.

Limitations in techniques for the elucidation of protein function have led to an increasing gap between the annotated proteins and those encoded in a genome. The functional selection and three-dimensional structural constraints of proteins in nature often relate to the retention of significant sequence similarity between proteins of similar fold and function despite poor sequence identity. We identify spatially interacting conserved regions, or motifs, within protein superfamilies that are critical for structure and/or function. A search in sequence databases using these descriptors as additional constraints is an approach to identifying putative additional members of superfamilies. Such constrained searches have been tested against proteins of known structure to demonstrate high percentage specificity (93) with a low error rate of 0.0004. This approach has been compared with other sensitive sequence search methods (e.g., PSI-BLAST, HMMsearch, and IMPALA). It has been extended to analyze the distribution of 11 superfamilies in 93 genomes, including the human genome.

Amino Acid Motifs↗
PubMed2004

Selective enrichment of monospecific polyclonal antibodies for antibody-based proteomics efforts.

A high stringency protocol, suitable for systematic purification of polyclonal antibodies, is described. The procedure is designed to allow the generation of target protein-specific antibodies suitable for functional annotation of proteins. Antibodies were generated by immunization with recombinantly produced affinity-tagged target proteins. To obtain stringent recovery of the antibodies, a two-step affinity chromatography principle was devised to first deplete the affinity tag-specific antibodies followed by a second step for affinity capture of the target protein-specific antibodies. An analytical dot-blot array system was developed to analyze the cross-reactivity of the affinity-purified antibodies. The results suggest that the protocol can be used in a highly parallel and automated manner to generate mono-specific polyclonal antibodies for large-scale, antibody-based proteomics efforts, i.e. affinity proteomics.

Antibodies↗
PubMed2003

A bioinformatics-based approach for the prediction and identification of novel proteins potentially involved in phosphorylation signalling pathways.

Together with the explosion in the availability of genome data of a number of organisms including human and mouse, various methods and programs for computational prediction of protein-coding genes and annotation of functional proteins have dramatically increased. For the last decade there has been intense interest in the role of protein phosphorylation which is involved in post-translation modification mechanisms critically regulating inter/intracellular communication, patho/physiological responses and homeostasis during many biological processes. In the present study a total of 202 functionally uncharacterized human full-coding cDNA sequences were investigated using a bioinformatics-based approach. Ten novel potential substrates for protein kinases have been identified which may play multiple roles in regulating intracellular phosphorylation signalling pathways. In addition, 5 of those may be involved in the human-only post-translation mechanism regulated by specific protein kinases. The data presented here therefore would greatly contribute toward the understanding of human molecular basis and cellular signalling networks.

Amino Acid Motifs↗
PubMed1998

Sources of systematic error in functional annotation of genomes: domain rearrangement, non-orthologous gene displacement and operon disruption.

Functional annotation of proteins encoded in newly sequenced genomes can be expected to meet two conflicting objectives: (i) provide as much information as possible, and (ii) avoid erroneous functional assignments and over-predictions. The continuing exponential growth of the number of sequenced genomes makes the quality of sequence annotation a critical factor in the efforts to utilize this new information. When dubious functional assignments are used as a basis for subsequent predictions, they tend to proliferate, leading to "database explosion". It is therefore important to identify the common factors that hamper functional annotation. As a first step towards that goal, we have compared the annotations of the Mycoplasma genitalium and Methanococcus jannaschii genomes produced in several independent studies. The most common causes of questionable predictions appear to be: i) non-critical use of annotations from existing database entries; ii) taking into account only the annotation of the best database hit; iii) insufficient masking of low complexity regions (e.g. non-globular domains) in protein sequences, resulting in spurious database hits obscuring relevant ones; iv) ignoring multi-domain organization of the query proteins and/or the database hits; v) non-critical functional inferences on the basis of the functions of neighboring genes in an operon; vi) non-orthologous gene displacement, i.e. involvement of structurally unrelated proteins in the same function. These observations suggest that case by case validation of functional annotation by expert biologists remains crucial for productive genome analysis.

Databases, Factual↗