TPR基序——一种介导蛋白质相互作用的结构域

ＴＰＲ蛋白家庭成员功能复杂,均含有ＴＰＲ基序。该基序是一个含有３４个氨基酸的重复序列,通常串行排列。通过形成特殊的空间结构,介导蛋白质的相互作用,并在一些重要的蛋白复合物形成中非常重要。本文拟就ＴＰＲ基序的结构及功能作一简要综述。     

WW结构域——蛋白质-蛋白质相互作用的一种模式

WW结构域是由38～40个氨基酸残基严密组织形成一个连贯、紧凑的结构域;它以包含两个色氨酸残基为主要特征,能专一地与含有XPPXY保守序列的蛋白质相互作用.这种相互作用涉及许多细胞内事件,如非受体信号传导、转录调节、蛋白质降解等等,并且这种相互作用的变化会直接或间接影响到人体的正常生理代谢功能而引起疾病.     

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。     

锚蛋白重复序列介导的蛋白质-蛋白质相互作用

锚蛋白重复序列(ANK)是生物体中广泛利用的一种序列模体.ANK模体在ANK结构域中折叠成β₂α₂结构,在空间上则形成L型结构.数目不等的ANK串联起来, 依靠氢键和疏水相互作用,组成紧密、稳定的结构域,并且形成了种类众多但功能各异的ANK蛋白质分子.ANK结构域介导蛋白质与蛋白质的相互作用,它能够和多种配体结合,实现纷繁复杂的生物功能.着重介绍几类结构已知的ANK家族蛋白质分子及复合物的结构特征、生理功能及与疾病的关系.     

FRET技术及其在蛋白质-蛋白质分子相互作用研究中的应用

简要综述了FRET方法在活细胞生理条件下研究蛋白质-蛋白质间相互作用方面的最新进展.蛋白质-蛋白质间相互作用在整个细胞生命过程中占有重要地位,由于细胞内各种组分极其复杂,因此一些传统研究蛋白质-蛋白质间相互作用的方法,例如酵母双杂交、免疫沉淀等可能会丢失某些重要的信息,无法正确地反映在当时活细胞生理条件下蛋白质-蛋白质间相互作用的动态变化过程.荧光共振能量转移（fluorescence resonance energy transfer, FRET）是近来发展的一项新技术,此项技术的应用,为在活细胞生理条件下对蛋白质-蛋白质间相互作用进行实时的动态研究,提供一个非常便利的条件.     

蛋白质相互作用研究进展

蛋白质行使功能时,需要与其他蛋白质或者其他分子相互作用才能完成.在蛋白质相互作用水平上研究蛋白质对理解蛋白质功能、疾病与进化具有重要的意义.就蛋白质相互作用的预测、常用的蛋白质相互作用数据库以及蛋白质相互作用网络的研究进行了介绍.     

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。     

用于蛋白质间相互作用研究的新型双杂交系统

近年来,一些不依赖于转录因子活性的新型双杂交系统相继建立,如分离的泛素系统、蛋白质片段互补分析、阻遏物重构分析和SOS恢复系统等。同利用转录因子活性的酵母双杂交系统相似,这些方法也利用了一些活性蛋白的结构与功能特点来研究蛋白质间相互作用,这些活性蛋白不是转录因子,但也可在结构上进行分离可通过重构使其生物活性得以恢复。由于这些新型双杂交系统的各自特点,使得它们成为酵母双杂交系统的有益补充和研究蛋白质间相互作用的有力工具。     

蛋白质分子细胞定位的应用

蛋白质分子的功能与其在细胞内的定位密切相关,其细胞定位在新基因克隆、基因功能研究、多肽分子细胞内传送、临床药物设计方面发挥越来越重要的作用。     

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。     

Protein-Protein Interaction Domains of Bacillus subtilis DivIVA

DivIVA proteins are curvature-sensitive membrane binding proteins that recruit other proteins to the poles and the division septum. They consist of a conserved N-terminal lipid binding domain fused to a less conserved C-terminal domain. DivIVA homologues interact with different proteins involved in cell division, chromosome segregation, genetic competence, or cell wall synthesis. It is unknown how DivIVA interacts with these proteins, and we used the interaction of Bacillus subtilis DivIVA with MinJ and RacA to investigate this. MinJ is a transmembrane protein controlling division site selection, and the DNA-binding protein RacA is crucial for chromosome segregation during sporulation. Initial bacterial two-hybrid experiments revealed that the C terminus of DivIVA appears to be important for recruiting both proteins. However, the interpretation of these results is limited since it appeared that C-terminal truncations also interfere with DivIVA oligomerization. Therefore, a chimera approach was followed, making use of the fact that Listeria monocytogenes DivIVA shows normal polar localization but is not biologically active when expressed in B. subtilis. Complementation experiments with different chimeras of B. subtilis and L. monocytogenes DivIVA suggest that MinJ and RacA bind to separate DivIVA domains. Fluorescence microscopy of green fluorescent protein-tagged RacA and MinJ corroborated this conclusion and suggests that MinJ recruitment operates via the N-terminal lipid binding domain, whereas RacA interacts with the C-terminal domain. We speculate that this difference is related to the cellular compartments in which MinJ and RacA are active: the cell membrane and the cytoplasm, respectively.     

蛋白质相互作用的研究方法

蛋白质相互作用的研究是现代分子生物学研究领域一个很重要的方面。该介绍了经典的和最近新建立的研究蛋白质相互作用的方法,并比较了各种方法的优缺点和所适用的领域。     

蛋白质相互作用研究方法及其应用

过去10年来,蛋白质组学得到迅速发展,蛋白质间的相互作用作为蛋白质组学的重要内容,更是成为国内外竞相研究的重点,研究方法的快速发展为蛋白质间相互作用的研究奠定了坚实基础。着重就经典的噬菌体展示、酵母双杂交以及新近发展起来的串联亲和纯化、荧光共振能量转移技术和表面等离子共振等蛋白质相互作用研究方法的原理及应用作一综述并展望其发展前景。     

Geometric De-noising of Protein-Protein Interaction Networks

Understanding complex networks of protein-protein interactions (PPIs) is one of the foremost challenges of the post-genomic era. Due to the recent advances in experimental bio-technology, including yeast-2-hybrid (Y2H), tandem affinity purification (TAP) and other high-throughput methods for protein-protein interaction (PPI) detection, huge amounts of PPI network data are becoming available. Of major concern, however, are the levels of noise and incompleteness. For example, for Y2H screens, it is thought that the false positive rate could be as high as 64%, and the false negative rate may range from 43% to 71%. TAP experiments are believed to have comparable levels of noise.We present a novel technique to assess the confidence levels of interactions in PPI networks obtained from experimental studies. We use it for predicting new interactions and thus for guiding future biological experiments. This technique is the first to utilize currently the best fitting network model for PPI networks, geometric graphs. Our approach achieves specificity of 85% and sensitivity of 90%. We use it to assign confidence scores to physical protein-protein interactions in the human PPI network downloaded from BioGRID. Using our approach, we predict 251 interactions in the human PPI network, a statistically significant fraction of which correspond to protein pairs sharing common GO terms. Moreover, we validate a statistically significant portion of our predicted interactions in the HPRD database and the newer release of BioGRID. The data and Matlab code implementing the methods are freely available from the web site: http://www.kuchaev.com/Denoising.     

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.     

Protein-Protein Docking: From Interaction to Interactome

The protein-protein docking problem is one of the focal points of activity in computational biophysics and structural biology. The three-dimensional structure of a protein-protein complex, generally, is more difficult to determine experimentally than the structure of an individual protein. Adequate computational techniques to model protein interactions are important because of the growing number of known protein structures, particularly in the context of structural genomics. Docking offers tools for fundamental studies of protein interactions and provides a structural basis for drug design. Protein-protein docking is the prediction of the structure of the complex, given the structures of the individual proteins. In the heart of the docking methodology is the notion of steric and physicochemical complementarity at the protein-protein interface. Originally, mostly high-resolution, experimentally determined (primarily by x-ray crystallography) protein structures were considered for docking. However, more recently, the focus has been shifting toward lower-resolution modeled structures. Docking approaches have to deal with the conformational changes between unbound and bound structures, as well as the inaccuracies of the interacting modeled structures, often in a high-throughput mode needed for modeling of large networks of protein interactions. The growing number of docking developers is engaged in the community-wide assessments of predictive methodologies. The development of more powerful and adequate docking approaches is facilitated by rapidly expanding information and data resources, growing computational capabilities, and a deeper understanding of the fundamental principles of protein interactions.     

Protein-Protein Docking: From Interaction to Interactome

The protein-protein docking problem is one of the focal points of activity in computational biophysics and structural biology. The three-dimensional structure of a protein-protein complex, generally, is more difficult to determine experimentally than the structure of an individual protein. Adequate computational techniques to model protein interactions are important because of the growing number of known protein structures, particularly in the context of structural genomics. Docking offers tools for fundamental studies of protein interactions and provides a structural basis for drug design. Protein-protein docking is the prediction of the structure of the complex, given the structures of the individual proteins. In the heart of the docking methodology is the notion of steric and physicochemical complementarity at the protein-protein interface. Originally, mostly high-resolution, experimentally determined (primarily by x-ray crystallography) protein structures were considered for docking. However, more recently, the focus has been shifting toward lower-resolution modeled structures. Docking approaches have to deal with the conformational changes between unbound and bound structures, as well as the inaccuracies of the interacting modeled structures, often in a high-throughput mode needed for modeling of large networks of protein interactions. The growing number of docking developers is engaged in the community-wide assessments of predictive methodologies. The development of more powerful and adequate docking approaches is facilitated by rapidly expanding information and data resources, growing computational capabilities, and a deeper understanding of the fundamental principles of protein interactions.     

Inference of Gene-Phenotype Associations via Protein-Protein Interaction and Orthology

One of the fundamental goals of genetics is to understand gene functions and their associated phenotypes. To achieve this goal, in this study we developed a computational algorithm that uses orthology and protein-protein interaction information to infer gene-phenotype associations for multiple species. Furthermore, we developed a web server that provides genome-wide phenotype inference for six species: fly, human, mouse, worm, yeast, and zebrafish. We evaluated our inference method by comparing the inferred results with known gene-phenotype associations. The high Area Under the Curve values suggest a significant performance of our method. By applying our method to two human representative diseases, Type 2 Diabetes and Breast Cancer, we demonstrated that our method is able to identify related Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways. The web server can be used to infer functions and putative phenotypes of a gene along with the candidate genes of a phenotype, and thus aids in disease candidate gene discovery. Our web server is available at http://jjwanglab.org/PhenoPPIOrth.