Functional classification of protein domain superfamilies for protein function annotation

Proteins are made up of domains that are generally considered to be independent evolutionary and structural units having distinct functional properties. It is now well established that analysis of domains in proteins provides an effective approach to understand protein function using a `domain grammar'. Towards this end, evolutionarily-related protein domains have been classified into homologous superfamilies in CATH and SCOP databases. An ideal functional sub-classification of the domain superfamilies into `functional families' can not only help in function annotation of uncharacterised sequences but also provide a useful framework for understanding the diversity and evolution of function at the domain level. This work describes the development of a new protocol (FunFHMMer) for identifying functional families in CATH superfamilies that makes use of sequence patterns only and hence, is unaffected by the incompleteness of function annotations, annotation biases or misannotations existing in the databases. The resulting family classification was validated using known functional information and was found to generate more functionally coherent families than other domain-based protein resources. A protein function prediction pipeline was developed exploiting the functional annotations provided by the domain families which was validated by a database rollback benchmark set of proteins and an independent assessment by CAFA 2. The functional classification was found to capture the functional diversity of superfamilies well in terms of sequence, structure and the protein-context. This aided studies on evolution of protein domain function both at the superfamily level and in specific proteins of interest. The conserved positions in the functional family alignments were found to be enriched in catalytic site residues and ligand-binding site residues which led to the development of a functional site prediction tool. Lastly, the function prediction tools were assessed for annotation of moonlighting functions of proteins and a classification of moonlighting proteins was proposed based on their structure-function relationships

TarO : a target optimisation system for structural biology

This work was funded by the UK Biotechnology and Biological Sciences Research Council (BBSRC) Structural Proteomics of Rational Targets (SPoRT) initiative, (Grant BBS/B/14434). Funding to pay the Open Access publication charges for this article was provided by BBSRC.TarO (http://www.compbio.dundee.ac.uk/taro) offers a single point of reference for key bioinformatics analyses relevant to selecting proteins or domains for study by structural biology techniques. The protein sequence is analysed by 17 algorithms and compared to 8 databases. TarO gathers putative homologues, including orthologues, and then obtains predictions of properties for these sequences including crystallisation propensity, protein disorder and post-translational modifications. Analyses are run on a high-performance computing cluster, the results integrated, stored in a database and accessed through a web-based user interface. Output is in tabulated format and in the form of an annotated multiple sequence alignment (MSA) that may be edited interactively in the program Jalview. TarO also simplifies the gathering of additional annotations via the Distributed Annotation System, both from the MSA in Jalview and through links to Dasty2. Routes to other information gateways are included, for example to relevant pages from UniProt, COG and the Conserved Domains Database. Open access to TarO is available from a guest account with private accounts for academic use available on request. Future development of TarO will include further analysis steps and integration with the Protein Information Management System (PIMS), a sister project in the BBSRC Structural Proteomics of Rational Targets initiative.Publisher PDFPeer reviewe

Domain-mediated interactions for protein subfamily identification

Within a protein family, proteins with the same domain often exhibit different cellular functions, despite the shared evolutionary history and molecular function of the domain. We hypothesized that domain-mediated interactions (DMIs) may categorize a protein family into subfamilies because the diversified functions of a single domain often depend on interacting partners of domains. Here we systematically identified DMI subfamilies, in which proteins share domains with DMI partners, as well as with various functional and physical interaction networks in individual species. In humans, DMI subfamily members are associated with similar diseases, including cancers, and are frequently co-associated with the same diseases. DMI information relates to the functional and evolutionary subdivisions of human kinases. In yeast, DMI subfamilies contain proteins with similar phenotypic outcomes from specific chemical treatments. Therefore, the systematic investigation here provides insights into the diverse functions of subfamilies derived from a protein family with a link-centric approach and suggests a useful resource for annotating the functions and phenotypic outcomes of proteins.11Ysciescopu

Bacterial riboproteogenomics : the era of N-terminal proteoform existence revealed

With the rapid increase in the number of sequenced prokaryotic genomes, relying on automated gene annotation became a necessity. Multiple lines of evidence, however, suggest that current bacterial genome annotations may contain inconsistencies and are incomplete, even for so-called well-annotated genomes. We here discuss underexplored sources of protein diversity and new methodologies for high-throughput genome re-annotation. The expression of multiple molecular forms of proteins (proteoforms) from a single gene, particularly driven by alternative translation initiation, is gaining interest as a prominent contributor to bacterial protein diversity. In consequence, riboproteogenomic pipelines were proposed to comprehensively capture proteoform expression in prokaryotes by the complementary use of (positional) proteomics and the direct readout of translated genomic regions using ribosome profiling. To complement these discoveries, tailored strategies are required for the functional characterization of newly discovered bacterial proteoforms

Biases in the Experimental Annotations of Protein Function and their Effect on Our Understanding of Protein Function Space

The ongoing functional annotation of proteins relies upon the work of curators to capture experimental findings from scientific literature and apply them to protein sequence and structure data. However, with the increasing use of high-throughput experimental assays, a small number of experimental studies dominate the functional protein annotations collected in databases. Here we investigate just how prevalent is the "few articles -- many proteins" phenomenon. We examine the experimentally validated annotation of proteins provided by several groups in the GO Consortium, and show that the distribution of proteins per published study is exponential, with 0.14% of articles providing the source of annotations for 25% of the proteins in the UniProt-GOA compilation. Since each of the dominant articles describes the use of an assay that can find only one function or a small group of functions, this leads to substantial biases in what we know about the function of many proteins. Mass-spectrometry, microscopy and RNAi experiments dominate high throughput experiments. Consequently, the functional information derived from these experiments is mostly of the subcellular location of proteins, and of the participation of proteins in embryonic developmental pathways. For some organisms, the information provided by different studies overlap by a large amount. We also show that the information provided by high throughput experiments is less specific than those provided by low throughput experiments. Given the experimental techniques available, certain biases in protein function annotation due to high-throughput experiments are unavoidable. Knowing that these biases exist and understanding their characteristics and extent is important for database curators, developers of function annotation programs, and anyone who uses protein function annotation data to plan experiments.Comment: Accepted to PLoS Computational Biology. Press embargo applies. v4: text corrected for style and supplementary material inserte

Global Functional Atlas of \u3cem\u3eEscherichia coli\u3c/em\u3e Encompassing Previously Uncharacterized Proteins

One-third of the 4,225 protein-coding genes of Escherichia coli K-12 remain functionally unannotated (orphans). Many map to distant clades such as Archaea, suggesting involvement in basic prokaryotic traits, whereas others appear restricted to E. coli, including pathogenic strains. To elucidate the orphans’ biological roles, we performed an extensive proteomic survey using affinity-tagged E. coli strains and generated comprehensive genomic context inferences to derive a high-confidence compendium for virtually the entire proteome consisting of 5,993 putative physical interactions and 74,776 putative functional associations, most of which are novel. Clustering of the respective probabilistic networks revealed putative orphan membership in discrete multiprotein complexes and functional modules together with annotated gene products, whereas a machine-learning strategy based on network integration implicated the orphans in specific biological processes. We provide additional experimental evidence supporting orphan participation in protein synthesis, amino acid metabolism, biofilm formation, motility, and assembly of the bacterial cell envelope. This resource provides a “systems-wide” functional blueprint of a model microbe, with insights into the biological and evolutionary significance of previously uncharacterized proteins

A catalog of stability-associated sequence elements in 3' UTRs of yeast mRNAs

BACKGROUND: In recent years, intensive computational efforts have been directed towards the discovery of promoter motifs that correlate with mRNA expression profiles. Nevertheless, it is still not always possible to predict steady-state mRNA expression levels based on promoter signals alone, suggesting that other factors may be involved. Other genic regions, in particular 3' UTRs, which are known to exert regulatory effects especially through controlling RNA stability and localization, were less comprehensively investigated, and deciphering regulatory motifs within them is thus crucial. RESULTS: By analyzing 3' UTR sequences and mRNA decay profiles of Saccharomyces cerevisiae genes, we derived a catalog of 53 sequence motifs that may be implicated in stabilization or destabilization of mRNAs. Some of the motifs correspond to known RNA-binding protein sites, and one of them may act in destabilization of ribosome biogenesis genes during stress response. In addition, we present for the first time a catalog of 23 motifs associated with subcellular localization. A significant proportion of the 3' UTR motifs is highly conserved in orthologous yeast genes, and some of the motifs are strikingly similar to recently published mammalian 3' UTR motifs. We classified all genes into those regulated only at transcription initiation level, only at degradation level, and those regulated by a combination of both. Interestingly, different biological functionalities and expression patterns correspond to such classification. CONCLUSION: The present motif catalogs are a first step towards the understanding of the regulation of mRNA degradation and subcellular localization, two important processes which - together with transcription regulation - determine the cell transcriptome

Large-scale and significant expression from pseudogenes in Sodalis glossinidius – a facultative bacterial endosymbiont

The majority of bacterial genomes have high coding efficiencies, but there are some genomes of intracellular bacteria that have low gene density. The genome of the endosymbiont Sodalis glossinidius contains almost 50 % pseudogenes containing mutations that putatively silence them at the genomic level. We have applied multiple ‘omic’ strategies, combining Illumina and Pacific Biosciences Single-Molecule Real-Time DNA sequencing and annotation, stranded RNA sequencing and proteome analysis to better understand the transcriptional and translational landscape of Sodalis pseudogenes, and potential mechanisms for their control. Between 53 and 74 % of the Sodalis transcriptome remains active in cell-free culture. The mean sense transcription from coding domain sequences (CDSs) is four times greater than that from pseudogenes. Comparative genomic analysis of six Illumina-sequenced Sodalis isolates from different host Glossina species shows pseudogenes make up ~40 % of the 2729 genes in the core genome, suggesting that they are stable and/or that Sodalis is a recent introduction across the genus Glossina as a facultative symbiont. These data shed further light on the importance of transcriptional and translational control in deciphering host–microbe interactions. The combination of genomics, transcriptomics and proteomics gives a multidimensional perspective for studying prokaryotic genomes with a view to elucidating evolutionary adaptation to novel environmental niches