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。     

Feature-based classification of native and non-native protein-protein interactions: Comparing supervised and semi-supervised learning approaches

Structural knowledge about protein-protein interactions can provide insights to the basic processes underlying cell function. Recent progress in experimental and computational structural biology has led to a rapid growth of experimentally resolved structures and computationally determined near-native models of protein-protein interactions. However, determining whether a protein-protein interaction is physiological or it is the artifact of an experimental or computational method remains a challenging problem. In this work, we have addressed two related problems. The first problem is distinguishing between the experimentally obtained physiological and crystal-packing protein-protein interactions. The second problem is concerned with the classification of near-native and inaccurate docking models. We first defined a universal set of interface features and employed a support vector machines (SVM)-based approach to classify the interactions for both problems, with the accuracy, precision, and recall for the first problem classifier reaching 93%. To improve the classification, we next developed a semi-supervised learning approach for the second problem, using transductive SVM (TSVM). We applied both classifiers to a commonly used protein docking benchmark of 124 complexes. We found that while we reached the classification accuracies of 78.9% for the SVM classifier and 80.3% for the TSVM classifier, improving protein-docking methods by model re-ranking remains a challenging problem.     

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.     

An evaluation of in vitro protein-protein interaction techniques: assessing contaminating background proteins

Determination of protein-protein interactions is an important component in assigning function and discerning the biological relevance of proteins within a broader cellular context. In vitro protein-protein interaction methodologies, including affinity chromatography, coimmunoprecipitation, and newer approaches such as protein chip arrays, hold much promise in the detection of protein interactions, particularly in well-characterized organisms with sequenced genomes. However, each of these approaches attracts certain background proteins that can thwart detection and identification of true interactors. In addition, recombinant proteins expressed in Escherichia coli are also extensively used to assess protein-protein interactions, and background proteins in these isolates can thus contaminate interaction studies. Rigorous validation of a true interaction thus requires not only that an interaction be found by alternate techniques, but more importantly that researchers be aware of and control for matrix/support dependence. Here, we evaluate these methods for proteins interacting with DmsD (an E. coli redox enzyme maturation protein chaperone), in vitro, using E. coli subcellular fractions as prey sources. We compare and contrast the various in vitro interaction methods to identify some of the background proteins and protein profiles that are inherent to each of the methods in an E. coli system.     

Protein-protein interactions between two-component system transmitter and receiver domains of Myxococcus xanthus

We present a novel dataset assessing the specificity of protein-protein interactions between 69 transmitter and receiver domains from two-component system (TCS)-signalling pathways. TCS require a conserved protein-protein interaction between partner transmitter and receiver domains for signal transduction. The complex prokaryote Myxococcus xanthus possesses an unusually large number of TCS genes, many of which have no obvious interaction partners. Interactions between TCS domains of M. xanthus were assessed using a yeast two-hybrid assay, in which domains were expressed as both bait and prey translational fusions. LacZ production was monitored as an indicator of protein-protein interaction, and the strength of interactions classified as weak, medium or strong. Two-hundred and fifty-five transmitter-receiver domain interactions were observed (46 strong), allowing identification of potential signalling partners for individual M. xanthus TCS proteins. In addition, the dataset provides interesting 'meta' information. For instance, many strong interactions were identified between different transmitter domain pairs (34) and receiver domain pairs (23), suggesting a surprisingly large degree of heterodimerisation of these domains. Proteins in our dataset that exhibited similar 'profiles' of interactions, often shared a similar biological function, suggesting that interaction profiles can provide information on biological function, even considering sets of homologous domains.     

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.     

Specificity of molecular interactions in transient protein-protein interaction interfaces

In this study, we investigate what types of interactions are specific to their biological function, and what types of interactions are persistent regardless of their functional category in transient protein-protein heterocomplexes. This is the first approach to analyze protein-protein interfaces systematically at the molecular interaction level in the context of protein functions. We perform systematic analysis at the molecular interaction level using classification and feature subset selection technique prevalent in the field of pattern recognition. To represent the physicochemical properties of protein-protein interfaces, we design 18 molecular interaction types using canonical and noncanonical interactions. Then, we construct input vector using the frequency of each interaction type in protein-protein interface. We analyze the 131 interfaces of transient protein-protein heterocomplexes in PDB: 33 protease-inhibitors, 52 antibody-antigens, 46 signaling proteins including 4 cyclin dependent kinase and 26 G-protein. Using kNN classification and feature subset selection technique, we show that there are specific interaction types based on their functional category, and such interaction types are conserved through the common binding mechanism, rather than through the sequence or structure conservation. The extracted interaction types are C(alpha)-- H...O==C interaction, cation...anion interaction, amine...amine interaction, and amine...cation interaction. With these four interaction types, we achieve the classification success rate up to 83.2% with leave-one-out cross-validation at k = 15. Of these four interaction types, C(alpha)--H...O==C shows binding specificity for protease-inhibitor complexes, while cation-anion interaction is predominant in signaling complexes. The amine ... amine and amine...cation interaction give a minor contribution to the classification accuracy. When combined with these two interactions, they increase the accuracy by 3.8%. In the case of antibody-antigen complexes, the sign is somewhat ambiguous. From the evolutionary perspective, while protease-inhibitors and sig-naling proteins have optimized their interfaces to suit their biological functions, antibody-antigen interactions are the happenstance, implying that antibody-antigen complexes do not show distinctive interaction types. Persistent interaction types such as pi...pi, amide-carbonyl, and hydroxyl-carbonyl interaction, are also investigated. Analyzing the structural orientations of the pi...pi stacking interactions, we find that herringbone shape is a major configuration in transient protein-protein interfaces. This result is different from that of protein core, where parallel-displaced configurations are the major configuration. We also analyze overall trend of amide-carbonyl and hydroxyl-carbonyl interactions. It is noticeable that nearly 82% of the interfaces have at least one hydroxyl-carbonyl interactions.     

Molecular docking analysis of RN18 and VEC5 in A3G-Vif inhibition

The HIV-1 protein Vif is essential for in vivo viral replication that targets the human DNA-editing enzyme, APOBEC3G (A3G), which inhibits replication of retroviruses. The Vif-A3G interactions are believed to be important targets for antiviral drug development. Since the interactions of A3G and Vif evade the ubiquitination pathways in human host, the viral replication precedes which otherwise spreads infection. In this study, two potent Vif inhibitors RN 18 and VEC5 have been evaluated for their inhibitory potential employing ligand receptor and protein-protein interactions studies. VEC 5 showed better interaction with Vif than RN18. Predicted data show that VEC5 bound Vif and RN18 bound Vif showed diminished interaction to A3G compared to inhibitor unbound Vif. However, this should be further validated using in vitro studies.     

Methods for mapping of interaction networks involving membrane proteins

Nearly one-third of all genes in various organisms encode membrane-associated proteins that participate in numerous protein-protein interactions important to the processes of life. However, membrane protein interactions pose significant challenges due to the need to solubilize membranes without disrupting protein-protein interactions. Traditionally, analysis of isolated protein complexes by high-resolution 2D gel electrophoresis has been the main method used to obtain an overall picture of proteome constituents and interactions. However, this method is time consuming, labor intensive, detects only abundant proteins and is limited with respect to the coverage required to elucidate large interaction networks. In this review, we discuss the application of various methods to elucidate interactions involving membrane proteins. These techniques include methods for the direct isolation of single complexes or interactors as well as methods for characterization of entire subcellular and cellular interactomes.     

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.     

Comparison of protein-protein interactions in serine protease-inhibitor and antibody-antigen complexes: implications for the protein docking problem

The protein-protein interaction energy of 12 nonhomologous serine protease-inhibitor and 15 antibody-antigen complexes is calculated using a molecular mechanics formalism and dissected in terms of the main-chain vs. side-chain contribution, nonrotameric side-chain contributions, and amino acid residue type involvement in the interface interaction. There are major differences in the interactions of the two types of protein-protein complex. Protease-inhibitor complexes interact predominantly through a main-chain-main-chain mechanism while antibody-antigen complexes interact predominantly through a side-chain-side-chain or a side-chain-main-chain mechanism. However, there is no simple correlation between the main-chain-main-chain interaction energy and the percentage of main-chain surface area buried on binding. The interaction energy is equally effected by the presence of nonrotameric side-chain conformations, which constitute approximately 20% of the interaction energy. The ability to reproduce the interface interaction energy of the crystal structure if original side-chain conformations are removed from the calculation is much greater in the protease-inhibitor complexes than the antibody-antigen complexes. The success of a rotameric model for protein-protein docking appears dependent on the extent of the main-chain-main-chain contribution to binding. Analysis of (1) residue type and (2) residue pair interactions at the interface show that antibody-antigen interactions are very restricted with over 70% of the antibody energy attributable to just six residue types (Tyr > Asp > Asn > Ser > Glu > Trp) in agreement with previous studies on residue propensity. However, it is found here that 50% of the antigen energy is attributable to just four residue types (Arg = Lys > Asn > Asp). On average just 12 residue pair interactions (6%) contribute over 40% of the favorable interaction energy in the antibody-antigen complexes, with charge-charge and charge/polar-tyrosine interactions being prominent. In contrast protease inhibitors use a diverse set of residue types and residue pair interactions.     

Extent of protein-protein interactions and quasi-equivalence in viral capsids

Viral capsids are composed of multiple copies of one or a few gene products that self-assemble on their own or in the presence of the viral genome and/or auxiliary proteins into closed shells (capsids). We have analyzed 75 high-resolution virus capsid structures by calculating the average fraction of the solvent-accessible surface area of the coat protein subunits buried in the viral capsids. This fraction ranges from 0 to 1 and represents a normalized protein-protein interaction (PPI) index and is a measure of the extent of protein-protein interactions. The PPI indices were used to compare the extent of association of subunits among different capsids. We further examined the variation of the PPI indices as a function of the molecular weight of the coat protein subunit and the capsid diameter. Our results suggest that the PPI indices in T=1 and pseudo-T=3 capsids vary linearly with the molecular weight of the subunit and capsid size. This is in contrast to quasi-equivalent capsids with T>or=3, where the extent of protein-protein interactions is relatively independent of the subunit and capsid sizes. The striking outcome of this analysis is the distinctive clustering of the "T=2" capsids, which are distinguished by higher subunit molecular weights and a much lower degree of protein-protein interactions. Furthermore, the calculated residual (R(sym)) of the fraction buried surface areas of the structurally unique subunits in capsids with T>1 was used to calculate the quasi-equivalence of different subunit environments.     

基于质谱技术的蛋白质互作研究进展

蛋白质作为生命活动的执行者,其功能往往体现在与其他蛋白质的相互作用中,研究蛋白-蛋白相互作用对于人们深入了解和预防传染病、靶向治疗多基因疾病、阐明蛋白质的分子作用机制及各种复杂的生命现象具有重要意义。目前,有多种技术被用来研究蛋白间的相互作用,研究难点在于实时捕获瞬时或弱蛋白质间的相互作用,质谱技术（mass spectrometry, MS）可在某种程度上解决该难点。由于质谱技术可研究简单的蛋白质复合物再到大规模的蛋白质组实验,基于质谱技术研究蛋白质间相互作用被越来越多地应用于科学研究中。综述了蛋白质间相互作用检测方法的研究进展,重点介绍了氢氘交换质谱法和化学交联质谱法研究蛋白质间相互作用的优缺点及其应用,最后对基于质谱技术研究蛋白质间相互作用进行了总结与展望,以期为深入开展相关研究提供借鉴。     

A comparison of SCOP and CATH with respect to domain-domain interactions

The analysis and prediction of protein-protein interaction sites from structural data are restricted by the limited availability of structural complexes that represent the complete protein-protein interaction space. The domain classification schemes CATH and SCOP are normally used independently in the analysis and prediction of protein domain-domain interactions. In this article, the effect of different domain classification schemes on the number and type of domain-domain interactions observed in structural data is systematically evaluated for the SCOP and CATH hierarchies. Although there is a large overlap in domain assignments between SCOP and CATH, 23.6% of CATH interfaces had no SCOP equivalent and 37.3% of SCOP interfaces had no CATH equivalent in a nonredundant set. Therefore, combining both classifications gives an increase of between 23.6 and 37.3% in domain-domain interfaces. It is suggested that if possible, both domain classification schemes should be used together, but if only one is selected, SCOP provides better coverage than CATH. Employing both SCOP and CATH reduces the false negative rate of predictive methods, which employ homology matching to structural data to predict protein-protein interaction by an estimated 6.5%.     

Protein complementation as tool for studying protein-protein interactions in living cells

The association and dissociation of protein-protein complexes play an important role in various processes in living cells. The disruption of protein-protein interactions is observed in various pathologies. The study of the nature of these interactions will contribute to a better understanding of the molecular basis of the pathogenesis of the disease and the development of new approaches to therapy. Now there is a set of methods that allow one to reveal and analyze the interaction of proteins in vitro. However, more accurate data can be obtained by studying protein-protein interactions in vivo. One of a few prospective methods is based on the effect of the complementation of fragments of reporter proteins. These reporter systems are based on the change in the fluorescent properties or enzymatic activity of the proteins that can be measured using colorimetric, fluorescent, or other substrates. The principle of the complementation is widely used to analyze protein interactions, to determine of order of interaction of protein partners in different signaling pathways, as well as in high-performance screening studies for detecting and mapping previously unknown protein-protein interactions. The possibilities of existing complementation reporter systems allow one to solve problems that are far beyond the simple registration of the interactions of two or more proteins.     

Recent advances in charting protein-protein interaction: mass spectrometry-based approaches

Cellular functions are the result of the coordinated action of groups of proteins interacting in molecular assemblies or pathways. The systematic and unbiased charting of protein-protein networks in a variety of organisms has become an important challenge in systems biology. These protein-protein interaction networks contribute comprehensive cartographies of key pathways or biological processes relevant to health or disease by providing a molecular frame for the interpretation of genetic links. At a structural level protein-protein networks enabled the identification of the sequences, motifs and structural folds involved in the process of molecular recognition. A rapidly growing choice of technologies is available for the global charting of protein-protein interactions. In this review, we focus on recent developments in a suite of methods that enable the purification of protein complexes under native conditions and, in conjunction with protein mass spectrometry, identification of their constituents.     

A visual chip-based coimmunoprecipitation technique for analysis of protein-protein interactions

Here we report a visual chip-based coimmunoprecipitation (vChip-coIP) platform for analysis of protein-protein interactions (PPIs) by combining advantages of an antibody microarray, traditional coIP, and a silver enhancement detection method. The chip was fabricated by spotting anti-Flag antibody on aldehyde-modified slides, and the resulting platform could assay immunoprecipitate from a small amount of crude cell lysates containing Flag-bait and Myc-prey. The interaction signals are visible using biotinylated anti-Myc antibody and colloidal gold-labeled streptavidin followed by a silver enhancement detection method. It is shown that vChip-coIP is a simple, cost-effective, and highly efficient platform for the comprehensive study of PPIs.     

Protein-protein interactions as a target for drugs in proteomics

Protein-protein interactions play a central role in numerous processes in the cell and are one of the main fields of functional proteomics. This review highlights the methods of bioinformatics and functional proteomics of protein-protein interaction investigation. The structures and properties of contact surfaces, forces involved in protein-protein interactions, kinetic and thermodynamic parameters of these reactions were considered. The properties of protein contact surfaces depend on their functions. The contact surfaces of permanent complexes resemble domain contacts or the protein core and it is reasonable to consider such complex formation as a continuation of protein folding. Characteristics of contact surfaces of temporary protein complexes share some similarities with active sites of enzymes. The contact surfaces of the temporary protein complexes have unique structure and properties and they are more conservative in comparison with active site of enzymes. So they represent prospective targets for a new generation of drugs. During the last decade, numerous investigations were undertaken to find or design small molecules that block protein dimerization or protein(peptide)-receptor interaction, or, on the contrary, to induce protein dimerization.     

Reverse MAPPIT: screening for protein-protein interaction modifiers in mammalian cells

Interactions between proteins are at the heart of the cellular machinery. It is therefore not surprising that altered interaction profiles caused by aberrant protein expression patterns or by the presence of mutations can trigger cellular dysfunction, eventually leading to disease. Moreover, many viral and bacterial pathogens rely on protein-protein interactions to exert their damaging effects. Interfering with such interactions is an obvious pharmaceutical goal, but detailed insights into the protein binding properties as well as efficient screening platforms are needed. In this report, we describe a cytokine receptor-based assay with a positive readout to screen for disrupters of designated protein-protein interactions in intact mammalian cells and evaluate this concept using polypeptides as well as small organic molecules. These reverse mammalian protein-protein interaction trap (MAPPIT) screens were developed to monitor interactions between the erythropoietin receptor (EpoR) and suppressors of cytokine signaling (SOCS) proteins, between FKBP12 and ALK4, and between MDM2 and p53.