STRING: a database of predicted functional associations between proteins

Functional links between proteins can often be inferred from genomic associations between the genes that encode them: groups of genes that are required for the same function tend to show similar species coverage, are often located in close proximity on the genome (in prokaryotes), and tend to be involved in gene-fusion events. The database STRING is a precomputed global resource for the exploration and analysis of these associations. Since the three types of evidence differ conceptually, and the number of predicted interactions is very large, it is essential to be able to assess and compare the significance of individual predictions. Thus, STRING contains a unique scoring-framework based on benchmarks of the different types of associations against a common reference set, integrated in a single confidence score per prediction. The graphical representation of the network of inferred, weighted protein interactions provides a high-level view of functional linkage, facilitating the analysis of modularity in biological processes. STRING is updated continuously, and currently contains 261 033 orthologs in 89 fully sequenced genomes. The database predicts functional interactions at an expected level of accuracy of at least 80% for more than half of the genes; it is online at http://www.bork.embl-heidelberg.de/STRING/.     

Pi-pi interactions: the geometry and energetics of phenylalanine-phenylalanine interactions in proteins

The geometries of aromatic-aromatic interactions between phenylalanine residues in proteins are analysed in detail and correlated with energy calculations. A new definition of the interplanar angle is important for distinguishing favourable edge-to-face and unfavourable face-to-face orientations. The experimental observations are scattered over a wide range of conformational space, with no strongly preferred single orientation. However, Phe-Phe interactions occur almost exclusively in electrostatically attractive geometries: electrostatically unfavourable regions are only sparsely populated. Electrostatics dominate the geometry of interaction, while van der Waals' interactions are less significant, probably due to the hydrophobic environment of the protein core. The observations on proteins support the Hunter-Sanders rules for pi-pi interactions. In particular, offset stacked geometries, which theory predicts to be favourable, are observed experimentally. For monocyclic aromatics, use of a C-H dipole, the approach used in molecular mechanics calculations, accounts well for these aromatic-aromatic interactions. Comparison with the results obtained from the small molecules database indicates that the protein and small molecule crystal environments are very different.     

A novel biclustering approach to association rule mining for predicting HIV-1-human protein interactions

Identification of potential viral-host protein interactions is a vital and useful approach towards development of new drugs targeting those interactions. In recent days, computational tools are being utilized for predicting viral-host interactions. Recently a database containing records of experimentally validated interactions between a set of HIV-1 proteins and a set of human proteins has been published. The problem of predicting new interactions based on this database is usually posed as a classification problem. However, posing the problem as a classification one suffers from the lack of biologically validated negative interactions. Therefore it will be beneficial to use the existing database for predicting new viral-host interactions without the need of negative samples. Motivated by this, in this article, the HIV-1-human protein interaction database has been analyzed using association rule mining. The main objective is to identify a set of association rules both among the HIV-1 proteins and among the human proteins, and use these rules for predicting new interactions. In this regard, a novel association rule mining technique based on biclustering has been proposed for discovering frequent closed itemsets followed by the association rules from the adjacency matrix of the HIV-1-human interaction network. Novel HIV-1-human interactions have been predicted based on the discovered association rules and tested for biological significance. For validation of the predicted new interactions, gene ontology-based and pathway-based studies have been performed. These studies show that the human proteins which are predicted to interact with a particular viral protein share many common biological activities. Moreover, literature survey has been used for validation purpose to identify some predicted interactions that are already validated experimentally but not present in the database. Comparison with other prediction methods is also discussed.     

A Single Kernel-Based Approach to Extract Drug-Drug Interactions from Biomedical Literature

When one drug influences the level or activity of another drug this is known as a drug-drug interaction (DDI). Knowledge of such interactions is crucial for patient safety. However, the volume and content of published biomedical literature on drug interactions is expanding rapidly, making it increasingly difficult for DDIs database curators to detect and collate DDIs information manually. In this paper, we propose a single kernel-based approach to extract DDIs from biomedical literature. This novel kernel-based approach can effectively make full use of syntactic structural information of the dependency graph. In particular, our approach can efficiently represent both single subgraph topological information and the relation of two subgraphs in the dependency graph. Experimental evaluations showed that our single kernel-based approach can achieve state-of-the-art performance on the publicly available DDI corpus without exploiting multiple kernels or additional domain resources.     

ICBS: a database of interactions between protein chains mediated by beta-sheet formation

MOTIVATION: Interchain beta-sheet (ICBS) interactions occur widely in protein quaternary structures, interactions between proteins and protein aggregation. These interactions play a central role in many biological processes and in diseases ranging from AIDS and cancer to anthrax and Alzheimer's. RESULTS: We have created a comprehensive database of ICBS interactions that is updated on a weekly basis and allows entries to be sorted and searched by relevance and other criteria through a simple Web interface. We derive a simple ICBS index to quantify the relative contributions of the beta-ladders in the overall interchain interaction and compute first- and second-order statistics regarding amino acid composition and pairing at different relative positions in the beta-strands. Analysis of the database reveals a 15.8% prevalence of significant ICBS interactions, the majority of which involve the formation of antiparallel beta-sheets and many of which involve the formation of dimers and oligomers. The frequencies of amino acids in ICBS interfaces are similar to those in intrachain beta-sheet interfaces. A full range of non-covalent interactions between side chains complement the hydrogen-bonding interactions between the main chains. Polar amino acids pair preferentially with polar amino acids and non-polar amino acids pair preferentially with non-polar amino acids among antiparallel (i, j) pairs. We anticipate that the statistics and insights gained from the database will guide the development of agents that control interchain beta-sheet interactions and that the database will help identify new protein interactions and targets for these agents. AVAILABILITY: The database is available at: http://www.igb.uci.edu/servers/icbs/     

Identification of essential proteins based on edge clustering coefficient

Identification of essential proteins is key to understanding the minimal requirements for cellular life and important for drug design. The rapid increase of available protein-protein interaction (PPI) data has made it possible to detect protein essentiality on network level. A series of centrality measures have been proposed to discover essential proteins based on network topology. However, most of them tended to focus only on the location of single protein, but ignored the relevance between interactions and protein essentiality. In this paper, a new centrality measure for identifying essential proteins based on edge clustering coefficient, named as NC, is proposed. Different from previous centrality measures, NC considers both the centrality of a node and the relationship between it and its neighbors. For each interaction in the network, we calculate its edge clustering coefficient. A node’s essentiality is determined by the sum of the edge clustering coefficients of interactions connecting it and its neighbors. The new centrality measure NC takes into account the modular nature of protein essentiality. NC is applied to three different types of yeast protein-protein interaction networks, which are obtained from the DIP database, the MIPS database and the BioGRID database, respectively. The experimental results on the three different networks show that the number of essential proteins discovered by NC universally exceeds that discovered by the six other centrality measures: DC, BC, CC, SC, EC, and IC. Moreover, the essential proteins discovered by NC show significant cluster effect.     

The "random-coil" state of proteins: comparison of database statistics and molecular simulations.

This study presents a comparison of two models of the random-coil state, one based on statistical distributions from the structural database and the other based on molecular dynamics simulations. The database model relies on the assumption that the random- or statistical-coil state of a particular residue can be described by its conformational distribution in a sufficiently diverse subset of protein structures. The molecular dynamics model is based on distributions from molecular simulations carried out on "dipeptide" models (single residues with N-terminal acetyl and C-terminal N'-methyl amide blocking groups). A comparison of the two models for the residues Ala, Asn, Asp, Gly, and Val indicates that the database distributions are greatly influenced by long-range interactions and dominated by specific recognizable elements of protein structure. In contrast, the limited structural scope of the dipeptide models presents the extreme case of a peptide under the influence of only short-range interactions. The models were evaluated by a comparison of scalar coupling constants calculated from the conformational distributions and compared with experimentally values determined for unstructured peptides. Although the models gave different distributions, there was similar agreement with experiment. This comparison emphasizes the differences and limitations in each model and highlights the difficulty in presenting an accurate picture of the random-coil state. Proteins 1999;36:407- 418.     

PaVESy: Pathway Visualization and Editing System

A data managing system for editing and visualization of biological pathways is presented. The main component of PaVESy (Pathway Visualization and Editing System) is a relational SQL database system. The database design allows storage of biological objects, such as metabolites, proteins, genes and respective relations, which are required to assemble metabolic and regulatory biological interactions. The database model accommodates highly flexible annotation of biological objects by user-defined attributes. In addition, specific roles of objects are derived from these attributes in the context of user-defined interactions, e.g. in the course of pathway generation or during editing of the database content. Furthermore, the user may organize and arrange the database content within a folder structure and is free to group and annotate database objects of interest within customizable subsets. Thus, we allow an individualized view on the database content and facilitate user customization. A JAVA-based class library was developed, which serves as the database programming interface to PaVESy. This API provides classes, which implement the concepts of object persistence in SQL databases, such as entries, interactions, annotations, folders and subsets. We created editing and visualization tools for navigation in and visualization of the database content. User approved pathway assemblies are stored and may be retrieved for continued modification, annotation and export. Data export is interfaced with a range of network visualization programs, such as Pajek or other software allowing import of SBML or GML data format. AVAILABILITY: http://pavsey.mpimp-golm.mpg.de     

DIP: the database of interacting proteins

The Database of Interacting Proteins (DIP; http://dip.doe-mbi.ucla.edu) is a database that documents experimentally determined protein-protein interactions. This database is intended to provide the scientific community with a comprehensive and integrated tool for browsing and efficiently extracting information about protein interactions and interaction networks in biological processes. Beyond cataloging details of protein-protein interactions, the DIP is useful for understanding protein function and protein-protein relationships, studying the properties of networks of interacting proteins, benchmarking predictions of protein-protein interactions, and studying the evolution of protein-protein interactions.     

DIPOS: database of interacting proteins in Oryza sativa

Rice is an important crop throughout the world and is the staple food for about half the world's population. For better breeding and improved production, we need to know the function of rice molecules which facilitate their function through interactions with each other. The database of interacting proteins in Oryza sativa (DIPOS) provides comprehensive information of interacting proteins in rice, where the interactions are predicted using two computational methods, i.e., interologs and domain based methods. DIPOS contains 14?614?067 pairwise interactions among 27?746 proteins, covering about 41% of the whole Oryaza sativa proteome. Furthermore, each interaction is assigned a confidence score which further enables biologists to sort out the important proteins. Biological explanations of pathways and interactions are also provided based on the database. Public access to the DIPOS is available at and .     

Reconstruction of the yeast protein-protein interaction network involved in nutrient sensing and global metabolic regulation

Background  

Several protein-protein interaction studies have been performed for the yeast Saccharomyces cerevisiae using different high-throughput experimental techniques. All these results are collected in the BioGRID database and the SGD database provide detailed annotation of the different proteins. Despite the value of BioGRID for studying protein-protein interactions, there is a need for manual curation of these interactions in order to remove false positives.     

NCIR: a database of non-canonical interactions in known RNA structures

The secondary and tertiary structure of an RNA molecule typically includes a number of non-canonical base–base interactions. The known occurrences of these interactions are tabulated in the NCIR database, which can be accessed from http://prion.bchs.uh.edu/bp_type/. The number of examples is now over 1400, which is an increase of >700% since the database was first published. This dramatic increase reflects the addition of data from the recently published crystal structures of the 50S (2.4 Å) and 30S (3.0 Å) ribosomal subunits. In addition, non-canonical interactions observed in published crystal and NMR structures of tRNAs, group I introns, ribozymes, RNA aptamers and synthetic oligonucleotides are included. Properties associated with these interactions, such as sequence context, sugar pucker conformation, glycosidic angle conformation, melting temperature, chemical shift and free energy, are also reported when available. Out of the 29 anticipated pairs with at least two hydrogen bonds, 28 have been observed to date. In addition, several novel examples, not generally predicted, have also been encountered, bringing the total of such pairs to 36. Added to this list are a variety of single, bifurcated, triple and quadruple interactions. The most common non-canonical pairs are the sheared GA, GA imino, AU reverse Hoogsteen, and the GU and AC wobble pairs. The most frequent triple interaction connects N3 of an A with the amino of a G that is also involved in a standard Watson–Crick pair.     

Geometrical and electro-static determinants of protein-protein interactions

Protein-protein interactions (PPI) are pivotal to the numerous processes in the cell. Therefore, it is of interest to document the analysis of these interactions in terms of binding sites, topology of the interacting structures and physiochemical properties of interacting interfaces and the of forces interactions. The interaction interface of obligatory protein-protein complexes differs from that of the transient interactions. We have created a large database of protein-protein interactions containing over100 thousand interfaces. The structural redundancy was eliminated to obtain a non-redundant database of over 2,265 interaction interfaces. Therefore, it is of interest to document the analysis of these interactions in terms of binding sites, topology of the interacting structures and physiochemical properties of interacting interfaces and the offorces interactions. The residue interaction propensity and all of the rest of the parametric scores converged to a statistical indistinguishable common sub-range and followed the similar distribution trends for all three classes of sequence-based classifications PPInS. This indicates that the principles of molecular recognition are dependent on the preciseness of the fit in the interaction interfaces. Thus, it reinforces the importance of geometrical and electrostatic complementarity as the main determinants for PPIs.     

MultiProtIdent: identifying proteins using database search and protein-protein interactions

Protein identification is important in proteomics. Proteomic analyses based on mass spectra (MS) constitute innovative ways to identify the components of protein complexes. Instruments can obtain the mass spectrum to an accuracy of 0.01 Da or better, but identification errors are inevitable. This study shows a novel tool, MultiProtIdent, which can identify proteins using additional information about protein-protein interactions and protein functional associations. Both single and multiple Peptide Mass Fingerprints (PMFs) are input to MultiProtIdent, which matches the PMFs to a theoretical peptide mass database. The relationships or interactions among proteins are considered to reduce false positives in PMF matching. Experiments to identify protein complexes reveal that MultiProtIdent is highly promising. The website associated with this study is http://dbms104.csie.ncu.edu.tw/.     

Structure-based analysis of protein-RNA interactions using the program ENTANGLE

Until recently, drawing general conclusions about RNA recognition by proteins has been hindered by the paucity of high-resolution structures. We have analyzed 45 PDB entries of protein-RNA complexes to explore the underlying chemical principles governing both specific and non-sequence specific binding. To facilitate the analysis, we have constructed a database of interactions using ENTANGLE, a JAVA-based program that uses available structural models in their PDB format and searches for appropriate hydrogen bonding, stacking, electrostatic, hydrophobic and van der Waals interactions. The resulting database of interactions reveals correlations that suggest the basis for the discrimination of RNA from DNA and for base-specific recognition. The data illustrate both major and minor interaction strategies employed by families of proteins such as tRNA synthetases, ribosomal proteins, or RNA recognition motifs with their RNA targets. Perhaps most surprisingly, specific RNA recognition appears to be mediated largely by interactions of amide and carbonyl groups in the protein backbone with the edge of the RNA base. In cases where a base accepts a proton, the dominant amino acid donor is arginine, whereas in cases where the base donates a proton, the predominant acceptor is the backbone carbonyl group, not a side-chain group. This is in marked contrast to DNA-protein interactions, which are governed predominantly by amino acid side-chain interactions with functional groups that are presented in the accessible major groove. RNA recognition often proceeds through loops, bulges, kinks and other irregular structures that permit use of all the RNA functional groups and this is seen throughout the protein-RNA interaction database.     

Recognition of 5'-YpG-3' sequences by coupled stacking/hydrogen bonding interactions with amino acid residues

The combined biochemical and structural study of hundreds of protein-DNA complexes has indicated that sequence-specific interactions are mediated by two mechanisms termed direct and indirect readout. Direct readout involves direct interactions between the protein and base-specific atoms exposed in the major and minor grooves of DNA. For indirect readout, the protein recognizes DNA by sensing conformational variations in the structure dependent on nucleotide sequence, typically through interactions with the phosphodiester backbone. Based on our recent structure of Ndt80 bound to DNA in conjunction with a search of the existing PDB database, we propose a new method of sequence-specific recognition that utilizes both direct and indirect readout. In this mode, a single amino acid side-chain recognizes two consecutive base-pairs. The 3'-base is recognized by canonical direct readout, while the 5'-base is recognized through a variation of indirect readout, whereby the conformational flexibility of the particular dinucleotide step, namely a 5'-pyrimidine-purine-3' step, facilitates its recognition by the amino acid via cation-pi interactions. In most cases, this mode of DNA recognition helps explain the sequence specificity of the protein for its target DNA.     

Visualisation and graph-theoretic analysis of a large-scale protein structural interactome

Background  

Large-scale protein interaction maps provide a new, global perspective with which to analyse protein function. PSIMAP, the Protein Structural Interactome Map, is a database of all the structurally observed interactions between superfamilies of protein domains with known three-dimensional structure in the PDB. PSIMAP incorporates both functional and evolutionary information into a single network.