Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “protein function annotation”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6Linked to original sources
PubMed2006

A wiring of the human nucleolus.

Recent proteomic efforts have created an extensive inventory of the human nucleolar proteome. However, approximately 30% of the identified proteins lack functional annotation. We present an approach of assigning function to uncharacterized nucleolar proteins by data integration coupled to a machine-learning method. By assembling protein complexes, we present a first draft of the human ribosome biogenesis pathway encompassing 74 proteins and hereby assign function to 49 previously uncharacterized proteins. Moreover, the functional diversity of the nucleolus is underlined by the identification of a number of protein complexes with functions beyond ribosome biogenesis. Finally, we were able to obtain experimental evidence of nucleolar localization of 11 proteins, which were predicted by our platform to be associates of nucleolar complexes. We believe other biological organelles or systems could be "wired" in a similar fashion, integrating different types of data with high-throughput proteomics, followed by a detailed biological analysis and experimental validation.

Artificial Intelligence↗
PubMed2005

Proteome analysis of the rice etioplast: metabolic and regulatory networks and novel protein functions.

We report an extensive proteome analysis of rice etioplasts, which were highly purified from dark-grown leaves by a novel protocol using Nycodenz density gradient centrifugation. Comparative protein profiling of different cell compartments from leaf tissue demonstrated the purity of the etioplast preparation by the absence of diagnostic marker proteins of other cell compartments. Systematic analysis of the etioplast proteome identified 240 unique proteins that provide new insights into heterotrophic plant metabolism and control of gene expression. They include several new proteins that were not previously known to localize to plastids. The etioplast proteins were compared with proteomes from Arabidopsis chloroplasts and plastid from tobacco Bright Yellow 2 cells. Together with computational structure analyses of proteins without functional annotations, this comparative proteome analysis revealed novel etioplast-specific proteins. These include components of the plastid gene expression machinery such as two RNA helicases, an RNase II-like hydrolytic exonuclease, and a site 2 protease-like metalloprotease all of which were not known previously to localize to the plastid and are indicative for so far unknown regulatory mechanisms of plastid gene expression. All etioplast protein identifications and related data were integrated into a data base that is freely available upon request.

Amino Acid Sequence↗
PubMed2005

Evaluation of BioCreAtIvE assessment of task 2.

BACKGROUND: Molecular Biology accumulated substantial amounts of data concerning functions of genes and proteins. Information relating to functional descriptions is generally extracted manually from textual data and stored in biological databases to build up annotations for large collections of gene products. Those annotation databases are crucial for the interpretation of large scale analysis approaches using bioinformatics or experimental techniques. Due to the growing accumulation of functional descriptions in biomedical literature the need for text mining tools to facilitate the extraction of such annotations is urgent. In order to make text mining tools useable in real world scenarios, for instance to assist database curators during annotation of protein function, comparisons and evaluations of different approaches on full text articles are needed. RESULTS: The Critical Assessment for Information Extraction in Biology (BioCreAtIvE) contest consists of a community wide competition aiming to evaluate different strategies for text mining tools, as applied to biomedical literature. We report on task two which addressed the automatic extraction and assignment of Gene Ontology (GO) annotations of human proteins, using full text articles. The predictions of task 2 are based on triplets of protein--GO term--article passage. The annotation-relevant text passages were returned by the participants and evaluated by expert curators of the GO annotation (GOA) team at the European Institute of Bioinformatics (EBI). Each participant could submit up to three results for each sub-task comprising task 2. In total more than 15,000 individual results were provided by the participants. The curators evaluated in addition to the annotation itself, whether the protein and the GO term were correctly predicted and traceable through the submitted text fragment. CONCLUSION: Concepts provided by GO are currently the most extended set of terms used for annotating gene products, thus they were explored to assess how effectively text mining tools are able to extract those annotations automatically. Although the obtained results are promising, they are still far from reaching the required performance demanded by real world applications. Among the principal difficulties encountered to address the proposed task, were the complex nature of the GO terms and protein names (the large range of variants which are used to express proteins and especially GO terms in free text), and the lack of a standard training set. A range of very different strategies were used to tackle this task. The dataset generated in line with the BioCreative challenge is publicly available and will allow new possibilities for training information extraction methods in the domain of molecular biology.

Computational Biology↗
PubMed2006

Diffusion kernel-based logistic regression models for protein function prediction.

Assigning functions to unknown proteins is one of the most important problems in proteomics. Several approaches have used protein-protein interaction data to predict protein functions. We previously developed a Markov random field (MRF) based method to infer a protein's functions using protein-protein interaction data and the functional annotations of its protein interaction partners. In the original model, only direct interactions were considered and each function was considered separately. In this study, we develop a new model which extends direct interactions to all neighboring proteins, and one function to multiple functions. The goal is to understand a protein's function based on information on all the neighboring proteins in the interaction network. We first developed a novel kernel logistic regression (KLR) method based on diffusion kernels for protein interaction networks. The diffusion kernels provide means to incorporate all neighbors of proteins in the network. Second, we identified a set of functions that are highly correlated with the function of interest, referred to as the correlated functions, using the chi-square test. Third, the correlated functions were incorporated into our new KLR model. Fourth, we extended our model by incorporating multiple biological data sources such as protein domains, protein complexes, and gene expressions by converting them into networks. We showed that the KLR approach of incorporating all protein neighbors significantly improved the accuracy of protein function predictions over the MRF model. The incorporation of multiple data sets also improved prediction accuracy. The prediction accuracy is comparable to another protein function classifier based on the support vector machine (SVM), using a diffusion kernel. The advantages of the KLR model include its simplicity as well as its ability to explore the contribution of neighbors to the functions of proteins of interest.

Databases, Protein↗
PubMed2006

A Protein Classification Benchmark collection for machine learning.

Protein classification by machine learning algorithms is now widely used in structural and functional annotation of proteins. The Protein Classification Benchmark collection (http://hydra.icgeb.trieste.it/benchmark) was created in order to provide standard datasets on which the performance of machine learning methods can be compared. It is primarily meant for method developers and users interested in comparing methods under standardized conditions. The collection contains datasets of sequences and structures, and each set is subdivided into positive/negative, training/test sets in several ways. There is a total of 6405 classification tasks, 3297 on protein sequences, 3095 on protein structures and 10 on protein coding regions in DNA. Typical tasks include the classification of structural domains in the SCOP and CATH databases based on their sequences or structures, as well as various functional and taxonomic classification problems. In the case of hierarchical classification schemes, the classification tasks can be defined at various levels of the hierarchy (such as classes, folds, superfamilies, etc.). For each dataset there are distance matrices available that contain all vs. all comparison of the data, based on various sequence or structure comparison methods, as well as a set of classification performance measures computed with various classifier algorithms.

Algorithms↗
PubMed2003

Distinguishing enzyme structures from non-enzymes without alignments.

The ability to predict protein function from structure is becoming increasingly important as the number of structures resolved is growing more rapidly than our capacity to study function. Current methods for predicting protein function are mostly reliant on identifying a similar protein of known function. For proteins that are highly dissimilar or are only similar to proteins also lacking functional annotations, these methods fail. Here, we show that protein function can be predicted as enzymatic or not without resorting to alignments. We describe 1178 high-resolution proteins in a structurally non-redundant subset of the Protein Data Bank using simple features such as secondary-structure content, amino acid propensities, surface properties and ligands. The subset is split into two functional groupings, enzymes and non-enzymes. We use the support vector machine-learning algorithm to develop models that are capable of assigning the protein class. Validation of the method shows that the function can be predicted to an accuracy of 77% using 52 features to describe each protein. An adaptive search of possible subsets of features produces a simplified model based on 36 features that predicts at an accuracy of 80%. We compare the method to sequence-based methods that also avoid calculating alignments and predict a recently released set of unrelated proteins. The most useful features for distinguishing enzymes from non-enzymes are secondary-structure content, amino acid frequencies, number of disulphide bonds and size of the largest cleft. This method is applicable to any structure as it does not require the identification of sequence or structural similarity to a protein of known function.

Algorithms↗
PubMed2003

The Nuclear Protein Database (NPD): sub-nuclear localisation and functional annotation of the nuclear proteome.

The Nuclear Protein Database (NPD) is a curated database that contains information on more than 1300 vertebrate proteins that are thought, or are known, to localise to the cell nucleus. Each entry is annotated with information on predicted protein size and isoelectric point, as well as any repeats, motifs or domains within the protein sequence. In addition, information on the sub-nuclear localisation of each protein is provided and the biological and molecular functions are described using Gene Ontology (GO) terms. The database is searchable by keyword, protein name, sub-nuclear compartment and protein domain/motif. Links to other databases are provided (e.g. Entrez, SWISS-PROT, OMIM, PubMed, PubMed Central). Thus, NPD provides a gateway through which the nuclear proteome may be explored. The database can be accessed at http://npd.hgu.mrc.ac.uk and is updated monthly.

Amino Acid Sequence↗
PubMed2006

Evolution of enzyme superfamilies.

Enzyme evolution is often constrained by aspects of catalysis. Sets of homologous proteins that catalyze different overall reactions but share an aspect of catalysis, such as a common partial reaction, are called mechanistically diverse superfamilies. The common mechanistic steps and structural characteristics of several of these superfamilies, including the enolase, Nudix, amidohydrolase, and haloacid dehalogenase superfamilies have been characterized. In addition, studies of mechanistically diverse superfamilies are helping to elucidate mechanisms of functional diversification, such as catalytic promiscuity. Understanding how enzyme superfamilies evolve is vital for accurate genome annotation, predicting protein functions, and protein engineering.

Binding Sites↗
PubMed2002

The CATH protein family database: a resource for structural and functional annotation of genomes.

Over the last decade, there have been huge increases in the numbers of protein sequences and structures determined. In parallel, many methods have been developed for recognising similarities between these proteins, arising from their common evolutionary background, and for clustering such relatives into protein families. Here we review some of the protein family resources available to the biologist and describe how these can be used to provide structural and functional annotations for newly determined sequences. In particular we describe recent developments to the CATH domain database of protein structural families which have facilitated genome annotation and which have also revealed important caveats that must be considered when transferring functional data between homologous proteins.

Databases, Protein↗
PubMed2006

Modelling interaction sites in protein domains with interaction profile hidden Markov models.

MOTIVATION: Due to the growing number of completely sequenced genomes, functional annotation of proteins becomes a more and more important issue. Here, we describe a method for the prediction of sites within protein domains, which are part of protein-ligand interactions. As recently demonstrated, these sites are not trivial to detect because of a varying degree of conservation of their location and type within a domain family. RESULTS: The developed method for the prediction of protein-ligand interaction sites is based on a newly defined interaction profile hidden Markov model (ipHMM) topology that takes structural and sequence data into account. It is based on a homology search via a posterior decoding algorithm that yields probabilities for interacting sequence positions and inherits the efficiency and the power of the profile hidden Markov model (pHMM) methodology. The algorithm enhances the quality of interaction site predictions and is a suitable tool for large scale studies, which was already demonstrated for pHMMs. AVAILABILITY: The MATLAB-files are available on request from the first author.

Algorithms↗
PubMed2002

Ab initio protein structure prediction.

Steady progress has been made in the field of ab initio protein folding. A variety of methods now allow the prediction of low-resolution structures of small proteins or protein fragments up to approximately 100 amino acid residues in length. Such low-resolution structures may be sufficient for the functional annotation of protein sequences on a genome-wide scale. Although no consistently reliable algorithm is currently available, the essential challenges to developing a general theory or approach to protein structure prediction are better understood. The energy landscapes resulting from the structure prediction algorithms are only partially funneled to the native state of the protein. This review focuses on two areas of recent advances in ab initio structure prediction-improvements in the energy functions and strategies to search the caldera region of the energy landscapes.

Chemistry, Physical↗
PubMed2005

Methods for the functional genomic analysis of ubiquitin ligases.

Ubiquitin ligases (E3s) are critical components of the ubiquitin-proteasome system as they are the major determinants of specificity in ubiquitin conjugation. The number of predicted E3s in the mammalian genome is exceeding 400 and is represented by two major subfamilies: HECT domain-containing E3s and RING finger-type E3s. Given the size of this protein family and lack of knowledge on the functions of most of these 400 proteins, their functional annotation should benefit from modern genomic tools. This article presents a methodology consisting of the use of a cDNA expression library to identify suppressors of polyglutamine (polyQ)-mediated protein aggregate formation in cells, as an example of a genomic approach to assign functions to E3s. In this screen, we identified novel RING finger-type E3s exhibiting suppressor activity among >50% of all the potential E3s in the mouse and human genomes. This method could be adapted easily to identify E3s that function in other processes and signaling pathways.

Algorithms↗
PubMed2005

Protein variety and functional diversity: Swiss-Prot annotation in its biological context.

We all know that the dogma 'one gene, one protein' is obsolete. A functional protein and, likewise, a protein's ultimate function depend not only on the underlying genetic information but also on the ongoing conditions of the cellular system. Frequently the transcript, like the polypeptide, is processed in multiple ways, but only one or a few out of a multitude of possible variants are produced at a time. An overview on processes that can lead to sequence variety and structural diversity in eukaryotes is given. The UniProtKB/Swiss-Prot protein knowledgebase provides a wealth of information regarding protein variety, function and associated disorders. Examples for such annotation are shown and further ones are available at http://www.expasy.org/sprot/tutorial/examples_CRB.

Amino Acid Sequence↗
PubMed2005

Ontological visualization of protein-protein interactions.

BACKGROUND: Cellular processes require the interaction of many proteins across several cellular compartments. Determining the collective network of such interactions is an important aspect of understanding the role and regulation of individual proteins. The Gene Ontology (GO) is used by model organism databases and other bioinformatics resources to provide functional annotation of proteins. The annotation process provides a mechanism to document the binding of one protein with another. We have constructed protein interaction networks for mouse proteins utilizing the information encoded in the GO annotations. The work reported here presents a methodology for integrating and visualizing information on protein-protein interactions. RESULTS: GO annotation at Mouse Genome Informatics (MGI) captures 1318 curated, documented interactions. These include 129 binary interactions and 125 interaction involving three or more gene products. Three networks involve over 30 partners, the largest involving 109 proteins. Several tools are available at MGI to visualize and analyze these data. CONCLUSIONS: Curators at the MGI database annotate protein-protein interaction data from experimental reports from the literature. Integration of these data with the other types of data curated at MGI places protein binding data into the larger context of mouse biology and facilitates the generation of new biological hypotheses based on physical interactions among gene products.

Animals↗
PubMed2006

Social behavior of the yeast protein-protein interaction network.

Protein-protein interaction networks are useful in contextual annotation of protein function and in general to achieve a system-level understanding of cellular behavior. This work reports on the social behavior of the yeast protein-protein interaction network and concludes that it is non-random. This work, while providing an analysis of organization of genes into functional societies, can potentially be useful in assessing the accuracy of contextual gene annotation based on such interaction networks.

Computational Biology↗
PubMed2003

Identifying property based sequence motifs in protein families and superfamilies: application to DNase-1 related endonucleases.

MOTIVATION: Identification of short conserved sequence motifs common to a protein family or superfamily can be more useful than overall sequence similarity in suggesting the function of novel gene products. Locating motifs still requires expert knowledge, as automated methods using stringent criteria may not differentiate subtle similarities from statistical noise. RESULTS: We have developed a novel automatic method, based on patterns of conservation of 237 physical-chemical properties of amino acids in aligned protein sequences, to find related motifs in proteins with little or no overall sequence similarity. As an application, our web-server MASIA identified 12 property-based motifs in the apurinic/apyrimidinic endonuclease (APE) family of DNA-repair enzymes of the DNase-I superfamily. Searching with these motifs located distantly related representatives of the DNase-I superfamily, such as Inositol 5'-polyphosphate phosphatases in the ASTRAL40 database, using a Bayesian scoring function. Other proteins containing APE motifs had no overall sequence or structural similarity. However, all were phosphatases and/or had a metal ion binding active site. Thus our automated method can identify discrete elements in distantly related proteins that define local structure and aspects of function. We anticipate that our method will complement existing ones to functionally annotate novel protein sequences from genomic projects. AVAILABILITY: MASIA WEB site: http://www.scsb.utmb.edu/masia/masia.html SUPPLEMENTARY INFORMATION: The dendrogram of 42 APE sequences used to derive motifs is available on http://www.scsb.utmb.edu/comp_biol.html/DNA_repair/publication.html

Algorithms↗
PubMed2005

GeneFarm, structural and functional annotation of Arabidopsis gene and protein families by a network of experts.

Genomic projects heavily depend on genome annotations and are limited by the current deficiencies in the published predictions of gene structure and function. It follows that, improved annotation will allow better data mining of genomes, and more secure planning and design of experiments. The purpose of the GeneFarm project is to obtain homogeneous, reliable, documented and traceable annotations for Arabidopsis nuclear genes and gene products, and to enter them into an added-value database. This re-annotation project is being performed exhaustively on every member of each gene family. Performing a family-wide annotation makes the task easier and more efficient than a gene-by-gene approach since many features obtained for one gene can be extrapolated to some or all the other genes of a family. A complete annotation procedure based on the most efficient prediction tools available is being used by 16 partner laboratories, each contributing annotated families from its field of expertise. A database, named GeneFarm, and an associated user-friendly interface to query the annotations have been developed. More than 3000 genes distributed over 300 families have been annotated and are available at http://genoplante-info.infobiogen.fr/Genefarm/. Furthermore, collaboration with the Swiss Institute of Bioinformatics is underway to integrate the GeneFarm data into the protein knowledgebase Swiss-Prot.

Arabidopsis↗
PubMed2006

Rank information: a structure-independent measure of evolutionary trace quality that improves identification of protein functional sites.

Protein functional sites are key targets for drug design and protein engineering, but their large-scale experimental characterization remains difficult. The evolutionary trace (ET) is a computational approach to this problem that has been useful in a variety of case studies, but its proteomic scale application is partially hindered because automated retrieval of input sequences from databases often includes some with errors that degrade functional site identification. To recognize and purge these sequences, this study introduces a novel and structure-free measure of ET quality called rank information (RI). It is shown that RI decreases in response to errors in sequences, alignments, or functional classifications. Conversely, an automated procedure to increase RI by selectively removing sequences improves functional site identification so as to nearly match manually curated traces in kinases and in a test set of 79 diverse proteins. Thus we conclude that RI partially reflects the evolutionary consistency of sequence, structure, and function. In practice, as the size of the proteome continues to grow exponentially, it provides a novel and structure-free measure of ET quality that increases its accuracy for large-scale automated annotation of protein functional sites.

Algorithms↗