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.     

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.     

In vivo quantification of protein-protein interactions in Saccharomyces cerevisiae using bimolecular fluorescence complementation assay

Most of the biological processes are carried out and regulated by dynamic networks of protein-protein interactions. In this study, we demonstrate the feasibility of the bimolecular fluorescence complementation (BiFC) assay for in vivo quantitative analysis of protein-protein interactions in Saccharomyces cerevisiae. We show that the BiFC assay can be used to quantify not only the amount but also the cell-to-cell variation of protein-protein interactions in S. cerevisiae. In addition, we show that protein sumoylation and condition-specific protein-protein interactions can be quantitatively analyzed by using the BiFC assay. Taken together, our results validate that the BiFC assay is a very effective method for quantitative analysis of protein-protein interactions in living yeast cells and has a great potential as a versatile tool for the study of protein function.     

P-LISA技术

原位邻近式连接分析(proximity ligation in situ assay,P-LISA)是一种用来研究蛋白质-蛋白质相互作用的新方法。该方法能够对原位、瞬时、微弱的蛋白质-蛋白质相互作用进行定量分析和亚细胞定位,在药物研发和临床诊断中将有着重要的应用价值。     

Solid phase proteomics: dramatic reinforcement of very weak protein-protein interactions

Very weak protein-protein interactions may play a critical role in cell physiology but they are not easily detectable in "in vitro" experiments. To detect these weak interactions, we have developed a strategy that included: (a) design of a rapid and very effective crosslinking of protein-protein complexes with poly-functional reagents; (b) selective adsorption of very large proteins on lowly activated ionic exchangers, based on the need of a multipoint physical adsorption to incorporate the proteins into the matrix; (c) purification by selective adsorption of protein-protein complexes formed by strong protein-protein interactions, via selective adsorption of the complexes on lowly activated ionic exchangers via multi-protein physical adsorption and leaving the non-associated proteins in the solution; (d) reinforcement of very weak protein-protein interactions by selective adsorption of the complex on lowly activated ionic exchange supports via a synergetic cooperation of the weak protein-protein interaction plus the interactions of both proteins with the support enabling the almost full shifting of the equilibrium towards the association position; (e) control of the aggregation state of proteins like BSA, formed by weak protein-protein interactions. In this last case, it seems that the interaction of the protein molecules placed on the borders of the aggregate with the groups on the support partially stabilizes the whole aggregate, although, some molecules of the aggregate cannot interact with the support. The size of the aggregates may be defined by controlling the concentration of ionised groups on the support: the less activated the supports are, the bigger the complexes. In this way, solid-phase proteomics could be a very interesting tool to detect weak protein-protein interactions.     

蛋白质相互作用的生物信息学研究进展

生命过程的分子基础在于生物分子之间的相互作用,其中蛋白质分子之间的相互作用占有极其重要的地位。研究蛋白质相互作用对于理解生命的真谛、探讨致病微生物的致病机理,以及研究新药提高人们的健康水平具有重要的作用。用生物信息学的方法研究蛋白质的相互作用已经取得了许多重要的成果,但也有很多问题还需解决。本文从蛋白质相互作用的数据库、预测方法、可预测蛋白质相互作用的网上服务、蛋白质相互作用网络等几方面,对蛋白质相互作用的生物信息学研究成果及其存在的问题做了概述。     

Information assessment on predicting protein-protein interactions

Background  

Identifying protein-protein interactions is fundamental for understanding the molecular machinery of the cell. Proteome-wide studies of protein-protein interactions are of significant value, but the high-throughput experimental technologies suffer from high rates of both false positive and false negative predictions. In addition to high-throughput experimental data, many diverse types of genomic data can help predict protein-protein interactions, such as mRNA expression, localization, essentiality, and functional annotation. Evaluations of the information contributions from different evidences help to establish more parsimonious models with comparable or better prediction accuracy, and to obtain biological insights of the relationships between protein-protein interactions and other genomic information.     

Protein-protein interactions: principles, techniques, and their potential role in new drug development

A vast network of genes is inter-linked through protein-protein interactions and is critical component of almost every biological process under physiological conditions. Any disruption of the biologically essential network leads to pathological conditions resulting into related diseases. Therefore, proper understanding of biological functions warrants a comprehensive knowledge of protein-protein interactions and the molecular mechanisms that govern such processes. The importance of protein-protein interaction process is highlighted by the fact that a number of powerful techniques/methods have been developed to understand how such interactions take place under various physiological and pathological conditions. Many of the key protein-protein interactions are known to participate in disease-associated signaling pathways, and represent novel targets for therapeutic intervention. Thus, controlling protein-protein interactions offers a rich dividend for the discovery of new drug targets. Availability of various tools to study and the knowledge of human genome have put us in a unique position to understand highly complex biological network, and the mechanisms involved therein. In this review article, we have summarized protein-protein interaction networks, techniques/methods of their binding/kinetic parameters, and the role of these interactions in the development of potential tools for drug designing.     

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.     

Foreword: The structural biology of membrane proteins

Membrane protein-protein interactions are important for regulation, targeting, and activity of proteins in membranes but are difficult to detect and analyse. This review covers current approaches to studying membrane protein interactions. In addition to standard biochemical and genetic techniques, the classic yeast nuclear two-hybrid system has been highly successful in identification and characterization of soluble protein-protein interactions. However, classic yeast two-hybrid assays do not work for membrane proteins because such yeast-based interactions must occur in the nucleus. Here, we highlight recent advances in yeast systems for the detection and characterization of eukaryote membrane protein-protein interactions. We discuss these implications for drug screening and discovery.     

A fluorescent two-hybrid assay for direct visualization of protein interactions in living cells

Genetic high throughput screens have yielded large sets of potential protein-protein interactions now to be verified and further investigated. Here we present a simple assay to directly visualize protein-protein interactions in single living cells. Using a modified lac repressor system, we tethered a fluorescent bait at a chromosomal lac operator array and assayed for co-localization of fluorescent prey fusion proteins. With this fluorescent two-hybrid assay we successfully investigated the interaction of proteins from different subcellular compartments including nucleus, cytoplasm, and mitochondria. In combination with an S phase marker we also studied the cell cycle dependence of protein-protein interactions. These results indicate that the fluorescent two-hybrid assay is a powerful tool to investigate protein-protein interactions within their cellular environment and to monitor the response to external stimuli in real time.     

发现靶向蛋白质间相互作用的小分子药物研究进展

蛋白质-蛋白质相互作用在多种细胞功能中具有重要的作用。靶向蛋白质-蛋白质相互作用已经成为新药发现的重要策略,但发现能阻断蛋白质-蛋白质相互作用的小分子药物是一个巨大的挑战。尽管如此,近年来人们还是发现了许多能调控蛋白质-蛋白质相互作用的小分子。该文主要总结了在病毒进入、细胞凋亡通路和神经退行性疾病等方面的蛋白质-蛋白质相互作用小分子抑制剂的研究进展。     

Protein-Protein Interactions: Principles,Techniques, and their Potential Role in New Drug Development

Abstract

A vast network of genes is inter-linked through protein-protein interactions and is critical component of almost every biological process under physiological conditions. Any disruption of the biologically essential network leads to pathological conditions resulting into related diseases. Therefore, proper understanding of biological functions warrants a comprehensive knowledge of protein-protein interactions and the molecular mechanisms that govern such processes. The importance of protein-protein interaction process is highlighted by the fact that a number of powerful techniques/methods have been developed to understand how such interactions take place under various physiological and pathological conditions. Many of the key protein-protein interactions are known to participate in disease-associated signaling pathways, and represent novel targets for therapeutic intervention. Thus, controlling protein-protein interactions offers a rich dividend for the discovery of new drug targets. Availability of various tools to study and the knowledge of human genome have put us in a unique position to understand highly complex biological network, and the mechanisms involved therein. In this review article, we have summarized protein-protein interaction networks, techniques/methods of their binding/kinetic parameters, and the role of these interactions in the development of potential tools for drug designing.     

Improving yeast two-hybrid screening systems.

Yeast two-hybrid (Y2H) screening methods are an effective means for the detection of protein-protein interactions. Optimisation and automation has increased the throughput of the method to an extent that allows the systematic mapping of protein-protein interactions on a proteome-wide scale. Since two-hybrid screens fail to detect a great number of interactions, parallel high-throughput approaches are needed for proteome-wide interaction screens. In this review, we discuss and compare different approaches for adaptation of Y2H screening to high-throughput, the limits of the method and possible alternative approaches to complement the mapping of organism-wide protein-protein interactions.     

IDQD算法预测人类蛋白质相互作用

蛋白质是一切生命活动的物质基础,研究蛋白质的相互作用有助于理解生物过程的分子机制,阐明疾病的分子机理。本文依据蛋白质序列组分特征,应用基于多样性增量的二次判别分析方法,对人类的1 963对蛋白质相互作用进行了预测。自洽检验的各项预测指标均在79%以上,且交叉检验的总精度也大于60%,表明本算法可以用于蛋白质相互作用预测。     

Computational structural analysis of protein interactions and networks

Protein interactions have been at the focus of computational biology in recent years. In particular, interest has come from two different communities--structural and systems biology. Here, we will discuss key systems and structural biology methods that have been used for analysis and prediction of protein-protein interactions and the insight these approaches have provided on the nature and organization of protein-protein interactions inside cells.     

植物中验证蛋白相互作用的Pull-down和Co-IP技术

蛋白互作在细胞生命活动中发挥关键作用,在不同时空层面上参与多种细胞学过程,因此研究蛋白互作对理解分子调控网络至关重要。通常情况下,利用酵母双杂交系统筛选植物蛋白互作必须通过体外和体内系统进行验证。Pull-down和Co-IP是验证植物蛋白互作的常用技术。Pull-down被广泛用于体外验证蛋白间的直接互作;而在植物活体内,利用本氏烟草(Nicotiana benthamiana)叶片瞬时表达蛋白,继而通过Co-IP进行鉴定是目前验证蛋白互作最简单且最有效的方法之一。该文对GST Pull-down和烟草瞬时表达系统中Co-IP技术原理及实验方案进行详细描述,以期为验证植物蛋白互作提供参考。     

大规模蛋白质相互作用研究方法进展

随着2000年酵母大规模蛋白质相互作用网络图谱的成功描绘,蛋白质相互作用特别是大规模蛋白质相互作用研究成为生命科学领域的又一个研究热点。酵母、果蝇、线虫以及人类蛋白的大规模相互作用图谱的相继完成,不仅对系统研究细胞内各种生命活动有着重要意义,也标志着蛋白质相互作用研究方法的不断发展和完善。本文综述了当前大规模研究蛋白质相互作用的技术方法并进行了比较分析。每种技术都有其各自的优缺点,在实验中要根据不同的要求和目的选择适宜的方法。     

蛋白质-蛋白质相互作用及其抑制剂研究进展

蛋白质-蛋白质相互作用在细胞活动和生命过程中扮演着非常重要的角色。基因调节、免疫应答、信号转导、细胞组装等等都离不开蛋白质-蛋白质的相互作用。近几年,靶向蛋白质-蛋白质相互作用及其抑制剂研究也逐渐成为研究的热点;但是蛋白质复合物相互作用界面的一些特点和性质,如相互作用界面较大、结合界面较为平坦等,使蛋白质-蛋白质相互作用及其抑制剂研究充满了挑战。本文主要总结了蛋白质-蛋白质相互作用界面的一些性质和特点,分析了界面特性与其抑制剂设计的关系,并讨论了蛋白质-蛋白质相互作用的理论预测方法及其抑制剂的类型和特点,最后又通过实例说明了如何进行蛋白质-蛋白质相互作用抑制剂的设计。