Prediction of Protein-Protein Interactions Using Protein Signature Profiling

Protein domains are conserved and functionally independent structures that play an important role in interactions among related proteins. Domain-domain inter- actions have been recently used to predict protein-protein interactions (PPI). In general, the interaction probability of a pair of domains is scored using a trained scoring function. Satisfying a threshold, the protein pairs carrying those domains are regarded as “interacting“. In this study, the signature contents of proteins were utilized to predict PPI pairs in Saccharomyces cerevisiae, Caenorhabditis ele- gans, and Homo sapiens. Similarity between protein signature patterns was scored and PPI predictions were drawn based on the binary similarity scoring function. Results show that the true positive rate of prediction by the proposed approach is approximately 32% higher than that using the maximum likelihood estimation method when compared with a test set, resulting in 22% increase in the area un- der the receiver operating characteristic (ROC) curve. When proteins containing one or two signatures were removed, the sensitivity of the predicted PPI pairs in- creased significantly. The predicted PPI pairs are on average 11 times more likely to interact than the random selection at a confidence level of 0.95, and on aver- age 4 times better than those predicted by either phylogenetic profiling or gene expression profiling.     

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.     

Understanding protein-protein interactions by genetic suppression

Protein-protein interactions influence many cellular processes and it is increasingly being felt that even a weak and remote interplay between two subunits of a protein or between two proteins in a complex may govern the fate of a particular biochemical pathway. In a bacterial system where the complete genome sequence is available, it is an arduous task to assign function to a large number of proteins. It is possible that many of them are peripherally associated with a cellular event and it is very difficult to probe such interaction. However, mutations in the genes that encode such proteins (primary mutations) are useful in these studies. Isolation of a suppressor or a second-site mutation that restores the phenotype abolished by the primary mutation could be an elegant yet simple way to follow a set of interacting proteins. Such a reversion site need not necessarily be geometrically close to the primary mutation site.     

Novel assays to monitor gene expression and protein-protein interactions in rice using the bioluminescent protein,NanoLuc

Luciferases have been widely utilized as sensitive reporters to monitor gene expression and protein-protein interactions. Compared to firefly luciferase (Fluc), a recently developed luciferase, Nanoluciferase (NanoLuc or Nluc), has several superior properties such as a smaller size and stronger luminescence activity. We compared the reporter properties of Nluc and Fluc in rice (Oryza sativa). In both plant-based two-hybrid and split luc complementation (SLC) assays, Nluc activity was detected with higher sensitivity and specificity than that with Fluc. To apply Nluc to research involving the photoperiodic regulation of flowering, we made a knock-in rice plant in which the Nluc coding region was inserted in-frame with the OsMADS15 gene, a target of the rice florigen Hd3a. Strong Nluc activity in response to Hd3a, and in response to change in day length, was detected in rice protoplasts and in a single shoot apical meristem, respectively. Our results indicate that Nluc assay systems will be powerful tools to monitor gene expression and protein-protein interaction in plant research.     

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。     

牵出技术在蛋白质相互作用研究中的应用

蛋白质与蛋白质的相互作用参与生命体内的许多生物学过程,关于蛋白质相互作用的研究是人们了解蛋白质功能、揭开生命奥秘的关键所在。牵出(pull-down)技术作为一种简单、经济、行之有效的一种体外验证蛋白质与蛋白质之间的相互作用的实验技术,近几年受到了科研人员的青睐。本文阐述了该技术的基本原理和技术特点,总结了近年来牵出技术在生命科学领域中的应用情况,以及由此衍生的新技术的研究进展。     

Biological units and their effect upon the properties and prediction of protein-protein interactions

Structural data as collated in the Protein Data Bank (PDB) have been widely applied in the study and prediction of protein-protein interactions. However, since the basic PDB Entries contain only the contents of the asymmetric unit rather than the biological unit, some key interactions may be missed by analysing only the PDB Entry. A total of 69,054 SCOP (Structural Classification of Proteins) domains were examined systematically to identify the number of additional novel interacting domain pairs and interfaces found by considering the biological unit as stored in the PQS (Protein Quaternary Structure) database. The PQS data adds 25,965 interacting domain pairs to those seen in the PDB Entries to give a total of 61,783 redundant interacting domain pairs. Redundancy filtering at the level of the SCOP family shows PQS to increase the number of novel interacting domain-family pairs by 302 (13.3%) from 2277, but only 16/302 (1.4%) of the interacting domain pairs have the two domains in different SCOP families. This suggests the biological units add little to the elucidation of novel biological interaction networks. However, when the orientation of the domain pairs is considered, the PQS data increases the number of novel domain-domain interfaces observed by 1455 (34.5%) to give 5677 non-redundant domain-domain interfaces. In all, 162/1455 novel domain-domain interfaces are between domains from different families, an increase of 8.9% over the PDB Entries. Overall, the PQS biological units provide a rich source of novel domain-domain interfaces that are not seen in the studied PDB Entries, and so PQS domain-domain interaction data should be exploited wherever possible in the analysis and prediction of protein-protein interactions.     

Proteome-wide prediction of protein-protein interactions from high-throughput data

In this paper,we present a brief review of the existing computational methods for predicting proteome-wide protein-protein interaction networks from highthroughput data,The availability of various types of omics data provides great opportunity and also unprecedented challenge to infer the interactome in cells.Reconstructing the interactome or interaction network is a crucial step for studying the functional relationship among proteins and the involved biological processes.The protein interaction network will provide valuable resources and alternatives to decipher the mechanisms of these functionally interacting elements as well as the running system of cellular operations.In this paper,we describe the main steps of predicting protein-protein interaction networks and categorize the available approaches 1o couple the physical and functional linkages.The future topics and the analyses beyond prediction are also discussed and concluded.     

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。     

Exploring protein-protein interactions at the proteome level

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Exploring protein-protein interactions using the site-identification by ligand competitive saturation methodology

Protein docking methods are powerful computational tools to study protein-protein interactions (PPI). While a significant number of docking algorithms have been developed, they are usually based on rigid protein models or with limited considerations of protein flexibility and the desolvation effect is rarely considered in docking energy functions, which may lower the accuracy of the predictions. To address these issues, we introduce a PPI energy function based on the site-identification by ligand competitive saturation (SILCS) framework and utilize the fast Fourier transform (FFT) correlation approach. The free energy content of the SILCS FragMaps represent an alternative to traditional energy grids and they can be efficiently utilized to guide FFT-based protein docking. Application of the approach to eight diverse test cases, including seven from Protein Docking Benchmark 5.0, showed the PPI prediction using SILCS approach (SILCS-PPI) to be competitive with several commonly used protein docking methods indicating that the method has the ability to both qualitatively and quantitatively inform the prediction of PPI. Results show the utility of the SILCS-PPI docking approach for determination of probability distributions of PPI interactions over the surface of both partner proteins, allowing for identification of alternate binding poses. Such binding poses are confirmed by experimental crystal contacts in our test cases. While more computationally demanding than available PPI docking technologies, we anticipate that the SILCS-PPI docking approach will offer an alternative methodology for improved evaluation of PPIs that could be used in a variety of fields from systems biology to excipient design for biologics-based drugs.     

Co-evolutionary analysis reveals insights into protein-protein interactions

Protein-protein interactions play crucial roles in biological processes. Experimental methods have been developed to survey the proteome for interacting partners and some computational approaches have been developed to extend the impact of these experimental methods. Computational methods are routinely applied to newly discovered genes to infer protein function and plausible protein-protein interactions. Here, we develop and extend a quantitative method that identifies interacting proteins based upon the correlated behavior of the evolutionary histories of protein ligands and their receptors. We have studied six families of ligand-receptor pairs including: the syntaxin/Unc-18 family, the GPCR/G-alpha's, the TGF-beta/TGF-beta receptor system, the immunity/colicin domain collection from bacteria, the chemokine/chemokine receptors, and the VEGF/VEGF receptor family. For correlation scores above a defined threshold, we were able to find an average of 79% of all known binding partners. We then applied this method to find plausible binding partners for proteins with uncharacterized binding specificities in the syntaxin/Unc-18 protein and TGF-beta/TGF-beta receptor families. Analysis of the results shows that co-evolutionary analysis of interacting protein families can reduce the search space for identifying binding partners by not only finding binding partners for uncharacterized proteins but also recognizing potentially new binding partners for previously characterized proteins. We believe that correlated evolutionary histories provide a route to exploit the wealth of whole genome sequences and recent systematic proteomic results to extend the impact of these studies and focus experimental efforts to categorize physiologically or pathologically relevant 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。     

人类蛋白质相互作用组

高通量酵母双杂交与免疫亲和纯化技术的快速发展和日臻成熟,使得在蛋白质组水平上大规模地研究蛋白质之间的相互作用成为可能。目前,人类蛋白质互作网络在细胞、组织、器官乃至整个个体水平的研究已经陆续展开。蛋白质互作网络中蛋白质数量也由少数几个向整个蛋白质组扩展。同时,功能、疾病、生态等相关的蛋白质互作网络研究也取得了一定的成果。然而,人类的蛋白质互作网络研究正面临着一些问题和挑战。本文综述了人类蛋白质互作网络的研究方法、研究进展以及面临的挑战,同时指出了人类蛋白质互作网络研究的方向和目标。     

Analysing protein-protein interactions with the yeast two-hybrid system

Plant research is moving into the post-genomic era. Proteomic-based strategies are now being developed to study functional aspects of the genes predicted from the various genome-sequencing initiatives. All biological processes depend on interactions formed between proteins and the mapping of such interactions on a global scale is providing interesting functional insights. One of the techniques that has proved itself invaluable in the mapping of protein-protein interactions is the yeast two-hybrid system. This system is a sensitive molecular genetic approach for studying protein-protein interactions in vivo. In this review we will introduce the yeast two-hybrid system, discuss modifications of the system that may be of interest to the plant science community and suggest potential applications of the technology.     

Analysing six types of protein-protein interfaces

Non-covalent residue side-chain interactions occur in many different types of proteins and facilitate many biological functions. Are these differences manifested in the sequence compositions and/or the residue-residue contact preferences of the interfaces? Previous studies analysed small data sets and gave contradictory answers. Here, we introduced a new data-mining method that yielded the largest high-resolution data set of interactions analysed. We introduced an information theory-based analysis method. On the basis of sequence features, we were able to differentiate six types of protein interfaces, each corresponding to a different functional or structural association between residues. Particularly, we found significant differences in amino acid composition and residue-residue preferences between interactions of residues within the same structural domain and between different domains, between permanent and transient interfaces, and between interactions associating homo-oligomers and hetero-oligomers. The differences between the six types were so substantial that, using amino acid composition alone, we could predict statistically to which of the six types of interfaces a pool of 1000 residues belongs at 63-100% accuracy. All interfaces differed significantly from the background of all residues in SWISS-PROT, from the group of surface residues, and from internal residues that were not involved in non-trivial interactions. Overall, our results suggest that the interface type could be predicted from sequence and that interface-type specific mean-field potentials may be adequate for certain applications.     

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。     

Intercellular protein-protein interactions at synapses

Chemical synapses are asymmetric intercellular junctions through which neurons send nerve impulses to communicate with other neurons or excitable cells. The appropriate formation of synapses, both spatially and temporally, is essential for brain function and depends on the intercellular protein-protein interactions of cell adhesion molecules (CAMs) at synaptic clefts. The CAM proteins link pre- and post-synaptic sites, and play essential roles in promoting synapse formation and maturation, maintaining synapse number and type, accumulating neurotransmitter receptors and ion channels, controlling neuronal differentiation, and even regulating synaptic plasticity directly. Alteration of the interactions of CAMs leads to structural and functional impairments, which results in many neurological disorders, such as autism, Alzheimer’s disease and schizophrenia. Therefore, it is crucial to understand the functions of CAMs during development and in the mature neural system, as well as in the pathogenesis of some neurological disorders. Here, we review the function of the major classes of CAMs, and how dysfunction of CAMs relates to several neurological disorders.