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.     

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/     

Path PPI: an integrated dataset of human pathways and protein-protein interactions

Integration of pathway and protein-protein interaction(PPI) data can provide more information that could lead to new biological insights. PPIs are usually represented by a simple binary model, whereas pathways are represented by more complicated models. We developed a series of rules for transforming protein interactions from pathway to binary model, and the protein interactions from seven pathway databases, including PID, Bio Carta, Reactome, Net Path, INOH, SPIKE and KEGG, were transformed based on these rules. These pathway-derived binary protein interactions were integrated with PPIs from other five PPI databases including HPRD, Int Act, Bio GRID, MINT and DIP, to develop integrated dataset(named Path PPI). More detailed interaction type and modification information on protein interactions can be preserved in Path PPI than other existing datasets. Comparison analysis results indicate that most of the interaction overlaps values(OAB) among these pathway databases were less than 5%, and these databases must be used conjunctively. The Path PPI data was provided at http://proteomeview. hupo.org.cn/Path PPI/Path PPI.html.     

PRIME: automatically extracted PRotein Interactions and Molecular Information databasE

With the exponentially increasing amount of information in the biomedical field, the significance of advanced information retrieval and information extraction, as well as the role of databases, has been increasing. PRIME is an integrated gene/protein informatics database based on natural language processing. It provides automatically extracted protein/family/gene/compound interaction information including both physical and genetic interactions, gene ontology based functions, and graphic pathway viewers. Gene/protein/family names and functional terms are recognized based on dictionaries developed in our laboratory. The interaction and functional information are extracted by syntactic dependencies and various phrase patterns. We have included about 920,000 (non-redundant) protein interactions and 360,000 annotated gene-function relationships for major eukaryotes. By combining the sequence and text information, the pathway comparison between two organisms and simple pathway deduction based on other organism interaction data, and pathway filtering using tissue expression data, are also available. This database is accessible at http://prime.ontology.ims.u-tokyo.ac.jp:8081.     

蛋白质相互作用数据库

随着“蛋白质组学”的蓬勃发展和人类对生物大分子功能机制的知识积累,涌现出海量的蛋白质相互作用数据。随之,研究者开发了300多个蛋白质相互作用数据库,用于存储、展示和数据的重利用。蛋白质相互作用数据库是系统生物学、分子生物学和临床药物研究的宝贵资源。本文将数据库分为3类：（1）综合蛋白质相互作用数据库;（2）特定物种的蛋白质相互作用数据库;（3）生物学通路数据库。重点介绍常用的蛋白质相互作用数据库,包括BioGRID、STRING、IntAct、MINT、DIP、IMEx、HPRD、Reactome和KEGG等。     

Computational approaches to protein-protein interaction

The interactions between proteins allow the cell's life. A number of experimental, genome-wide, high-throughput studies have been devoted to the determination of protein-protein interactions and the consequent interaction networks. Here, the bioinformatics methods dealing with protein-protein interactions and interaction network are overviewed. 1. Interaction databases developed to collect and annotate this immense amount of data; 2. Automated data mining techniques developed to extract information about interactions from the published literature; 3. Computational methods to assess the experimental results developed as a consequence of the finding that the results of high-throughput methods are rather inaccurate; 4. Exploitation of the information provided by protein interaction networks in order to predict functional features of the proteins; and 5. Prediction of protein-protein interactions.     

Mining literature for systems biology

Currently, literature is integrated in systems biology studies in three ways. Hand-curated pathways have been sufficient for assembling models in numerous studies. Second, literature is frequently accessed in a derived form, such as the concepts represented by the Medical Subject Headings (MeSH) and Gene Ontologies (GO), or functional relationships captured in protein-protein interaction (PPI) databases; both of these are convenient, consistent reductions of more complex concepts expressed as free text in the literature. Moreover, their contents are easily integrated into computational processes required for dealing with large data sets. Last, mining text directly for specific types of information is on the rise as text analytics methods become more accurate and accessible. These uses of literature, specifically manual curation, derived concepts captured in ontologies and databases, and indirect and direct application of text mining, will be discussed as they pertain to systems biology.     

MINT and IntAct contribute to the Second BioCreative challenge: serving the text-mining community with high quality molecular interaction data

Background

In the absence of consolidated pipelines to archive biological data electronically, information dispersed in the literature must be captured by manual annotation. Unfortunately, manual annotation is time consuming and the coverage of published interaction data is therefore far from complete. The use of text-mining tools to identify relevant publications and to assist in the initial information extraction could help to improve the efficiency of the curation process and, as a consequence, the database coverage of data available in the literature. The 2006 BioCreative competition was aimed at evaluating text-mining procedures in comparison with manual annotation of protein-protein interactions.

Results

To aid the BioCreative protein-protein interaction task, IntAct and MINT (Molecular INTeraction) provided both the training and the test datasets. Data from both databases are comparable because they were curated according to the same standards. During the manual curation process, the major cause of data loss in mining the articles for information was ambiguity in the mapping of the gene names to stable UniProtKB database identifiers. It was also observed that most of the information about interactions was contained only within the full-text of the publication; hence, text mining of protein-protein interaction data will require the analysis of the full-text of the articles and cannot be restricted to the abstract.

Conclusion

The development of text-mining tools to extract protein-protein interaction information may increase the literature coverage achieved by manual curation. To support the text-mining community, databases will highlight those sentences within the articles that describe the interactions. These will supply data-miners with a high quality dataset for algorithm development. Furthermore, the dictionary of terms created by the BioCreative competitors could enrich the synonym list of the PSI-MI (Proteomics Standards Initiative-Molecular Interactions) controlled vocabulary, which is used by both databases to annotate their data content.

     

Meta-All: a system for managing metabolic pathway information

Background  

Many attempts are being made to understand biological subjects at a systems level. A major resource for these approaches are biological databases, storing manifold information about DNA, RNA and protein sequences including their functional and structural motifs, molecular markers, mRNA expression levels, metabolite concentrations, protein-protein interactions, phenotypic traits or taxonomic relationships. The use of these databases is often hampered by the fact that they are designed for special application areas and thus lack universality. Databases on metabolic pathways, which provide an increasingly important foundation for many analyses of biochemical processes at a systems level, are no exception from the rule. Data stored in central databases such as KEGG, BRENDA or SABIO-RK is often limited to read-only access. If experimentalists want to store their own data, possibly still under investigation, there are two possibilities. They can either develop their own information system for managing that own data, which is very time-consuming and costly, or they can try to store their data in existing systems, which is often restricted. Hence, an out-of-the-box information system for managing metabolic pathway data is needed.     

Finding the evidence for protein-protein interactions from PubMed abstracts

MOTIVATION: Protein-protein interactions play critical roles in biological processes, and many biologists try to find or to predict crucial information concerning these interactions. Before verifying interactions in biological laboratory work, validating them from previous research is necessary. Although many efforts have been made to create databases that store verified information in a structured form, much interaction information still remains as unstructured text. As the amount of new publications has increased rapidly, a large amount of research has sought to extract interactions from the text automatically. However, there remain various difficulties associated with the process of applying automatically generated results into manually annotated databases. For interactions that are not found in manually stored databases, researchers attempt to search for abstracts or full papers. RESULTS: As a result of a search for two proteins, PubMed frequently returns hundreds of abstracts. In this paper, a method is introduced that validates protein-protein interactions from PubMed abstracts. A query is generated from two given proteins automatically and abstracts are then collected from PubMed. Following this, target proteins and their synonyms are recognized and their interaction information is extracted from the collection. It was found that 67.37% of the interactions from DIP-PPI corpus were found from the PubMed abstracts and 87.37% of interactions were found from the given full texts. AVAILABILITY: Contact authors.     

计算方法在蛋白质相互作用研究中的应用

计算方法在蛋白质相互作用研究的各个阶段扮演了一个重要的角色。对此,作者将从以下几个方面对计算方法在蛋白质相互作用及相互作用网络研究中的应用做一个概述：蛋白质相互作用数据库及其发展;数据挖掘方法在蛋白质相互作用数据收集和整合中的应用;高通量方法实验结果的验证;根据蛋白质相互作用网络预测和推断未知蛋白质的功能;蛋白质相互作用的预测。     

Linking publication, gene and protein data

The computational reconstruction of biological systems, 'systems biology', is necessarily dependent on the existence of well-annotated data sets defining and describing the components of these systems, especially genes and the proteins they encode. Information about these components can be accessed either through structured bioinformatics databases, which store basic chemical and functional information abstracted from (or supplementing) the scientific literature, or through the literature itself, which is richer in content but essentially unstructured.     

DEPD: a novel database for differentially expressed proteins

SUMMARY: The Differentially Expressed Protein Database was designed to store the output of comparative proteomics studies and provides a publicly available query and analysis platform for data mining. The database contains information about more than 3000 differentially expressed proteins (DEPs) manually extracted from the published literature, including relevant biological, experimental and methodological elements. Tools for visualization and functional analysis of DEPs are provided via a user-friendly webinterface. AVAILABILITY: http://protchem.hunnu.edu.cn/depd/.     

Computational Prediction of Protein–Protein Interaction Networks: Algo-rithms and Resources

Protein interactions play an important role in the discovery of protein functions and pathways in biological processes. This is especially true in case of the diseases caused by the loss of specific protein-protein interactions in the organism. The accuracy of experimental results in finding protein-protein interactions, however, is rather dubious and high throughput experimental results have shown both high false positive beside false negative information for protein interaction. Computational methods have attracted tremendous attention among biologists because of the ability to predict protein-protein interactions and validate the obtained experimental results. In this study, we have reviewed several computational methods for protein-protein interaction prediction as well as describing major databases, which store both predicted and detected protein-protein interactions, and the tools used for analyzing protein interaction networks and improving protein-protein interaction reliability.     

Textmining in support of knowledge discovery for vaccine development

Complete genome data of infectious microorganisms permit systematic computational sequence-based predictions and experimental testing of candidate vaccine epitopes. Both, predictions and the interpretation of experiments rely on existing information in the literature which is mostly manually extracted and curated. The growing amount of data and literature information has created a major bottleneck for the interpretation of results and maintenance of curated databases. The lack of suitable free-text information extraction, processing, and reporting tools prompted us to develop a knowledge discovery support system that enhances the understanding of immune response and vaccine development. The current prototype system, Gene expression/epitpopes/protein interaction (GEpi), focuses on molecular functions of HIV-infected T-cells and HIV epitope information, using textmining, and interrelation of biomolecular data from domain-specific databases with MEDLINE abstract-inferred information. Results showed that extraction and processing of molecular interaction, disease associations, and gene ontology-derived functional information generate intuitive knowledge reports that aid the interpretation of host-pathogen interaction. In contrast, epitope (word and sequence) information in MEDLINE abstracts is surprisingly sparse and often lacks necessary context information, such as HLA-restriction. Since the majority of epitope information is found in tables, figures, and legends of full-text articles, its extraction may not require sophisticated natural language processing techniques. Support of vaccine development through textmining requires therefore the timely development of domain-specific extraction rules for full-text articles, and a knowledge model for epitope-related information.     

Exploiting the proteome to improve the genome-wide genetic analysis of epistasis in common human diseases

One of the central goals of human genetics is the identification of loci with alleles or genotypes that confer increased susceptibility. The availability of dense maps of single-nucleotide polymorphisms (SNPs) along with high-throughput genotyping technologies has set the stage for routine genome-wide association studies that are expected to significantly improve our ability to identify susceptibility loci. Before this promise can be realized, there are some significant challenges that need to be addressed. We address here the challenge of detecting epistasis or gene–gene interactions in genome-wide association studies. Discovering epistatic interactions in high dimensional datasets remains a challenge due to the computational complexity resulting from the analysis of all possible combinations of SNPs. One potential way to overcome the computational burden of a genome-wide epistasis analysis would be to devise a logical way to prioritize the many SNPs in a dataset so that the data may be analyzed more efficiently and yet still retain important biological information. One of the strongest demonstrations of the functional relationship between genes is protein-protein interaction. Thus, it is plausible that the expert knowledge extracted from protein interaction databases may allow for a more efficient analysis of genome-wide studies as well as facilitate the biological interpretation of the data. In this review we will discuss the challenges of detecting epistasis in genome-wide genetic studies and the means by which we propose to apply expert knowledge extracted from protein interaction databases to facilitate this process. We explore some of the fundamentals of protein interactions and the databases that are publicly available.     

QAUST: Protein Function Prediction Using Structure Similarity,Protein Interaction,and Functional Motifs

The number of available protein sequences in public databases is increasing exponentially. However, a significant percentage of these sequences lack functional annotation, which is essential for the understanding of how biological systems operate. Here, we propose a novel method, Quantitative Annotation of Unknown STructure (QAUST), to infer protein functions, specifically Gene Ontology (GO) terms and Enzyme Commission (EC) numbers. QAUST uses three sources of information: structure information encoded by global and local structure similarity search, biological network information inferred by protein–protein interaction data, and sequence information extracted from functionally discriminative sequence motifs. These three pieces of information are combined by consensus averaging to make the final prediction. Our approach has been tested on 500 protein targets from the Critical Assessment of Functional Annotation (CAFA) benchmark set. The results show that our method provides accurate functional annotation and outperforms other prediction methods based on sequence similarity search or threading. We further demonstrate that a previously unknown function of human tripartite motif-containing 22 (TRIM22) protein predicted by QAUST can be experimentally validated.     

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.