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.     

水稻锚蛋白基因家族分析和锚定膜蛋白的表达模式研究

锚蛋白重复序列模体是生物体内最普遍的蛋白质序列模体之一,在多种细胞活动中主要介导蛋白质与蛋白质的相互作用。采用生物信息学的方法,在基因组水平上搜索和鉴定了水稻锚定重复序列蛋白,通过分析蛋白质二级结构,进行水稻锚蛋白基因家族的聚类分析,并在此基础上,用RT-PCR的方法分析水稻锚定膜蛋白的表达模式,为水稻锚蛋白基因的研究提供了理论依据。     

Prediction of Heterodimeric Protein Complexes from Weighted Protein-Protein Interaction Networks Using Novel Features and Kernel Functions

Since many proteins express their functional activity by interacting with other proteins and forming protein complexes, it is very useful to identify sets of proteins that form complexes. For that purpose, many prediction methods for protein complexes from protein-protein interactions have been developed such as MCL, MCODE, RNSC, PCP, RRW, and NWE. These methods have dealt with only complexes with size of more than three because the methods often are based on some density of subgraphs. However, heterodimeric protein complexes that consist of two distinct proteins occupy a large part according to several comprehensive databases of known complexes. In this paper, we propose several feature space mappings from protein-protein interaction data, in which each interaction is weighted based on reliability. Furthermore, we make use of prior knowledge on protein domains to develop feature space mappings, domain composition kernel and its combination kernel with our proposed features. We perform ten-fold cross-validation computational experiments. These results suggest that our proposed kernel considerably outperforms the naive Bayes-based method, which is the best existing method for predicting heterodimeric protein complexes.     

Methods for the detection and analysis of protein-protein interactions

A large number of methods have been developed over the years to study protein-protein interactions. Many of these techniques are now available to the nonspecialist researcher thanks to new affordable instruments and/or resource centres. A typical protein-protein interaction study usually starts with an initial screen for novel binding partners. We start this review by describing three techniques that can be used for this purpose: (i) affinity-tagged proteins (ii) the two-hybrid system and (iii) some quantitative proteomic techniques that can be used in combination with, e.g., affinity chromatography and coimmunoprecipitation for screening of protein-protein interactions. We then describe some public protein-protein interaction databases that can be searched to identify previously reported interactions for a given bait protein. Four strategies for validation of protein-protein interactions are presented: confocal microscopy for intracellular colocalization of proteins, coimmunoprecipitation, surface plasmon resonance (SPR) and spectroscopic studies. Throughout the review we focus particularly on the advantages and limitations of each method.     

Augmented transitive relationships with high impact protein distillation in protein interaction prediction

Predicting new protein-protein interactions is important for discovering novel functions of various biological pathways. Predicting these interactions is a crucial and challenging task. Moreover, discovering new protein-protein interactions through biological experiments is still difficult. Therefore, it is increasingly important to discover new protein interactions. Many studies have predicted protein-protein interactions, using biological features such as Gene Ontology (GO) functional annotations and structural domains of two proteins. In this paper, we propose an augmented transitive relationships predictor (ATRP), a new method of predicting potential protein interactions using transitive relationships and annotations of protein interactions. In addition, a distillation of virtual direct protein-protein interactions is proposed to deal with unbalanced distribution of different types of interactions in the existing protein-protein interaction databases. Our results demonstrate that ATRP can effectively predict protein-protein interactions. ATRP achieves an 81% precision, a 74% recall and a 77% F-measure in average rate in the prediction of direct protein-protein interactions. Using the generated benchmark datasets from KUPS to evaluate of all types of the protein-protein interaction, ATRP achieved a 93% precision, a 49% recall and a 64% F-measure in average rate. This article is part of a Special Issue entitled: Computational Methods for Protein Interaction and Structural Prediction.     

Evolution and dynamics of protein interactions and networks

The central role of protein-protein interactions (PPIs) in biology has stimulated colossal efforts to identify thousands of them in several organisms. The resulting PPI maps are commonly represented as graphs, where nodes denote proteins and edges represent physical interactions. However, the methods used to generate PPI data on a large scale do not readily allow one to discriminate features such as interaction strength (affinity), type (protein-protein or protein-peptide interaction) or spatiotemporal existence (where and when the proteins are present and interact). Yet, in recent years, a number of studies have tackled these limitations by projecting additional information onto PPIs, revealing novel properties in terms of their evolution and dynamics. In this review we examine these properties both at the binary interaction level and at the network level. We suggest that the diverse and sometimes contradictory results described by different research groups are mostly due to incomplete data coverage and limited data types. Finally, we discuss recently developed methods that will improve this picture in the future.     

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.     

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。     

串联亲和纯化（TAP）技术在蛋白质组学中的应用

蛋白质是各种生命活动的主要执行者,因此构建蛋白质相互作用的网络图对于准确理解蛋白质功能、揭开各种细胞活动的奥秘十分重要.串联亲和纯化（TAP）,是近年来发展出来的一种能够快速研究在生理条件下蛋白质相互作用,揭示蛋白质复合体相互作用网络的新技术,已成为研究蛋白质组学的一个重要工具.随着该技术的不断完善,TAP技术在认识蛋白质相互作用的过程中必将发挥越来越重要的作用.     

Tetramer protein complex interface residue pairs prediction with LSTM combined with graph representations

MotivationProtein-protein interactions are important for many biological processes. Theoretical understanding of the structurally determining factors of interaction sites will help to understand the underlying mechanism of protein-protein interactions. Taking advantage of advanced mathematical methods to correctly predict interaction sites will be useful. Although some previous studies have been devoted to the interaction interface of protein monomer and the interface residues between chains of protein dimers, very few studies about the interface residues prediction of protein multimers, including trimers, tetramer and even more monomers in a large protein complex. As we all know, a large number of proteins function with the form of multibody protein complexes. And the complexity of the protein multimers structure causes the difficulty of interface residues prediction on them. So, we hope to build a method for the prediction of protein tetramer interface residue pairs.ResultsHere, we developed a new deep network based on LSTM network combining with graph to predict protein tetramers interaction interface residue pairs. On account of the protein structure data is not the same as the image or video data which is well-arranged matrices, namely the Euclidean Structure mentioned in many researches. Because the Non-Euclidean Structure data can't keep the translation invariance, and we hope to extract some spatial features from this kind of data applying on deep learning, an algorithm combining with graph was developed to predict the interface residue pairs of protein interactions based on a topological graph building a relationship between vertexes and edges in graph theory combining multilayer Long Short-Term Memory network. First, selecting the training and test samples from the Protein Data Bank, and then extracting the physicochemical property features and the geometric features of surface residue associated with interfacial properties. Subsequently, we transform the protein multimers data to topological graphs and predict protein interaction interface residue pairs using the model. In addition, different types of evaluation indicators verified its validity.     

Predicting binding sites of hydrolase-inhibitor complexes by combining several methods

Background

Protein-protein interactions play a critical role in protein function. Completion of many genomes is being followed rapidly by major efforts to identify interacting protein pairs experimentally in order to decipher the networks of interacting, coordinated-in-action proteins. Identification of protein-protein interaction sites and detection of specific amino acids that contribute to the specificity and the strength of protein interactions is an important problem with broad applications ranging from rational drug design to the analysis of metabolic and signal transduction networks.

Results

In order to increase the power of predictive methods for protein-protein interaction sites, we have developed a consensus methodology for combining four different methods. These approaches include: data mining using Support Vector Machines, threading through protein structures, prediction of conserved residues on the protein surface by analysis of phylogenetic trees, and the Conservatism of Conservatism method of Mirny and Shakhnovich. Results obtained on a dataset of hydrolase-inhibitor complexes demonstrate that the combination of all four methods yield improved predictions over the individual methods.

Conclusions

We developed a consensus method for predicting protein-protein interface residues by combining sequence and structure-based methods. The success of our consensus approach suggests that similar methodologies can be developed to improve prediction accuracies for other bioinformatic problems.     

In silico discovery and experimental validation of new protein-protein interactions

We introduce a framework for predicting novel protein-protein interactions (PPIs), based on Fisher's method for combining probabilities of predictions that are based on different data sources, such as the biomedical literature, protein domain and mRNA expression information. Our method compares favorably to our previous method based on text-mining alone and other methods such as STRING. We evaluated our algorithms through the prediction of experimentally found protein interactions underlying Muscular Dystrophy, Huntington's Disease and Polycystic Kidney Disease, which had not yet been recorded in protein-protein interaction databases. We found a 1.74-fold increase in the mean average prediction precision for dysferlin and a 3.09-fold for huntingtin when compared to STRING. The top 10 of predicted interaction partners of huntingtin were analysed in depth. Five were identified previously, and the other five were new potential interaction partners. The full matrix of human protein pairs and their prediction scores are available for download. Our framework can be extended to predict other types of relationships such as proteins in a complex, pathway or related disease mechanisms.     

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.     

Integrative approach for computationally inferring protein domain interactions

MOTIVATION: The current need for high-throughput protein interaction detection has resulted in interaction data being generated en masse through such experimental methods as yeast-two-hybrids and protein chips. Such data can be erroneous and they often do not provide adequate functional information for the detected interactions. Therefore, it is useful to develop an in silico approach to further validate and annotate the detected protein interactions. RESULTS: Given that protein-protein interactions involve physical interactions between protein domains, domain-domain interaction information can be useful for validating, annotating, and even predicting protein interactions. However, large-scale, experimentally determined domain-domain interaction data do not exist. Here, we describe an integrative approach to computationally derive putative domain interactions from multiple data sources, including protein interactions, protein complexes, and Rosetta Stone sequences. We further prove the usefulness of such an integrative approach by applying the derived domain interactions to predict and validate protein-protein interactions. AVAILABILITY: A database of putative protein domain interactions derived using the method described in this paper is available at http://interdom.lit.org.sg.     

BIPSPI+: Mining Type-Specific Datasets of Protein Complexes to Improve Protein Binding Site Prediction

Computational approaches for predicting protein-protein interfaces are extremely useful for understanding and modelling the quaternary structure of protein assemblies. In particular, partner-specific binding site prediction methods allow delineating the specific residues that compose the interface of protein complexes. In recent years, new machine learning and other algorithmic approaches have been proposed to solve this problem. However, little effort has been made in finding better training datasets to improve the performance of these methods. With the aim of vindicating the importance of the training set compilation procedure, in this work we present BIPSPI+, a new version of our original server trained on carefully curated datasets that outperforms our original predictor. We show how prediction performance can be improved by selecting specific datasets that better describe particular types of protein interactions and interfaces (e.g. homo/hetero). In addition, our upgraded web server offers a new set of functionalities such as the sequence-structure prediction mode, hetero- or homo-complex specialization and the guided docking tool that allows to compute 3D quaternary structure poses using the predicted interfaces. BIPSPI+ is freely available at https://bipspi.cnb.csic.es.     

Green fluorescent protein as a signal for protein-protein interactions.

Green fluorescent protein (GFP) is autofluorescent. This property has made GFP useful in monitoring in vivo activities such as gene expression and protein localization. We find that GFP can be used in vitro to reveal and characterize protein-protein interactions. The interaction between the S-peptide and S-protein fragments of ribonuclease A was chosen as a model system. GFP-tagged S-peptide was produced, and the interaction of this fusion protein with S-protein was analyzed by two distinct methods: fluorescence gel retardation and fluorescence polarization. The fluorescence gel retardation assay is a rapid method to demonstrate the existence of a protein-protein interaction and to estimate the dissociation constant (Kd) of the resulting complex. The fluorescence polarization assay is an accurate method to evaluate Kd in a specified homogeneous solution and can be adapted for the high-throughput screening of protein or peptide libraries. These two methods are powerful new tools to probe protein-protein interactions.     

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。     

In silico protein interaction analysis using the global proteome machine database

Experiments to probe for protein-protein interactions are the focus of functional proteomic studies, thus proteomic data repositories are increasingly likely to contain a large cross-section of such information. Here, we use the Global Proteome Machine database (GPMDB), which is the largest curated and publicly available proteomic data repository derived from tandem mass spectrometry, to develop an in silico protein interaction analysis tool. Using a human histone protein for method development, we positively identified an interaction partner from each histone protein family that forms the histone octameric complex. Moreover, this method, applied to the α subunits of the human proteasome, identified all of the subunits in the 20S core particle. Furthermore, we applied this approach to human integrin αIIb and integrin β3, a major receptor involved in the activation of platelets. We identified 28 proteins, including a protein network for integrin and platelet activation. In addition, proteins interacting with integrin β1 obtained using this method were validated by comparing them to those identified in a formaldehyde-supported coimmunoprecipitation experiment, protein-protein interaction databases and the literature. Our results demonstrate that in silico protein interaction analysis is a novel tool for identifying known/candidate protein-protein interactions and proteins with shared functions in a protein network.     

基于Internet网生物信息资源特定基因同源新基因克隆策略

随着人类基因组计划的基本完成,各类生物信息学数据库和软件层出不穷,利用Internet网上生物信息资源克隆新基因的策略也得到新的发展。作者在简单介绍网上生物信息资源以及新基因克隆策略的同时,着重对基于EST数据库和基因组数据库特定基因的同源新基因克隆策略进行了详细阐述。     

Construction of reliable protein-protein interaction networks with a new interaction generality measure

MOTIVATION: Recent screening techniques have made large amounts of protein-protein interaction data available, from which biologically important information such as the function of uncharacterized proteins, the existence of novel protein complexes, and novel signal-transduction pathways can be discovered. However, experimental data on protein interactions contain many false positives, making these discoveries difficult. Therefore computational methods of assessing the reliability of each candidate protein-protein interaction are urgently needed. RESULTS: We developed a new 'interaction generality' measure (IG2) to assess the reliability of protein-protein interactions using only the topological properties of their interaction-network structure. Using yeast protein-protein interaction data, we showed that reliable protein-protein interactions had significantly lower IG2 values than less-reliable interactions, suggesting that IG2 values can be used to evaluate and filter interaction data to enable the construction of reliable protein-protein interaction networks.