Exploiting Amino Acid Composition for Predicting Protein-Protein Interactions

Computational prediction of protein interactions typically use protein domains as classifier features because they capture conserved information of interaction surfaces. However, approaches relying on domains as features cannot be applied to proteins without any domain information. In this paper, we explore the contribution of pure amino acid composition (AAC) for protein interaction prediction. This simple feature, which is based on normalized counts of single or pairs of amino acids, is applicable to proteins from any sequenced organism and can be used to compensate for the lack of domain information.AAC performed at par with protein interaction prediction based on domains on three yeast protein interaction datasets. Similar behavior was obtained using different classifiers, indicating that our results are a function of features and not of classifiers. In addition to yeast datasets, AAC performed comparably on worm and fly datasets. Prediction of interactions for the entire yeast proteome identified a large number of novel interactions, the majority of which co-localized or participated in the same processes. Our high confidence interaction network included both well-studied and uncharacterized proteins. Proteins with known function were involved in actin assembly and cell budding. Uncharacterized proteins interacted with proteins involved in reproduction and cell budding, thus providing putative biological roles for the uncharacterized proteins.AAC is a simple, yet powerful feature for predicting protein interactions, and can be used alone or in conjunction with protein domains to predict new and validate existing interactions. More importantly, AAC alone performs at par with existing, but more complex, features indicating the presence of sequence-level information that is predictive of interaction, but which is not necessarily restricted to domains

Unique features of Plasmids among different Citrobacter species

The _Citrobacter_ plasmids are supposed to represent the host genetic association within the living bacterial cell. The plasmids impart various beneficial characteristics to the host, helping it to retain suitable characteristics for adaptation as well as evolution. The study aims at understanding the role of prophage in influencing host functional characteristics by horizontal gene transfer or as whole plasmids. The _Citrobacter_ plasmid can be understood by analyzing many hypothetical protein sequences within its genome. Our study included 82 hypothetical proteins in 5 _Citrobacter_ plasmids genomes. The function predictions in 31 hypothetical proteins and 3-D structures were predicted for 11 protein sequences using PS2 server. The probable function prediction was done by using Bioinformatics web tools like CDD-BLAST, INTERPROSCAN, PFAM and COGs by searching sequence databases for the presence of orthologous enzymatic conserved domains in the hypothetical sequences. This study identified many uncharacterized proteins, whose roles are yet to be discovered in _Citrobacter_ plasmids. These results for unknown proteins within plasmids can be used in linking the genetic interactions of _Citrobacter_ species and their functions in different environmental conditions

Binding Ligand Prediction for Proteins Using Partial Matching of Local Surface Patches

Functional elucidation of uncharacterized protein structures is an important task in bioinformatics. We report our new approach for structure-based function prediction which captures local surface features of ligand binding pockets. Function of proteins, specifically, binding ligands of proteins, can be predicted by finding similar local surface regions of known proteins. To enable partial comparison of binding sites in proteins, a weighted bipartite matching algorithm is used to match pairs of surface patches. The surface patches are encoded with the 3D Zernike descriptors. Unlike the existing methods which compare global characteristics of the protein fold or the global pocket shape, the local surface patch method can find functional similarity between non-homologous proteins and binding pockets for flexible ligand molecules. The proposed method improves prediction results over global pocket shape-based method which was previously developed by our group

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

Bacterial protein meta-interactomes predict cross-species interactions and protein function

Background Protein-protein interactions (PPIs) can offer compelling evidence for protein function, especially when viewed in the context of proteome-wide interactomes. Bacteria have been popular subjects of interactome studies: more than six different bacterial species have been the subjects of comprehensive interactome studies while several more have had substantial segments of their proteomes screened for interactions. The protein interactomes of several bacterial species have been completed, including several from prominent human pathogens. The availability of interactome data has brought challenges, as these large data sets are difficult to compare across species, limiting their usefulness for broad studies of microbial genetics and evolution. Results In this study, we use more than 52,000 unique protein-protein interactions (PPIs) across 349 different bacterial species and strains to determine their conservation across data sets and taxonomic groups. When proteins are collapsed into orthologous groups (OGs) the resulting meta-interactome still includes more than 43,000 interactions, about 14,000 of which involve proteins of unknown function. While conserved interactions provide support for protein function in their respective species data, we found only 429 PPIs (~1% of the available data) conserved in two or more species, rendering any cross-species interactome comparison immediately useful. The meta-interactome serves as a model for predicting interactions, protein functions, and even full interactome sizes for species with limited to no experimentally observed PPI, including Bacillus subtilis and Salmonella enterica which are predicted to have up to 18,000 and 31,000 PPIs, respectively. Conclusions In the course of this work, we have assembled cross-species interactome comparisons that will allow interactomics researchers to anticipate the structures of yet-unexplored microbial interactomes and to focus on well-conserved yet uncharacterized interactors for further study. Such conserved interactions should provide evidence for important but yet-uncharacterized aspects of bacterial physiology and may provide targets for anti-microbial therapies

Escherichia coli EHEC Germany outbreak preliminary functional annotation using BG7 system

We have annotated the European outbreak E. coli EHEC genome sequenced by BGI (6-2-2011) and assembled with MIRA by Nick Loman (6-2-2011 ). Our system BG7, Bacterial Genome annotation of Era7 Bioinformatics, predicts ORFs and annotates them based on fragments of similarity with Uniprot proteins. We have predicted 6327 genes, 6156 encoding proteins y 171 corresponding to ribosomal and tRNA. Based on the preliminary results of our semi-automated method of annotation we have selected some predicted proteins with potential implications in pathogenicity and virulence.
There are 33 predicted genes annotated as toxins and we have found three putative hemolysins: Hemolysin E, a putative hemolysin expression modulating protein and a channel protein, hemolysin III family. We have found 31 predicted genes that could be related to specific antibiotic resistance: beta-lactamic, aminoglycoside, macrolide, polymyxin, tetracycline, fosfomycin and deoxycholate, novobiocin, chloramphenicol, bicyclomycin, norfloxacin and enoxacin and 6-mercaptopurine. This strain is rich in adhesion, secretion systems, pathogenicity and virulence related proteins. It seems to have a restriction-modification system, many proteins involved in Fe transport and utilization (siderophores as aerobactin and enterobactin), lysozyme, one inhibitor of pancreatic serine proteases, proteins involved in anaerobic respiration, antimicrobial peptides, and proteins involved in quorum sensing and biofilm formation that could confer competitive advantage to this strain

PubServer: literature searches by homology.

PubServer, available at http://pubserver.burnham.org/, is a tool to automatically collect, filter and analyze publications associated with groups of homologous proteins. Protein entries in databases such as Entrez Protein database at NCBI contain information about publications associated with a given protein. The scope of these publications varies a lot: they include studies focused on biochemical functions of individual proteins, but also reports from genome sequencing projects that introduce tens of thousands of proteins. Collecting and analyzing publications related to sets of homologous proteins help in functional annotation of novel protein families and in improving annotations of well-studied protein families or individual genes. However, performing such collection and analysis manually is a tedious and time-consuming process. PubServer automatically collects identifiers of homologous proteins using PSI-Blast, retrieves literature references from corresponding database entries and filters out publications unlikely to contain useful information about individual proteins. It also prepares simple vocabulary statistics from titles, abstracts and MeSH terms to identify the most frequently occurring keywords, which may help to quickly identify common themes in these publications. The filtering criteria applied to collected publications are user-adjustable. The results of the server are presented as an interactive page that allows re-filtering and different presentations of the output

Predicting protein functions by relaxation labelling protein interaction network

<p>Abstract</p> <p>Background</p> <p>One of key issues in the post-genomic era is to assign functions to uncharacterized proteins. Since proteins seldom act alone; rather, they must interact with other biomolecular units to execute their functions. Thus, the functions of unknown proteins may be discovered through studying their interactions with proteins having known functions. Although many approaches have been developed for this purpose, one of main limitations in most of these methods is that the dependence among functional terms has not been taken into account.</p> <p>Results</p> <p>We developed a new network-based protein function prediction method which combines the likelihood scores of local classifiers with a relaxation labelling technique. The framework can incorporate the inter-relationship among functional labels into the function prediction procedure and allow us to efficiently discover relevant non-local dependence. We evaluated the performance of the new method with one other representative network-based function prediction method using E. coli protein functional association networks.</p> <p>Conclusion</p> <p>Our results showed that the new method has better prediction performance than the previous method. The better predictive power of our method gives new insights about the importance of the dependence between functional terms in protein functional prediction.</p

Ltc1 is an ER-localized sterol transporter and a component of ER-mitochondria and ER-vacuole contacts.

Organelle contact sites perform fundamental functions in cells, including lipid and ion homeostasis, membrane dynamics, and signaling. Using a forward proteomics approach in yeast, we identified new ER-mitochondria and ER-vacuole contacts specified by an uncharacterized protein, Ylr072w. Ylr072w is a conserved protein with GRAM and VASt domains that selectively transports sterols and is thus termed Ltc1, for Lipid transfer at contact site 1. Ltc1 localized to ER-mitochondria and ER-vacuole contacts via the mitochondrial import receptors Tom70/71 and the vacuolar protein Vac8, respectively. At mitochondria, Ltc1 was required for cell viability in the absence of Mdm34, a subunit of the ER-mitochondria encounter structure. At vacuoles, Ltc1 was required for sterol-enriched membrane domain formation in response to stress. Increasing the proportion of Ltc1 at vacuoles was sufficient to induce sterol-enriched vacuolar domains without stress. Thus, our data support a model in which Ltc1 is a sterol-dependent regulator of organelle and cellular homeostasis via its dual localization to ER-mitochondria and ER-vacuole contact sites