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。     

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.     

Geometrical and electro-static determinants of protein-protein interactions

Protein-protein interactions (PPI) are pivotal to the numerous processes in the cell. Therefore, it is of interest to document the analysis of these interactions in terms of binding sites, topology of the interacting structures and physiochemical properties of interacting interfaces and the of forces interactions. The interaction interface of obligatory protein-protein complexes differs from that of the transient interactions. We have created a large database of protein-protein interactions containing over100 thousand interfaces. The structural redundancy was eliminated to obtain a non-redundant database of over 2,265 interaction interfaces. Therefore, it is of interest to document the analysis of these interactions in terms of binding sites, topology of the interacting structures and physiochemical properties of interacting interfaces and the offorces interactions. The residue interaction propensity and all of the rest of the parametric scores converged to a statistical indistinguishable common sub-range and followed the similar distribution trends for all three classes of sequence-based classifications PPInS. This indicates that the principles of molecular recognition are dependent on the preciseness of the fit in the interaction interfaces. Thus, it reinforces the importance of geometrical and electrostatic complementarity as the main determinants for PPIs.     

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。     

A dissection of specific and non-specific protein-protein interfaces

We compare the geometric and physical-chemical properties of interfaces involved in specific and non-specific protein-protein interactions in crystal structures reported in the Protein Data Bank. Specific interactions are illustrated by 70 protein-protein complexes and by subunit contacts in 122 homodimeric proteins; non-specific interactions are illustrated by 188 pairs of monomeric proteins making crystal-packing contacts selected to bury more than 800 A2 of protein surface. A majority of these pairs have 2-fold symmetry and form "crystal dimers" that cannot be distinguished from real dimers on the basis of the interface size or symmetry. The chemical and amino acid compositions of the large crystal-packing interfaces resemble the protein solvent-accessible surface. These interfaces are less hydrophobic than in homodimers and contain much fewer fully buried atoms. We develop a residue propensity score and a hydrophobic interaction score to assess preferences seen in the chemical and amino acid compositions of the different types of interfaces, and we derive indexes to evaluate the atomic packing, which we find to be less compact at non-specific than at specific interfaces. We test the capacity of these parameters to identify homodimeric proteins in crystal structures, and show that a simple combination of the non-polar interface area and the fraction of buried interface atoms assigns the quaternary structure of 88% of the homodimers and 77% of the monomers in our data set correctly. These success rates increase to 93-95% when the residue propensity score of the interfaces is taken into consideration.     

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 interactions 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 elegans, 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 under the receiver operating characteristic (ROC) curve. When proteins containing one or two signatures were removed, the sensitivity of the predicted PPI pairs increased 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 average 4 times better than those predicted by either phylogenetic profiling or gene expression profiling.     

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.     

Architectures and functional coverage of protein-protein interfaces

The diverse range of cellular functions is performed by a limited number of protein folds existing in nature. One may similarly expect that cellular functional diversity would be covered by a limited number of protein-protein interface architectures. Here, we present 8205 interface clusters, each representing a unique interface architecture. This data set of protein-protein interfaces is analyzed and compared with older data sets. We observe that the number of both biological and crystal interfaces increases significantly compared to the number of Protein Data Bank entries. Furthermore, we find that the number of distinct interface architectures grows at a much faster rate than the number of folds and is yet to level off. We further analyze the growth trend of the functional coverage by constructing functional interaction networks from interfaces. The functional coverage is also found to steadily increase. Interestingly, we also observe that despite the diversity of interface architectures, some are more favorable and frequently used, and of particular interest, are the ones that are also preferred in single chains.     

Structural segments and residue propensities in protein-RNA interfaces: comparison with protein-protein and protein-DNA complexes

The interface of a protein molecule that is involved in binding another protein, DNA or RNA has been characterized in terms of the number of unique secondary structural segments (SSSs), made up of stretches of helix, strand and non-regular (NR) regions. On average 10-11 segments define the protein interface in protein-protein (PP) and protein-DNA (PD) complexes, while the number is higher (14) for protein-RNA (PR) complexes. While the length of helical segments in PP interaction increases with the interface area, this is not the case in PD and PR complexes. The propensities of residues to occur in the three types of secondary structural elements (SSEs) in the interface relative to the corresponding elements in the protein tertiary structures have been calculated. Arg, Lys, Asn, Tyr, His and Gln are preferred residues in PR complexes; in addition, Ser and Thr are also favoured in PD interfaces.     

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.     

基于蛋白质互作评价差异表达基因的重复性

利用高通量基因表达谱数据可以识别在肿瘤与正常组织中差异表达的基因,为研究癌机理提供重要的线索。目前,在研究同种癌型的不同实验中发现的差异表达基因的交叠比例很低。这种高通量基因表达谱数据低重复性的现象严重制约了基因芯片在癌症研究中的应用。然而,已有研究表明从研究同种癌型的不同实验数据中得到的不交叠的差异表达基因倾向于扰动相同的功能。因此,在评价差异表达基因重复性时,应考虑其在生物学功能上的一致性。本文结合基因共表达和蛋白质互作关系,设计了功能重复性指标来评价差异表达基因列表的可重复性。通过分析两套卵巢癌数据,发现对同种癌型得到的差异表达基因具有很高的功能一致性(p<0.0001)。结果表明,在功能水平上评估差异表达基因的一致性具有合理性。     

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.     

Similarity between protein-protein and protein-carbohydrate interactions, revealed by two crystal structures of lectins from the roots of pokeweed

The roots of pokeweed (Phytolacca americana) are known to contain the lectins designated PL-A, PL-B, PL-C, PL-D1, and PL-D2. Of these lectins, the crystal structures of two PLs, the ligand-free PL-C and the complex of PL-D2 with tri-N-acetylchitotriose, have been determined at 1.8A resolution. The polypeptide chains of PL-C and PL-D2 form three and two repetitive chitin-binding domains, respectively. In the crystal structure of the PL-D2 complex, one trisaccharide molecule is shared mainly between two neighboring molecules related to each other by a crystallographic 2(1)-screw axis, and infinite helical chains of complexed molecules are generated by the sharing of ligand molecules. The crystal structure of PL-C reveals that the molecule is a dimer of two identical subunits, whose polypeptide chains are located in a head-to-tail fashion by a molecular 2-fold axis. Three putative carbohydrate-binding sites in each subunit are located in the dimer interface. The dimerization of PL-C is performed through the hydrophobic interactions between the carbohydrate-binding sites of the opposite domains in the dimer, leading to a distinct dimerization mode from that of wheat-germ agglutinin. Three aromatic residues in each carbohydrate-binding site of PL-C are involved in the dimerization. These residues correspond to the residues that interact mainly with the trisaccharide in the PL-D2 complex and appear to mimic the saccharide residues in the complex. Consequently, the present structure of the PL-C dimer has no room for accommodating carbohydrate. The quaternary structure of PL-C formed through these putative carbohydrate-binding residues may lead to the lack of hemagglutinating activity.     

Transcription factors do it together: the hows and whys of studying protein-protein interactions

Protein–protein interactions are intrinsic to virtually every cellular process. Recent breakthroughs in techniques to study protein-interaction and the availability of fully sequenced plant genomes have attracted many plant scientists to undertake the first steps in the field of protein interactions. High-throughput screening systems allow the discovery of protein functions. Even without performing laborious functional assays, in planta functional homologues and redundant proteins can be accurately predicted based on protein-interaction maps. Therefore, protein–protein-interaction screenings are an essential supplement to the current functional-genomics toolbox.     

Structure and thermodynamics of transient protein-protein complexes by chemometric decomposition of SAXS datasets

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in silico identification of protein-protein interactions in Silkworm,Bombyx mori

The Domesticated silkworm, Bombyx mori, an economically important insect has been used as a lepidopteran molecular model next only to Drosophila. Compared to the genomic information in silkworm, the protein-protein interaction data are limited. Therefore experimentally identified PPI maps from five model organisms such as E.coli, C.elegans, D.melanogaster, H. sapiens, S. cerevisiae were used to infer the PPI network of silkworm using the well-recognized Interlog based method. Among the 14623 silkworm proteins, 7736 protein-protein interaction pairs were predicted which include 2700 unique proteins of the silkworms. Using the iPfam interaction domains and the gene expression data, these predictions were validated. In that 625 PPI pairs of predicted network were associated with the iPfam domain-domain interactions and the random network has average of 9. In the gene expression method, the average PCC value of the predicted network and random network was 0.29 and 0.23100±0.00042 respectively. It reveals that the predicted PPI networks of silkworm are highly significant and reliable. This is the first PPI network for the silkworm which will provide a framework for deciphering the cellular processes governing key metabolic pathways in the silkworm, Bombyx mori and available at SilkPPI (http://210.212.197.30/SilkPPI/).     

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.     

Identification of transient hub proteins and the possible structural basis for their multiple interactions

Proteins that can interact with multiple partners play central roles in the network of protein-protein interactions. They are called hub proteins, and recently it was suggested that an abundance of intrinsically disordered regions on their surfaces facilitates their binding to multiple partners. However, in those studies, the hub proteins were identified as proteins with multiple partners, regardless of whether the interactions were transient or permanent. As a result, a certain number of hub proteins are subunits of stable multi-subunit proteins, such as supramolecules. It is well known that stable complexes and transient complexes have different structural features, and thus the statistics based on the current definition of hub proteins will hide the true nature of hub proteins. Therefore, in this paper, we first describe a new approach to identify proteins with multiple partners dynamically, using the Protein Data Bank, and then we performed statistical analyses of the structural features of these proteins. We refer to the proteins as transient hub proteins or sociable proteins, to clarify the difference with hub proteins. As a result, we found that the main difference between sociable and nonsociable proteins is not the abundance of disordered regions, in contrast to the previous studies, but rather the structural flexibility of the entire protein. We also found greater predominance of charged and polar residues in sociable proteins than previously reported.