BIND: the Biomolecular Interaction Network Database

The Biomolecular Interaction Network Database (BIND: http://bind.ca) archives biomolecular interaction, complex and pathway information. A web-based system is available to query, view and submit records. BIND continues to grow with the addition of individual submissions as well as interaction data from the PDB and a number of large-scale interaction and complex mapping experiments using yeast two hybrid, mass spectrometry, genetic interactions and phage display. We have developed a new graphical analysis tool that provides users with a view of the domain composition of proteins in interaction and complex records to help relate functional domains to protein interactions. An interaction network clustering tool has also been developed to help focus on regions of interest. Continued input from users has helped further mature the BIND data specification, which now includes the ability to store detailed information about genetic interactions. The BIND data specification is available as ASN.1 and XML DTD.     

Automatic annotation of BIND molecular interactions from three-dimensional structures

Software to automate the process of extracting molecular interactions from three-dimensional (3D) structures has been developed that records these as Biomolecular Interaction Network Database (BIND) pairwise interaction records. Full annotation of BIND records is provided through a database processing tool called MMDBind, including detailed atom-atom and residue-residue level interaction information. BIND three-dimensional interaction annotation is synthesized by combining information from the Molecular Modeling Database (MMDB), and the HET (heterogen) group dictionary of small molecules in the macromolecular Crystallographic Information Format (mmCIF). Interactions are validated using the Protein Quaternary Structure (PQS) system. A total of 18,166 interactions were removed as being redundant or biologically irrelevant after PQS validation. This first pass MMDBind annotation creates two new divisions of BIND, 3D Biopolymers (BIND-3DBP) comprising 16,737 initial interaction records, and 3D Small Molecules (BIND-3DSM) comprising 48,219 records. Visualization of interacting residues and nucleotides within a macromolecular structure is possible directly from the BIND database owing to added 3D feature annotation within the BIND records that can be conveniently seen using Cn3D ("see-in-3D") after query from the BIND Data Manager. These interaction records provide a further demonstration of the completeness of the BIND data specification and its capabilities as storage and exchange format for all kinds of molecular interactions, including RNA, DNA, protein, and small molecules. Data from the 3DBP and 3DSM sets are available for downloading in Abstract Syntax Notation.1 (ASN.1) or Extensible Markup Language (XML) formats at ftp://ftp.bind.ca/DB/MMDBBind. Data from the 3DBP set is available for interactive query from the BIND Data Manager at www.bind.ca.     

Biomolecular interaction network database

This software review looks at the utility of the Biomolecular Interaction Network Database (BIND) as a web database. BIND offers methods common to related biology databases and specialisations for its protein interaction data. Searching and browsing this database is easy and well integrated with the underlying data and the needs of scientists. Interaction networks are visualised with software that offers many useful options. The innovative ontoglyphs are used throughout to provide visual cues to protein functions, localisation and other aspects one needs to know for this data set. One can expect to get useful results that may be well integrated with one's research needs.     

PreBIND and Textomy – mining the biomedical literature for protein-protein interactions using a support vector machine

Background

The majority of experimentally verified molecular interaction and biological pathway data are present in the unstructured text of biomedical journal articles where they are inaccessible to computational methods. The Biomolecular interaction network database (BIND) seeks to capture these data in a machine-readable format. We hypothesized that the formidable task-size of backfilling the database could be reduced by using Support Vector Machine technology to first locate interaction information in the literature. We present an information extraction system that was designed to locate protein-protein interaction data in the literature and present these data to curators and the public for review and entry into BIND.

Results

Cross-validation estimated the support vector machine's test-set precision, accuracy and recall for classifying abstracts describing interaction information was 92%, 90% and 92% respectively. We estimated that the system would be able to recall up to 60% of all non-high throughput interactions present in another yeast-protein interaction database. Finally, this system was applied to a real-world curation problem and its use was found to reduce the task duration by 70% thus saving 176 days.

Conclusions

Machine learning methods are useful as tools to direct interaction and pathway database back-filling; however, this potential can only be realized if these techniques are coupled with human review and entry into a factual database such as BIND. The PreBIND system described here is available to the public at http://bind.ca. Current capabilities allow searching for human, mouse and yeast protein-interaction information.     

Online predicted human interaction database

MOTIVATION: High-throughput experiments are being performed at an ever-increasing rate to systematically elucidate protein-protein interaction (PPI) networks for model organisms, while the complexities of higher eukaryotes have prevented these experiments for humans. RESULTS: The Online Predicted Human Interaction Database (OPHID) is a web-based database of predicted interactions between human proteins. It combines the literature-derived human PPI from BIND, HPRD and MINT, with predictions made from Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster and Mus musculus. The 23,889 predicted interactions currently listed in OPHID are evaluated using protein domains, gene co-expression and Gene Ontology terms. OPHID can be queried using single or multiple IDs and results can be visualized using our custom graph visualization program. AVAILABILITY: Freely available to academic users at http://ophid.utoronto.ca, both in tab-delimited and PSI-MI formats. Commercial users, please contact I.J. CONTACT: juris@ai.utoronto.ca SUPPLEMENTARY INFORMATION: http://ophid.utoronto.ca/supplInfo.pdf.     

Light- and carbon-signaling pathways. Modeling circuits of interactions

Here, we report the systematic exploration and modeling of interactions between light and sugar signaling. The data set analyzed explores the interactions of sugar (sucrose) with distinct light qualities (white, blue, red, and far-red) used at different fluence rates (low or high) in etiolated seedlings and mature green plants. Boolean logic was used to model the effect of these carbon/light interactions on three target genes involved in nitrogen assimilation: asparagine synthetase (ASN1 and ASN2) and glutamine synthetase (GLN2). This analysis enabled us to assess the effects of carbon on light-induced genes (GLN2/ASN2) versus light-repressed genes (ASN1) in this pathway. New interactions between carbon and blue-light signaling were discovered, and further connections between red/far-red light and carbon were modeled. Overall, light was able to override carbon as a major regulator of ASN1 and GLN2 in etiolated seedlings. By contrast, carbon overrides light as the major regulator of GLN2 and ASN2 in light-grown plants. Specific examples include the following: Carbon attenuated the blue-light induction of GLN2 in etiolated seedlings and also attenuated the white-, blue-, and red-light induction of GLN2 and ASN2 in light-grown plants. By contrast, carbon potentiated far-red-light induction of GLN2 and ASN2 in light-grown plants. Depending on the fluence rate of far-red light, carbon either attenuated or potentiated light repression of ASN1 in light-grown plants. These studies indicate the interaction of carbon with blue, red, and far-red-light signaling and set the stage for further investigation into modeling this complex web of interacting pathways using systems biology approaches.     

Extracting human protein interactions from MEDLINE using a full-sentence parser

MOTIVATION: The living cell is a complex machine that depends on the proper functioning of its numerous parts, including proteins. Understanding protein functions and how they modify and regulate each other is the next great challenge for life-sciences researchers. The collective knowledge about protein functions and pathways is scattered throughout numerous publications in scientific journals. Bringing the relevant information together becomes a bottleneck in a research and discovery process. The volume of such information grows exponentially, which renders manual curation impractical. As a viable alternative, automated literature processing tools could be employed to extract and organize biological data into a knowledge base, making it amenable to computational analysis and data mining. RESULTS: We present MedScan, a completely automated natural language processing-based information extraction system. We have used MedScan to extract 2976 interactions between human proteins from MEDLINE abstracts dated after 1988. The precision of the extracted information was found to be 91%. Comparison with the existing protein interaction databases BIND and DIP revealed that 96% of extracted information is novel. The recall rate of MedScan was found to be 21%. Additional experiments with MedScan suggest that MEDLINE is a unique source of diverse protein function information, which can be extracted in a completely automated way with a reasonably high precision. Further directions of the MedScan technology improvement are discussed. AVAILABILITY: MedScan is available for commercial licensing from Ariadne Genomics, Inc.     

Toxicity of extracellular secreted alpha-synuclein: Its role in nitrosative stress and neurodegeneration

It has been demonstrated that both oligomerisation and accumulation of α-synuclein (ASN) are the key molecular processes involved in the pathophysiology of neurodegenerative diseases such as Parkinson’s disease, Alzheimer’s disease and other synucleinopathies. Alterations of ASN expression and impairment of its degradation can lead to the formation of intracellular deposits of this protein, called Lewy bodies. Overexpressed or misfolded ASN could be secreted to the extracellular space. Today the prion-like transmission of ASN oligomers to neighbouring cells is believed to be responsible for protein modification and propagation of neurodegeneration in the brain. It was presented that oxidative/nitrosative stress may play a key role in ASN secretion and spread of ASN pathology. Moreover, ASN-evoked protein oxidation, nitration and nitrosylation lead to disturbances in synaptic transmission and cell death. The interaction of secreted ASN with other amyloidogenic proteins and its involvement in irreversible mitochondrial disturbances and oxidative stress were also described. A better understanding of the mechanisms of ASN secretion and dysfunction may help to explain the molecular mechanisms of neurodegeneration and may be the basis for the development of novel therapeutic strategies.     

MPIDB: the microbial protein interaction database

SUMMARY: The microbial protein interaction database (MPIDB) aims to collect and provide all known physical microbial interactions. Currently, 22,530 experimentally determined interactions among proteins of 191 bacterial species/strains can be browsed and downloaded. These microbial interactions have been manually curated from the literature or imported from other databases (IntAct, DIP, BIND, MINT) and are linked to 24,060 experimental evidences (PubMed ID, PSI-MI methods). In contrast to these databases, interactions in MPIDB are further supported by 8150 additional evidences based on interaction conservation, co-purification and 3D domain contacts (iPfam, 3did). AVAILABILITY: http://www.jcvi.org/mpidb/     

Heat shock protein 90 mediates protein-protein interactions between human aminoacyl-tRNA synthetases

Heat shock protein 90 (hsp90) is a molecular chaperone responsible for protein folding and maturation in vivo. Interaction of hsp90 with human glutamyl-prolyl-tRNA synthetase (EPRS) was found by genetic screening, co-immunoprecipitation, and in vitro binding experiments. This interaction was sensitive to the hsp90 inhibitor, geldanamycin, and also ATP, suggesting that the chaperone activity of hsp90 is required for interaction with EPRS. Interaction of EPRS with hsp90 was targeted to the region of three tandem repeats linking the two catalytic domains of EPRS that is also responsible for the interaction with isoleucyl-tRNA synthetase (IRS). Interaction of EPRS and IRS also depended on the activity of hsp90, implying that their association was mediated by hsp90. EPRS and IRS form a macromolecular protein complex with at least six other tRNA synthetases and three cofactors. hsp90 preferentially binds to most of the complex-forming enzymes rather than those that are not found in the complex. In addition, inactivation of hsp90 interfered with the in vivo incorporation of the nascent aminoacyl-tRNA synthetases into the multi-ARS complex. Thus, hsp90 appears to mediate protein-protein interactions of mammalian tRNA synthetases.     

Two functionally distinct steps mediate high affinity binding of U1A protein to U1 hairpin II RNA

Binding of the U1A protein to its RNA target U1 hairpin II has been extensively studied as a model for a high affinity RNA/protein interaction. However, the mechanism and kinetics by which this complex is formed remain largely unknown. Here we use real-time biomolecular interaction analysis to dissect the roles various protein and RNA structural elements play in the formation of the U1A.U1 hairpin II complex. We show that neutralization of positive charges on the protein or increasing the salt concentration slows the association rate, suggesting that electrostatic interactions play an important role in bringing RNA and protein together. In contrast, removal of hydrogen bonding or stacking interactions within the RNA/protein interface, or reducing the size of the RNA loop, dramatically destabilizes the complex, as seen by a strong increase in the dissociation rate. Our data support a binding mechanism consisting of a rapid initial association based on electrostatic interactions and a subsequent locking step based on close-range interactions that occur during the induced fit of RNA and protein. Remarkably, these two steps can be clearly distinguished using U1A mutants containing single amino acid substitutions. Our observations explain the extraordinary affinity of U1A for its target and may suggest a general mechanism for high affinity RNA/protein interactions.     

Identification of novel p53-binding proteins by biomolecular interaction analysis combined with tandem mass spectrometry

Electrospray tandem mass spectrometry (ESI-MS/MS) was combined with biomolecular interaction analysis (BIA) to develop a method of direct protein identification after real-time analysis of protein-protein interactions. Using this method, called BIA-MS/MS, we detected multiple p53-interacting proteins in whole tissue extracts from human placenta and liver. Peptide sequencing revealed three proteins whose interaction with p53 had not been previously reported: a cyclin-dependent kinase inhibitor p57/Kip2, a serine/threonine protein phosphatase PP1C, and hemoglobin. Using our system, unambiguous sequence information can be obtained at the femto- to picomole level after repeating the recovery procedure five times. Furthermore, the association and dissociation constants are easily determined by kinetic analysis. This system provides a powerful tool for analyzing complex biological materials in a simple but highly specific and sensitive manner.     

Predicting and validating protein interactions using network structure

Protein interactions play a vital part in the function of a cell. As experimental techniques for detection and validation of protein interactions are time consuming, there is a need for computational methods for this task. Protein interactions appear to form a network with a relatively high degree of local clustering. In this paper we exploit this clustering by suggesting a score based on triplets of observed protein interactions. The score utilises both protein characteristics and network properties. Our score based on triplets is shown to complement existing techniques for predicting protein interactions, outperforming them on data sets which display a high degree of clustering. The predicted interactions score highly against test measures for accuracy. Compared to a similar score derived from pairwise interactions only, the triplet score displays higher sensitivity and specificity. By looking at specific examples, we show how an experimental set of interactions can be enriched and validated. As part of this work we also examine the effect of different prior databases upon the accuracy of prediction and find that the interactions from the same kingdom give better results than from across kingdoms, suggesting that there may be fundamental differences between the networks. These results all emphasize that network structure is important and helps in the accurate prediction of protein interactions. The protein interaction data set and the program used in our analysis, and a list of predictions and validations, are available at http://www.stats.ox.ac.uk/bioinfo/resources/PredictingInteractions.     

蛋白质相互作用数据库及其应用

对蛋白质相互作用及其网络的了解不仅有助于深入理解生命活动的本质和疾病发生的机制,而且可以为药物研发提供靶点.目前,通过高通量筛选、计算方法预测和文献挖掘等方法,获得了大批量的蛋白质相互作用数据,并由此构建了很多内容丰富并日益更新的蛋白质相互作用数据库.本文首先简要阐述了大规模蛋白质相互作用数据产生的3种方法,然后重点介绍了几个人类相关的蛋白质相互作用公共数据库,包括HPRD、BIND、 IntAct、MINT、 DIP 和MIPS,并概述了蛋白质相互作用数据库的整合情况以及这些数据库在蛋白质相互作用网络构建上的应用.     

Molecular simulation of N-acetylneuraminic acid analogs and molecular dynamics studies of cholera toxin-Neu5Gc complex

Cholera toxin (CT) is an AB₅ protein complex secreted by the pathogen Vibrio cholera, which is responsible for cholera infection. N-acetylneuraminic acid (NeuNAc) is a derivative of neuraminic acid with nine-carbon backbone. NeuNAc is distributed on the cell surface mainly located in the terminal components of glycoconjugates, and also plays an important role in cell–cell interaction. In our current study, molecular docking and molecular dynamic (MD) simulations were implemented to identify the potent NeuNAc analogs with high-inhibitory activity against CT protein. Thirty-four NeuNAc analogs, modified in different positions C-1/C-2/C-4/C-5/C-7/C-8/C-9, were modeled and docked against the active site of CT protein. Among the 34 NeuNAc analogs, the analog Neu5Gc shows the least extra precision glide score of ?9.52 and glide energy of ?44.71?kcal/mol. NeuNAc analogs block the CT active site residues HIS:13, ASN:90, LYS:91, GLN:56, GLN:61, and TRP:88 through intermolecular hydrogen bonding. The MD simulation for CT-Neu5Gc docking complex was performed using Desmond. MD simulation of CT-Neu5Gc complex reveals the stable nature of docking interaction.