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Results for “protein function annotation”

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PubMed2005

Metalloproteomics: high-throughput structural and functional annotation of proteins in structural genomics.

A high-throughput method for measuring transition metal content based on quantitation of X-ray fluorescence signals was used to analyze 654 proteins selected as targets by the New York Structural GenomiX Research Consortium. Over 10% showed the presence of transition metal atoms in stoichiometric amounts; these totals as well as the abundance distribution are similar to those of the Protein Data Bank. Bioinformatics analysis of the identified metalloproteins in most cases supported the metalloprotein annotation; identification of the conserved metal binding motif was also shown to be useful in verifying structural models of the proteins. Metalloproteomics provides a rapid structural and functional annotation for these sequences and is shown to be approximately 95% accurate in predicting the presence or absence of stoichiometric metal content. The project's goal is to assay at least 1 member from each Pfam family; approximately 500 Pfam families have been characterized with respect to transition metal content so far.

Binding Sites↗

PubMed1999

A novel method for automatic functional annotation of proteins.

MOTIVATION: To cope with the increasing amount of sequence data, reliable automatic annotation tools are required. The TrEMBL database contains together with SWISS-PROT nearly all publicly available protein sequences, but in contrast to SWISS-PROT only limited functional annotation. To improve this situation, we had to develop a method of automatic annotation that produces highly reliable functional prediction using the language and the syntax of SWISS-PROT. RESULTS: An algorithm was developed and successfully used for the automatic annotation of a testset of unknown proteins. The predicted information included description, function, catalytic activity, cofactors, pathway, subcellular location, quaternary structure, similarity to other protein, active sites, and keywords. The algorithm showed a low coverage (10%), but a high specificity and reliability. AVAILABILITY: The results can be obtained by anonymous ftp from ftp.ebi.ac.uk/pub/databases/sp_tr_nrdb. The source code is available on request from the authors.

Algorithms↗

PubMed2003

Bioverse: Functional, structural and contextual annotation of proteins and proteomes.

Functional annotation is routinely performed for large-scale genomics projects and databases. Researchers working on more specific problems, for instance on an individual pathway or complex, also need to be able to quickly, completely and accurately annotate sequences. The Bioverse sequence annotation server (http://bioverse.compbio.washington.edu) provides a web-based interface to allow users to submit protein sequences to the Bioverse framework. Sequences are functionally and structurally annotated and potential contextual annotations are provided. Researchers can also submit candidate genomes for annotation of all proteins encoded by the genome (proteome).

Internet↗

PubMedDate not supplied

Enhanced functional annotation of protein sequences via the use of structural descriptors.

In order to circumvent limitations of sequence based methods in the process of making functional predictions for proteins, we have developed a methodology that uses a sequence-to-structure-to-function paradigm. First, an approximate three-dimensional structure is predicted. Then, a three-dimensional descriptor of the functional site, termed a Fuzzy Functional Form, or FFF, is used to screen the structure for the presence of the functional site of interest (Fetrow et al., 1998; Fetrow and Skolnick, 1998). Previously, a disulfide oxidoreductase FFF was developed and applied to predicted structures obtained from a small structural database. Here, using a substantially larger structural database, we expand the analysis of the disulfide oxidoreductase FFF to the B. subtilis genome. To ascertain the performance of the FFF, its results are compared to those obtained using both the sequence alignment method BLAST and three local sequence motif databases: PRINTS, Prosite, and Blocks. The FFF method is then compared in detail to Blocks and it is shown that the FFF is more flexible and sensitive in finding a specific function in a set of unknown proteins. In addition, the estimated false positive rate of function prediction is significantly lower using the FFF structural motif, rather than the standard sequence motif methods. We also present a second FFF and describe a specific example of the results of its whole-genome application to D. melanogaster using a newer threading algorithm. Our results from all of these studies indicate that the addition of three-dimensional structural information adds significant value in the prediction of biochemical function of genomic sequences.

Algorithms↗

PubMed2005

Learning statistical models for annotating proteins with function information using biomedical text.

BACKGROUND: The BioCreative text mining evaluation investigated the application of text mining methods to the task of automatically extracting information from text in biomedical research articles. We participated in Task 2 of the evaluation. For this task, we built a system to automatically annotate a given protein with codes from the Gene Ontology (GO) using the text of an article from the biomedical literature as evidence. METHODS: Our system relies on simple statistical analyses of the full text article provided. We learn n-gram models for each GO code using statistical methods and use these models to hypothesize annotations. We also learn a set of Naïve Bayes models that identify textual clues of possible connections between the given protein and a hypothesized annotation. These models are used to filter and rank the predictions of the n-gram models. RESULTS: We report experiments evaluating the utility of various components of our system on a set of data held out during development, and experiments evaluating the utility of external data sources that we used to learn our models. Finally, we report our evaluation results from the BioCreative organizers. CONCLUSION: We observe that, on the test data, our system performs quite well relative to the other systems submitted to the evaluation. From other experiments on the held-out data, we observe that (i) the Naïve Bayes models were effective in filtering and ranking the initially hypothesized annotations, and (ii) our learned models were significantly more accurate when external data sources were used during learning.

Bayes Theorem↗

PubMed2006

Systematic identification of functional orthologs based on protein network comparison.

Annotating protein function across species is an important task that is often complicated by the presence of large paralogous gene families. Here, we report a novel strategy for identifying functionally related proteins that supplements sequence-based comparisons with information on conserved protein-protein interactions. First, the protein interaction networks of two species are aligned by assigning proteins to sequence homology clusters using the Inparanoid algorithm. Next, probabilistic inference is performed on the aligned networks to identify pairs of proteins, one from each species, that are likely to retain the same function based on conservation of their interacting partners. Applying this method to Drosophila melanogaster and Saccharomyces cerevisiae, we analyze 121 cases for which functional orthology assignment is ambiguous when sequence similarity is used alone. In 61 of these cases, the network supports a different protein pair than that favored by sequence comparisons. These results suggest that network analysis can be used to provide a key source of information for refining sequence-based homology searches.

Animals↗

PubMed2004

Statistically rigorous automated protein annotation.

MOTIVATION: Assignment of putative protein functional annotation by comparative analysis using pre-defined experimental annotations is performed routinely by molecular biologists. The number and statistical significance of these assignments remains a challenge in this era of high-throughput proteomics. A combined statistical method that enables robust, automated protein annotation by reliably expanding existing annotation sets is described. An existing clustering scheme, based on relevant experimental information (e.g. sequence identity, keywords or gene expression data) is required. The method assigns new proteins to these clusters with a measure of reliability. It can also provide human reviewers with a reliability score for both new and previously classified proteins. RESULTS: A dataset of 27 000 annotated Protein Data Bank (PDB) polypeptide chains (of 36 000 chains currently in the PDB) was generated from 23 000 chains classified a priori. AVAILABILITY: PDB annotations and sample software implementation are freely accessible on the Web at http://pmr.sdsc.edu/go

Abstracting and Indexing↗

PubMed2006

Functional annotation of proteins identified in human brain during the HUPO Brain Proteome Project pilot study.

The HUPO Brain Proteome Project is an initiative coordinating proteomics studies to characterise human and mouse brain proteomes. Proteins identified in human brain samples during the project's pilot phase were put into biological context through integration with various annotation sources followed by a bioinformatics analysis. The data set was related to the genome sequence via the genes encoding identified proteins including an assessment of splice variant identification as well as an analysis of tissue specificity of the respective transcripts. Proteins were furthermore categorised according to subcellular localisation, molecular function and biological process, grouped into protein families and mapped to biological pathways they are known to act in. Involvement in pathological conditions was examined based on association with entries in the online version of Mendelian Inheritance in Man and an interaction network was derived from curated protein-proteininteraction data. Overall a non-redundant set of 1804 proteins was identified in human brain samples. In the majority of cases splice variants could be unambiguously identified by unique peptides, including matches to several hypothetical transcripts of known as well as predicted genes.

Alternative Splicing↗

PubMed2006

Detecting differential and correlated protein expression in label-free shotgun proteomics.

Recent studies have revealed a relationship between protein abundance and sampling statistics, such as sequence coverage, peptide count, and spectral count, in label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) shotgun proteomics. The use of sampling statistics offers a promising method of measuring relative protein abundance and detecting differentially expressed or coexpressed proteins. We performed a systematic analysis of various approaches to quantifying differential protein expression in eukaryotic Saccharomyces cerevisiae and prokaryotic Rhodopseudomonas palustris label-free LC-MS/MS data. First, we showed that, among three sampling statistics, the spectral count has the highest technical reproducibility, followed by the less-reproducible peptide count and relatively nonreproducible sequence coverage. Second, we used spectral count statistics to measure differential protein expression in pairwise experiments using five statistical tests: Fisher's exact test, G-test, AC test, t-test, and LPE test. Given the S. cerevisiae data set with spiked proteins as a benchmark and the false positive rate as a metric, our evaluation suggested that the Fisher's exact test, G-test, and AC test can be used when the number of replications is limited (one or two), whereas the t-test is useful with three or more replicates available. Third, we generalized the G-test to increase the sensitivity of detecting differential protein expression under multiple experimental conditions. Out of 1622 identified R. palustris proteins in the LC-MS/MS experiment, the generalized G-test detected 1119 differentially expressed proteins under six growth conditions. Finally, we studied correlated expression of these 1119 proteins by analyzing pairwise expression correlations and by delineating protein clusters according to expression patterns. Through pairwise expression correlation analysis, we demonstrated that proteins co-located in the same operon were much more strongly coexpressed than those from different operons. Combining cluster analysis with existing protein functional annotations, we identified six protein clusters with known biological significance. In summary, the proposed generalized G-test using spectral count sampling statistics is a viable methodology for robust quantification of relative protein abundance and for sensitive detection of biologically significant differential protein expression under multiple experimental conditions in label-free shotgun proteomics.

Bacterial Proteins↗

PubMed2004

3D-GENOMICS: a database to compare structural and functional annotations of proteins between sequenced genomes.

The 3D-GENOMICS database (http://www.sbg.bio. ic.ac.uk/3dgenomics/) provides structural annotations for proteins from sequenced genomes. In August 2003 the database included data for 93 proteomes. The annotations stored in the database include homologous sequences from various sequence databases, domains from SCOP and Pfam, patterns from Prosite and other predicted sequence features such as transmembrane regions and coiled coils. In addition to annotations at the sequence level, several precomputed cross- proteome comparative analyses are available based on SCOP domain superfamily composition. Annotations are available to the user via a web interface to the database. Multiple points of entry are available so that a user is able to: (i) directly access annotations for a single protein sequence via keywords or accession codes, (ii) examine a sequence of interest chosen from a summary of annotations for a particular proteome, or (iii) access precomputed frequency-based cross-proteome comparative analyses.

Amino Acid Sequence↗

PubMed2004

Automatic annotation of protein motif function with Gene Ontology terms.

BACKGROUND: Conserved protein sequence motifs are short stretches of amino acid sequence patterns that potentially encode the function of proteins. Several sequence pattern searching algorithms and programs exist foridentifying candidate protein motifs at the whole genome level. However, a much needed and important task is to determine the functions of the newly identified protein motifs. The Gene Ontology (GO) project is an endeavor to annotate the function of genes or protein sequences with terms from a dynamic, controlled vocabulary and these annotations serve well as a knowledge base. RESULTS: This paper presents methods to mine the GO knowledge base and use the association between the GO terms assigned to a sequence and the motifs matched by the same sequence as evidence for predicting the functions of novel protein motifs automatically. The task of assigning GO terms to protein motifs is viewed as both a binary classification and information retrieval problem, where PROSITE motifs are used as samples for mode training and functional prediction. The mutual information of a motif and aGO term association is found to be a very useful feature. We take advantage of the known motifs to train a logistic regression classifier, which allows us to combine mutual information with other frequency-based features and obtain a probability of correct association. The trained logistic regression model has intuitively meaningful and logically plausible parameter values, and performs very well empirically according to our evaluation criteria. CONCLUSIONS: In this research, different methods for automatic annotation of protein motifs have been investigated. Empirical result demonstrated that the methods have a great potential for detecting and augmenting information about the functions of newly discovered candidate protein motifs.

Amino Acid Motifs↗

PubMed2005

Evolutionary rate variation in eukaryotic lineage specific human intronless proteins.

The present study examines 783 human-mouse orthologous gene pairs for their pattern of sequence evolution, contrasting mammalia, eukaryota, coelomata, and bilateria specific human intronless genes. Such comparisons may be of use in understanding the general evolution of human genome. Evolutionary rate analyses indicate that mammalia specific human intronless genes are evolving faster as compared to other intronless genes specific to eukaryotic lineage, indicating towards their rapid evolution. The observations indicates that the genes conserved in eukaryota, coelomata, and bilateria, that is, proteins that arose earlier in evolution as compared to mammalia specific genes evolve slowly and are subjected to negative selection. The cause underlying rate variations was also explored. Although mutational bias might slightly fasten the nonsynonymous rates in mammalia specific genes, it is unlikely to be major cause of rate difference between the various categories. Furthermore, rate of divergence of mammalia specific intronless genes has been related to functional classification using the protein family annotation. Protein function was found in some cases to have larger impact on the rate of evolution of genes. Also, the codon usage pattern of mammalia specific intronless genes do not seem to differ much from those of other intronless genes conserved solely in eukaryotic lineage.

Animals↗

PubMed2003

Protein family classification and functional annotation.

With the accelerated accumulation of genomic sequence data, there is a pressing need to develop computational methods and advanced bioinformatics infrastructure for reliable and large-scale protein annotation and biological knowledge discovery. The Protein Information Resource (PIR) provides an integrated public resource of protein informatics to support genomic and proteomic research. PIR produces the Protein Sequence Database of functionally annotated protein sequences. The annotation problems are addressed by a classification-driven and rule-based method with evidence attribution, coupled with an integrated knowledge base system being developed. The approach allows sensitive identification, consistent and rich annotation, and systematic detection of annotation errors, as well as distinction of experimentally verified and computationally predicted features. The knowledge base consists of two new databases, sequence analysis tools, and graphical interfaces. PIR-NREF, a non-redundant reference database, provides a timely and comprehensive collection of all protein sequences, totaling more than 1,000,000 entries. iProClass, an integrated database of protein family, function, and structure information, provides extensive value-added features for about 830,000 proteins with rich links to over 50 molecular databases. This paper describes our approach to protein functional annotation with case studies and examines common identification errors. It also illustrates that data integration in PIR supports exploration of protein relationships and may reveal protein functional associations beyond sequence homology.

Amino Acid Motifs↗

PubMed2005

Functional genome annotation through phylogenomic mapping.

Accurate determination of functional interactions among proteins at the genome level remains a challenge for genomic research. Here we introduce a genome-scale approach to functional protein annotation--phylogenomic mapping--that requires only sequence data, can be applied equally well to both finished and unfinished genomes, and can be extended beyond single genomes to annotate multiple genomes simultaneously. We have developed and applied it to more than 200 sequenced bacterial genomes. Proteins with similar evolutionary histories were grouped together, placed on a three dimensional map and visualized as a topographical landscape. The resulting phylogenomic maps display thousands of proteins clustered in mountains on the basis of coinheritance, a strong indicator of shared function. In addition to systematic computational validation, we have experimentally confirmed the ability of phylogenomic maps to predict both mutant phenotype and gene function in the delta proteobacterium Myxococcus xanthus.

Bacterial Proteins↗

PubMed2004

Comprehensive de novo structure prediction in a systems-biology context for the archaea Halobacterium sp. NRC-1.

BACKGROUND: Large fractions of all fully sequenced genomes code for proteins of unknown function. Annotating these proteins of unknown function remains a critical bottleneck for systems biology and is crucial to understanding the biological relevance of genome-wide changes in mRNA and protein expression, protein-protein and protein-DNA interactions. The work reported here demonstrates that de novo structure prediction is now a viable option for providing general function information for many proteins of unknown function. RESULTS: We have used Rosetta de novo structure prediction to predict three-dimensional structures for 1,185 proteins and protein domains (<150 residues in length) found in Halobacterium NRC-1, a widely studied halophilic archaeon. Predicted structures were searched against the Protein Data Bank to identify fold similarities and extrapolate putative functions. They were analyzed in the context of a predicted association network composed of several sources of functional associations such as: predicted protein interactions, predicted operons, phylogenetic profile similarity and domain fusion. To illustrate this approach, we highlight three cases where our combined procedure has provided novel insights into our understanding of chemotaxis, possible prophage remnants in Halobacterium NRC-1 and archaeal transcriptional regulators. CONCLUSIONS: Simultaneous analysis of the association network, coordinated mRNA level changes in microarray experiments and genome-wide structure prediction has allowed us to glean significant biological insights into the roles of several Halobacterium NRC-1 proteins of previously unknown function, and significantly reduce the number of proteins encoded in the genome of this haloarchaeon for which no annotation is available.

Archaeal Proteins↗

PubMed2003

Comprehensive analysis of orthologous protein domains using the HOPS database.

One of the most reliable methods for protein function annotation is to transfer experimentally known functions from orthologous proteins in other organisms. Most methods for identifying orthologs operate on a subset of organisms with a completely sequenced genome, and treat proteins as single-domain units. However, it is well known that proteins are often made up of several independent domains, and there is a wealth of protein sequences from genomes that are not completely sequenced. A comprehensive set of protein domain families is found in the Pfam database. We wanted to apply orthology detection to Pfam families, but first some issues needed to be addressed. First, orthology detection becomes impractical and unreliable when too many species are included. Second, shorter domains contain less information. It is therefore important to assess the quality of the orthology assignment and avoid very short domains altogether. We present a database of orthologous protein domains in Pfam called HOPS: Hierarchical grouping of Orthologous and Paralogous Sequences. Orthology is inferred in a hierarchic system of phylogenetic subgroups using ortholog bootstrapping. To avoid the frequent errors stemming from horizontally transferred genes in bacteria, the analysis is presently limited to eukaryotic genes. The results are accessible in the graphical browser NIFAS, a Java tool originally developed for analyzing phylogenetic relations within Pfam families. The method was tested on a set of curated orthologs with experimentally verified function. In comparison to tree reconciliation with a complete species tree, our approach finds significantly more orthologs in the test set. Examples for investigating gene fusions and domain recombination using HOPS are given.

Animals↗

PubMed2005

FunShift: a database of function shift analysis on protein subfamilies.

Members of a protein family normally have a general biochemical function in common, but frequently one or more subgroups have evolved a slightly different function, such as different substrate specificity. It is important to detect such function shifts for a more accurate functional annotation. The FunShift database described here is a compilation of function shift analysis performed between subfamilies in protein families. It consists of two main components: (i) subfamilies derived from protein domain families and (ii) pairwise subfamily comparisons analyzed for function shift. The present release, FunShift 12, was derived from Pfam 12 and consists of 151,934 subfamilies derived from 7300 families. We carried out function shift analysis by two complementary methods on families with up to 500 members. From a total of 179,210 subfamily pairs, 62,384 were predicted to be functionally shifted in 2881 families. Each subfamily pair is provided with a markup of probable functional specificity-determining sites. Tools for searching and exploring the data are provided to make this database a valuable resource for protein function annotation. Knowledge of these functionally important sites will be useful for experimental biologists performing functional mutation studies. FunShift is available at http://FunShift.cgb.ki.se.

Databases, Protein↗

PubMed2006

Annotating proteins by mining protein interaction networks.

MOTIVATION: In general, most accurate gene/protein annotations are provided by curators. Despite having lesser evidence strengths, it is inevitable to use computational methods for fast and a priori discovery of protein function annotations. This paper considers the problem of assigning Gene Ontology (GO) annotations to partially annotated or newly discovered proteins. RESULTS: We present a data mining technique that computes the probabilistic relationships between GO annotations of proteins on protein-protein interaction data, and assigns highly correlated GO terms of annotated proteins to non-annotated proteins in the target set. In comparison with other techniques, probabilistic suffix tree and correlation mining techniques produce the highest prediction accuracy of 81% precision with the recall at 45%. AVAILABILITY: Code is available upon request. Results and used materials are available online at http://kirac.case.edu/PROTAN.

Amino Acid Sequence↗