BACPHLIP: predicting bacteriophage lifestyle from conserved protein domains.
Ontology highlight
ABSTRACT:
SUBMITTER: Hockenberry AJ
PROVIDER: S-EPMC8106911 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Protein domains are subunits of proteins that recur throughout the protein world. There are many definitions attempting to capture the essence of a protein domain, and several systems that identify protein domains and classify them into families. EVEREST, recently described in Portugaly et al. (2006) BMC Bioinformatics, 7, 277, is one such system that performs the task automatically, using protein sequence alone. Herein we describe EVEREST release 2.0, consisting of 20,029 families, each defined by one or more HMMs. The current EVEREST database was constructed by scanning UniProt 8.1 and all PDB sequences (total over 3,000,000 sequences) with each of the EVEREST families. EVEREST annotates 64% of all sequences, and covers 59% of all residues. EVEREST is available at http://www.everest.cs.huji.ac.il/. The website provides annotations given by SCOP, CATH, Pfam A and EVEREST. It allows for browsing through the families of each of those sources, graphically visualizing the domain organization of the proteins in the family. The website also provides access to analyzes of relationships between domain families, within and across domain definition systems. Users can upload sequences for analysis by the set of EVEREST families. Finally an advanced search form allows querying for families matching criteria regarding novelty, phylogenetic composition and more.
| S-EPMC1669739 | biostudies-literature
Project description:BackgroundProfile HMMs (hidden Markov models) provide effective methods for modeling the conserved regions of protein families. A limitation of the resulting domain models is the difficulty to pinpoint their much shorter functional sub-features, such as catalytically relevant sequence motifs in enzymes or ligand binding signatures of receptor proteins.ResultsTo identify these conserved motifs efficiently, we propose a method for extracting the most information-rich regions in protein families from their profile HMMs. The method was used here to predict a comprehensive set of sub-HMMs from the Pfam domain database. Cross-validations with the PROSITE and CSA databases confirmed the efficiency of the method in predicting most of the known functionally relevant motifs and residues. At the same time, 46,768 novel conserved regions could be predicted. The data set also allowed us to link at least 461 Pfam domains of known and unknown function by their common sub-HMMs. Finally, the sub-HMM method showed very promising results as an alternative search method for identifying proteins that share only short sequence similarities.ConclusionsSub-HMMs extend the application spectrum of profile HMMs to motif discovery. Their most interesting utility is the identification of the functionally relevant residues in proteins of known and unknown function. Additionally, sub-HMMs can be used for highly localized sequence similarity searches that focus on shorter conserved features rather than entire domains or global similarities. The motif data generated by this study is a valuable knowledge resource for characterizing protein functions in the future.
| S-EPMC2879284 | biostudies-literature
Project description:RNA polymerase II establishes many protein-protein interactions with transcriptional regulators to coordinate gene expression, but little is known about protein domains involved in the contact with them. We use a new approach to look for conserved regions of the RNA pol II of S. cerevisiae located at the surface of the structure of the complex, hypothesizing that they might be involved in the interaction with transcriptional regulators. We defined five different conserved domains and demonstrate that all of them make contact with transcriptional regulators.
| S-EPMC3173688 | biostudies-literature
Project description:Despite significant efforts from the research community, an extensive portion of the proteins encoded by human genes lack an assigned cellular function. Most metazoan proteins are composed of structural and/or functional domains, of which many appear in multiple proteins. Once a domain is characterized in one protein, the presence of a similar sequence in an uncharacterized protein serves as a basis for inference of function. Thus knowledge of a domain's function, or the protein within which it arises, can facilitate the analysis of an entire set of proteins.From the Pfam domain database, we extracted uncharacterized protein domains represented in proteins from humans, worms, and flies. A data centre was created to facilitate the analysis of the uncharacterized domain-containing proteins. The centre both provides researchers with links to dispersed internet resources containing gene-specific experimental data and enables them to post relevant experimental results or comments. For each human gene in the system, a characterization score is posted, allowing users to track the progress of characterization over time or to identify for study uncharacterized domains in well-characterized genes. As a test of the system, a subset of 39 domains was selected for analysis and the experimental results posted to the NovelFam3000 system. For 25 human protein members of these 39 domain families, detailed sub-cellular localizations were determined. Specific observations are presented based on the analysis of the integrated information provided through the online NovelFam3000 system.Consistent experimental results between multiple members of a domain family allow for inferences of the domain's functional role. We unite bioinformatics resources and experimental data in order to accelerate the functional characterization of scarcely annotated domain families.
| S-EPMC1440326 | biostudies-literature
Project description:Bacteriophage T4 initially recognizes its host cells using its long tail fibers. Long tail fibers consist of a phage-proximal and a phage-distal rod, each around 80 nm long and attached to each other at a slight angle. The phage-proximal rod is formed by a homo-trimer of gene product 34 (gp34) and is attached to the phage-distal rod by a monomer of gp35. The phage-distal rod consists of two protein trimers: a trimer of gp36, attached to gp35, although most of the phage-distal rod, including the receptor-binding domain, is formed by a trimer of gp37. In this review, we discuss what is known about the detailed structure and function of the different long tail fiber domains. Partial crystal structures of gp34 and gp37 have revealed the presence of new protein folds, some of which are present in several repeats, while others are apparently unique. Gp38, a phage chaperone protein necessary for folding of gp37, is thought to act on an α-helical coiled-coil region in gp37. Future studies should reveal the remaining structure of the long tail fibers, how they assemble into a functional unit, and how the long tail fibers trigger the infection process after successful recognition of a suitable host bacterium.
| S-EPMC5899708 | biostudies-literature
Project description:The structures of homologous proteins are generally better conserved than their sequences. This phenomenon is demonstrated by the prevalence of structurally conserved regions (SCRs) even in highly divergent protein families. Defining SCRs requires the comparison of two or more homologous structures and is affected by their availability and divergence, and our ability to deduce structurally equivalent positions among them. In the absence of multiple homologous structures, it is necessary to predict SCRs of a protein using information from only a set of homologous sequences and (if available) a single structure. Accurate SCR predictions can benefit homology modelling and sequence alignment.Using pairwise DaliLite alignments among a set of homologous structures, we devised a simple measure of structural conservation, termed structural conservation index (SCI). SCI was used to distinguish SCRs from non-SCRs. A database of SCRs was compiled from 386 SCOP superfamilies containing 6489 protein domains. Artificial neural networks were then trained to predict SCRs with various features deduced from a single structure and homologous sequences. Assessment of the predictions via a 5-fold cross-validation method revealed that predictions based on features derived from a single structure perform similarly to ones based on homologous sequences, while combining sequence and structural features was optimal in terms of accuracy (0.755) and Matthews correlation coefficient (0.476). These results suggest that even without information from multiple structures, it is still possible to effectively predict SCRs for a protein. Finally, inspection of the structures with the worst predictions pinpoints difficulties in SCR definitions.The SCR database and the prediction server can be found at http://prodata.swmed.edu/SCR.91huangi@gmail.com or grishin@chop.swmed.eduSupplementary data are available at Bioinformatics Online.
| S-EPMC3546793 | biostudies-literature
Project description:A heuristic algorithm for associating Gene Ontology (GO) defined molecular functions to protein domains as listed in the ProDom and CDD databases is described. The algorithm generates rules for function-domain associations based on the intersection of functions assigned to gene products by the GO consortium that contain ProDom and/or CDD domains at varying levels of sequence similarity. The hierarchical nature of GO molecular functions is incorporated into rule generation. Manual review of a subset of the rules generated indicates an accuracy rate of 87% for ProDom rules and 84% for CDD rules. The utility of these associations is that novel sequences can be assigned a putative function if sufficient similarity exists to a ProDom or CDD domain for which one or more GO functions has been associated. Although functional assignments are increasingly being made for gene products from model organisms, it is likely that the needs of investigators will continue to outpace the efforts of curators, particularly for nonmodel organisms. A comparison with other methods in terms of coverage and agreement was performed, indicating the utility of the approach. The domain-function associations and function assignments are available from our website http://www.cbil.upenn.edu/GO.
| S-EPMC187511 | biostudies-literature
Project description:Bacteriophages play key roles in microbial evolution1,2, marine nutrient cycling3 and human disease4. Phages are genetically diverse, and their genome architectures are characteristically mosaic, driven by horizontal gene transfer with other phages and host genomes5. As a consequence, phage evolution is complex and their genomes are composed of genes with distinct and varied evolutionary histories6,7. However, there are conflicting perspectives on the roles of mosaicism and the extent to which it generates a spectrum of genome diversity8 or genetically discrete populations9,10. Here, we show that bacteriophages evolve within two general evolutionary modes that differ in the extent of horizontal gene transfer by an order of magnitude. Temperate phages distribute into high and low gene flux modes, whereas lytic phages share only the lower gene flux mode. The evolutionary modes are also a function of the bacterial host and different proportions of temperate and lytic phages are distributed in either mode depending on the host phylum. Groups of genetically related phages fall into either the high or low gene flux modes, suggesting there are genetic as well as ecological drivers of horizontal gene transfer rates. Consequently, genome mosaicism varies depending on the host, lifestyle and genetic constitution of phages.
| S-EPMC5540316 | biostudies-literature
Project description:Signaling proteins comprised of modular domains have evolved along with multicellularity as a method to facilitate increasing intracellular bandwidth. The effects of intramolecular interactions between modular domains within the context of native proteins have been largely unexplored. Here we examine intra- and intermolecular interactions in the multidomain signaling protein, protein kinase Cα (PKCα). We identify three interactions between two activated PKC molecules that synergistically stabilize a nanomolar affinity homodimer. Disruption of the homodimer results in a loss of PKC-mediated ERK1/2 phosphorylation, whereas disruption of the auto-inhibited state promotes the homodimer and prolongs PKC membrane localization. These observations support a novel regulatory mechanism wherein homodimerization dictates the equilibrium between the auto-inhibited and active states of PKC by sequestering auto-inhibitory interactions. Our findings underscore the physiological importance of context-dependent modular domain interactions in cell signaling.
| S-EPMC4067214 | biostudies-literature
Project description:There are more than 500 amino acid substitutions in each human genome, and bioinformatics tools irreplaceably contribute to determination of their functional effects. We have developed feature-based algorithm for the detection of mutations outside conserved functional domains (CFDs) and compared its classification efficacy with the most commonly used phylogeny-based tools, PolyPhen-2 and SIFT. The new algorithm is based on the informational spectrum method (ISM), a feature-based technique, and statistical analysis. Our dataset contained neutral polymorphisms and mutations associated with myeloid malignancies from epigenetic regulators ASXL1, DNMT3A, EZH2, and TET2. PolyPhen-2 and SIFT had significantly lower accuracies in predicting the effects of amino acid substitutions outside CFDs than expected, with especially low sensitivity. On the other hand, only ISM algorithm showed statistically significant classification of these sequences. It outperformed PolyPhen-2 and SIFT by 15% and 13%, respectively. These results suggest that feature-based methods, like ISM, are more suitable for the classification of amino acid substitutions outside CFDs than phylogeny-based tools.
| S-EPMC3855963 | biostudies-literature