邻近标记在蛋白质组学中的发展及应用

人体内各种复杂的生命活动离不开蛋白质之间的相互作用。这种相互作用具有瞬时性和结合力弱等特点,并受到多种动态调节,特别是蛋白质翻译后修饰（post-translation modifications, PTM）。传统的亲和质谱检测方法存在蛋白纯化的局限性,在高效检测到动态变化方面存在不足。邻近标记是一种能够给与靶蛋白质瞬时靠近,或者互作（邻近）的蛋白质加上生物素的技术,它与质谱检测技术的联合使用能检测细胞过程中弱的、瞬时的蛋白质相互作用,有效解决上述问题。本文综述了基于生物素的邻近标记方法的发展现状,从依赖于融合序列的生物素标记开始,依次介绍有关生物素连接酶、过氧化物酶及其进化后的2代标记方法等经典生物素标记的方法和原理,比较各个方法间的差异和优缺点;也列举了一些近年来新出现的标记方法,如将生物素连接酶进行拆分、鉴定蛋白质在不同复合物中功能的方法、抗体靶向的标记方法,以及其他来源的生物素连接酶突变体,例如枯草芽孢杆菌（Bacillus subtilis）的C端氨基酸突变的生物素连接酶,能够应用在苍蝇和蠕虫中的生物素连接酶突变体。本文对这些方法进行归纳总结,旨在为初步接触该领域的科研工作者提供参考,同时也希望能够提供一些新的思路,推动蛋白质相互作用组学的发展。     

邻近标记在蛋白质组学中的发展及应用

人体内各种复杂的生命活动离不开蛋白质之间的相互作用。这种相互作用具有瞬时性和结合力弱等特点,并受到多种动态调节,特别是蛋白质翻译后修饰（post-translation modifications, PTM）。传统的亲和质谱检测方法存在蛋白纯化的局限性,在高效检测到动态变化方面存在不足。邻近标记是一种能够给与靶蛋白质瞬时靠近,或者互作（邻近）的蛋白质加上生物素的技术,它与质谱检测技术的联合使用能检测细胞过程中弱的、瞬时的蛋白质相互作用,有效解决上述问题。本文综述了基于生物素的邻近标记方法的发展现状,从依赖于融合序列的生物素标记开始,依次介绍有关生物素连接酶、过氧化物酶及其进化后的2代标记方法等经典生物素标记的方法和原理,比较各个方法间的差异和优缺点;也列举了一些近年来新出现的标记方法,如将生物素连接酶进行拆分、鉴定蛋白质在不同复合物中功能的方法、抗体靶向的标记方法,以及其他来源的生物素连接酶突变体,例如枯草芽孢杆菌（Bacillus subtilis）的C端氨基酸突变的生物素连接酶,能够应用在苍蝇和蠕虫中的生物素连接酶突变体。本文对这些方法进行归纳总结,旨在为初步接触该领域的科研工作者提供参考,同时也希望能够提供一些新的思路,推动蛋白质相互作用组学的发展。     

基于TurboID的植物蛋白邻近标记实验方法

邻近标记作为近些年发展起来的一项检测活细胞内蛋白互作关系和亚细胞结构蛋白组的新型技术,已成功应用于多种动植物体系的研究。该技术通过给诱饵蛋白融合一个具有特定催化连接活性的酶,在酶的催化作用下将小分子底物(如生物素)共价连接到酶邻近的内源蛋白,通过富集和分析被标记的蛋白可获得与诱饵互作的蛋白组。经定向进化产生的生物素连接...     

A fluorescent reporter for mapping cellular protein‐protein interactions in time and space

We introduce a fluorescent reporter for monitoring protein–protein interactions in living cells. The method is based on the Split‐Ubiquitin method and uses the ratio of two auto‐fluorescent reporter proteins as signal for interaction (SPLIFF). The mating of two haploid yeast cells initiates the analysis and the interactions are followed online by two‐channel time‐lapse microscopy of the diploid cells during their first cell cycle. Using this approach we could with high spatio‐temporal resolution visualize the differences between the interactions of the microtubule binding protein Stu2p with two of its binding partners, monitor the transient association of a Ran‐GTPase with its receptors at the nuclear pore, and distinguish between protein interactions at the polar cortical domain at different phases of polar growth. These examples further demonstrate that protein–protein interactions identified from large‐scale screens can be effectively followed up by high‐resolution single‐cell analysis.     

The cysteine‐free single mutant C32S of APEX2 is a highly expressed and active fusion tag for proximity labeling applications

APEX2, an engineered ascorbate peroxidase for high activity, is a powerful tool for proximity labeling applications. Owing to its lack of disulfides and the calcium‐independent activity, APEX2 can be applied intracellularly for targeted electron microscopy imaging or interactome mapping when fusing to a protein of interest. However, APEX2 fusion is often deleterious to the protein expression, which seriously hampers its wide utility. This problem is especially compelling when APEX2 is fused to structurally delicate proteins, such as multi‐pass membrane proteins. In this study, we found that a cysteine‐free single mutant C32S of APEX2 dramatically improved the expression of fusion proteins in mammalian cells without compromising the enzyme activity. We fused APEX2 and APEX2^C32S to four multi‐transmembrane solute carriers (SLCs), SLC1A5, SLC6A5, SLC6A14, and SLC7A1, and compared their expressions in stable HEK293T cell lines. Except the SLC6A5 fusions expressing at decent levels for both APEX2 (70%) and APEX2^C32S (73%), other three SLC proteins showed significantly better expression when fusing to APEX2^C32S (69 ± 13%) than APEX2 (29 ± 15%). Immunofluorescence and western blot experiments showed correct plasma membrane localization and strong proximity labeling efficiency in all four SLC‐APEX2^C32S cells. Enzyme kinetic experiments revealed that APEX2 and APEX2^C32S have comparable activities in terms of oxidizing guaiacol. Overall, we believe APEX2^C32S is a superior fusion tag to APEX2 for proximity labeling applications, especially when mismatched disulfide bonding or poor expression is a concern.     

Characterization of protein–glycolipid recognition at the membrane bilayer

A growing number of important molecular recognition events are being shown to involve the interactions between proteins and glycolipids. Glycolipids are molecules in which one or more monosaccharides are glycosidically linked to a lipid moiety. The lipid moiety is generally buried in the cell membrane or other bilayer, leaving the oligosaccharide moiety exposed but in close proximity to the bilayer surface. This presents a unique environment for protein–carbohydrate interactions, and studies to determine the influence of the bilayer on these phenomena are in their infancy. One important property of the bilayer is the ability to orient and cluster glycolipid species, as strong interactions in biological systems are often achieved through multivalency arising from the simultaneous association of two or more proteins and receptors. This is especially true of protein–carbohydrate binding because of the unusually low affinities that characterize the monovalent interactions. More recent studies have also shown that the composition of the lipid bilayer is a critical parameter in protein–glycolipid recognition. The fluidity of the bilayer allows for correct geometric positioning of the oligosaccharide head group relative to the binding sites on the protein. In addition, there are activity‐based and structural data demonstrating the impact of the bilayer microenvironment on the modulation of oligosaccharide presentation. The use of model membranes in biosensor‐based methods has supplied decisive evidence of the importance of the membrane in receptor presentation. These data can be correlated with three‐dimensional structural information from X‐ray <?tw=98%>crystallography, NMR, and molecular mechanics to provide insight into specific protein–carbohydrate inter‐­actions at the bilayer. Copyright © 1999 National Research Council Canada and John Wiley & Sons, Ltd.     

An engineered ultra‐high affinity Fab‐Protein G pair enables a modular antibody platform with multifunctional capability

Engineered recombinant antibody‐based reagents are rapidly supplanting traditionally derived antibodies in many cell biological applications. A particularly powerful aspect of these engineered reagents is that other modules having myriad functions can be attached to them either chemically or through molecular fusions. However, these processes can be cumbersome and do not lend themselves to high throughput applications. Consequently, we have endeavored to develop a platform that can introduce multiple functionalities into a class of Fab‐based affinity reagents in a “plug and play” fashion. This platform exploits the ultra‐tight binding interaction between affinity matured variants of a Fab scaffold (Fab^S) and a domain of an immunoglobulin binding protein, protein G (GA1). GA1 is easily genetically manipulatable facilitating the ability to link these modules together like beads on a string with adjustable spacing to produce multivalent and bi‐specific entities. GA1 can also be fused to other proteins or be chemically modified to engage other types of functional components. To demonstrate the utility for the Fab‐GA1 platform, we applied it to a detection proximity assay based on the β‐lactamase (BL) split enzyme system. We also show the bi‐specific capabilities of the module by using it in context of a Bi‐specific T‐cell engager (BiTE), which is a therapeutic assemblage that induces cell killing by crosslinking T‐cells to cancer cells. We show that GA1‐Fab modules are easily engineered into potent cell‐killing BiTE‐like assemblages and have the advantage of interchanging Fabs directed against different cell surface cancer‐related targets in a plug and play fashion.     

Mass spectrometry‐based protein–protein interaction networks for the study of human diseases

A better understanding of the molecular mechanisms underlying disease is key for expediting the development of novel therapeutic interventions. Disease mechanisms are often mediated by interactions between proteins. Insights into the physical rewiring of protein–protein interactions in response to mutations, pathological conditions, or pathogen infection can advance our understanding of disease etiology, progression, and pathogenesis and can lead to the identification of potential druggable targets. Advances in quantitative mass spectrometry (MS)‐based approaches have allowed unbiased mapping of these disease‐mediated changes in protein–protein interactions on a global scale. Here, we review MS techniques that have been instrumental for the identification of protein–protein interactions at a system‐level, and we discuss the challenges associated with these methodologies as well as novel MS advancements that aim to address these challenges. An overview of examples from diverse disease contexts illustrates the potential of MS‐based protein–protein interaction mapping approaches for revealing disease mechanisms, pinpointing new therapeutic targets, and eventually moving toward personalized applications.     

Tissue specificity and the human protein interaction network

A protein interaction network describes a set of physical associations that can occur between proteins. However, within any particular cell or tissue only a subset of proteins is expressed and so only a subset of interactions can occur. Integrating interaction and expression data, we analyze here this interplay between protein expression and physical interactions in humans. Proteins only expressed in restricted cell types, like recently evolved proteins, make few physical interactions. Most tissue‐specific proteins do, however, bind to universally expressed proteins, and so can function by recruiting or modifying core cellular processes. Conversely, most ‘housekeeping’ proteins that are expressed in all cells also make highly tissue‐specific protein interactions. These results suggest a model for the evolution of tissue‐specific biology, and show that most, and possibly all, ‘housekeeping’ proteins actually have important tissue‐specific molecular interactions.     

A method for reverse interactome analysis: High‐resolution mapping of interdomain interaction network in Dam1 complex and its specific disorganization based on the interaction domain expression

An experimental methodology that facilitates functional analysis of numerous protein–protein interactions, which have been found in genome‐wide interactome researches, has long been awaited. We propose herein an antagonistic inhibition‐based approach. The antagonizing polypeptide is generated in the course of interaction domain mapping based on yeast 2‐hybrid (Y2H) screening coupled with in vitro convergence of the Y2H‐selected fragments, which is performed in a formatted procedure. Using the coupled methodology, we first performed a high‐resolution mapping of an interdomain interaction network within budding yeast's Dam1 complex. Dam1 complex is a kinetochore protein complex composed of 10 essential subunits including Spc34p and Spc19p. The high‐resolution mapping revealed the overall network structure within the complex for the first time: Dam1 components form into two separated subnetworks on N‐terminal scaffolding domains of Spc34p and Spc19p, and the coiled‐coil interaction in their C‐terminal domains connects the subnetworks. Secondly, we show that the domain fragments converged in the high‐resolution mapping acted as potent inhibitors for the endogenous interactions when episomally overexpressed. The in vivo Dam1 interaction targeting with the fragments conferred a similar phenotype on the host cells; a critical and irreversible damage, which was accompanied with disturbed budding and chromosome mis‐segregation as a result of disorganized spindle. These phenotypes were strongly related to the cellular function of the Dam1 complex. The results and approach we demonstrated herein not only shed light on the Dam1 molecular architecture but also pave the road to reverse‐interactome analysis and discoveries of novel drugs that target disease‐related protein–protein interactions. © 2010 American Institute of Chemical Engineers Biotechnol. Prog., 2010     

Constraints imposed by non‐functional protein–protein interactions on gene expression and proteome size

Crowded intracellular environments present a challenge for proteins to form functional specific complexes while reducing non‐functional interactions with promiscuous non‐functional partners. Here we show how the need to minimize the waste of resources to non‐functional interactions limits the proteome diversity and the average concentration of co‐expressed and co‐localized proteins. Using the results of high‐throughput Yeast 2‐Hybrid experiments, we estimate the characteristic strength of non‐functional protein–protein interactions. By combining these data with the strengths of specific interactions, we assess the fraction of time proteins spend tied up in non‐functional interactions as a function of their overall concentration. This allows us to sketch the phase diagram for baker's yeast cells using the experimentally measured concentrations and subcellular localization of their proteins. The positions of yeast compartments on the phase diagram are consistent with our hypothesis that the yeast proteome has evolved to operate closely to the upper limit of its size, whereas keeping individual protein concentrations sufficiently low to reduce non‐functional interactions. These findings have implication for conceptual understanding of intracellular compartmentalization, multicellularity and differentiation.     

蛋白质微阵列检测抗原-抗体相互作用

为了制备蛋白质微阵列和研究芯片表面抗原-抗体的相互作用,研究了如何在玻片表面固化蛋白质和用荧光染料（Cy3,Cy5）对蛋白质进行标记.结果表明,在醛基修饰的玻璃表面,通过共价偶联的方法将抗原或抗体固定到芯片表面,能使二者保持其特异性结合能力.同时,荧光标记后的抗原或抗体仍然具有特异性结合能力.蛋白质微阵列是通过机械手在玻片表面排阵制作的.芯片上的荧光信号获取采用了激光共焦荧光扫描系统.用不同浓度的抗原探针阵列,对其相应的抗体靶分子的特异性结合进行了分析和研究.此外,还通过在玻片表面固定兔IgG和固定鼠IgG,对羊抗兔和羊抗鼠抗体与其相应抗原的特异性相互作用进行了检测.     

Sulfo‐NHS‐SS‐biotin derivatization: A versatile tool for MALDI mass analysis of PTMs in lysine‐rich proteins

The discovery of PTMs in proteins by MS requires nearly complete sequence coverage of the detected proteolytic peptides. Unfortunately, mass spectrometric analysis of the desired sequence fragments is often impeded due to low ionization efficiency and/or signal suppression in complex samples. When several lysine residues are in close proximity tryptic peptides may be too short for mass analysis. Moreover, modified peptides often appear in low stoichiometry and need to be enriched before analysis. We present here how the use of sulfo‐NHS‐SS‐biotin derivatization of lysine side chain can help to detect PTMs in lysine‐rich proteins. This label leads to a mass shift which can be adjusted by reduction of the SS bridge and alkylation with different reagents. Low intensity peptides can be enriched by use of streptavidin beads. Using this method, the functionally relevant protein kinase A phosphorylation site in 5‐lipoxygenase was detected for the first time by MS. Additionally, methylation and acetylation could be unambiguously determined in histones.     

A Novel Core‐Attachment–Based Method to Identify Dynamic Protein Complexes Based on Gene Expression Profiles and PPI Networks

Cellular functions are always performed by protein complexes. At present, many approaches have been proposed to identify protein complexes from protein–protein interaction (PPI) networks. Some approaches focus on detecting local dense subgraphs in PPI networks which are regarded as protein‐complex cores, then identify protein complexes by including local neighbors. However, from gene expression profiles at different time points or tissues it is known that proteins are dynamic. Therefore, identifying dynamic protein complexes should become very important and meaningful. In this study, a novel core‐attachment–based method named CO‐DPC to detect dynamic protein complexes is presented. First, CO‐DPC selects active proteins according to gene expression profiles and the 3‐sigma principle, and constructs dynamic PPI networks based on the co‐expression principle and PPI networks. Second, CO‐DPC detects local dense subgraphs as the cores of protein complexes and then attach close neighbors of these cores to form protein complexes. In order to evaluate the method, the method and the existing algorithms are applied to yeast PPI networks. The experimental results show that CO‐DPC performs much better than the existing methods. In addition, the identified dynamic protein complexes can match very well and thus become more meaningful for future biological study.     

Hydrophobic environment is a key factor for the stability of thermophilic proteins

The stability of thermophilic proteins has been viewed from different perspectives and there is yet no unified principle to understand this stability. It would be valuable to reveal the most important interactions for designing thermostable proteins for such applications as industrial protein engineering. In this work, we have systematically analyzed the importance of various interactions by computing different parameters such as surrounding hydrophobicity, inter‐residue interactions, ion‐pairs and hydrogen bonds. The importance of each interaction has been determined by its predicted relative contribution in thermophiles versus the same contribution in mesophilic homologues based on a dataset of 373 protein families. We predict that hydrophobic environment is the major factor for the stability of thermophilic proteins and found that 80% of thermophilic proteins analyzed showed higher hydrophobicity than their mesophilic counterparts. Ion pairs, hydrogen bonds, and interaction energy are also important and favored in 68%, 50%, and 62% of thermophilic proteins, respectively. Interestingly, thermophilic proteins with decreased hydrophobic environments display a greater number of hydrogen bonds and/or ion pairs. The systematic elimination of mesophilic proteins based on surrounding hydrophobicity, interaction energy, and ion pairs/hydrogen bonds, led to correctly identifying 95% of the thermophilic proteins in our analyses. Our analysis was also applied to another, more refined set of 102 thermophilic–mesophilic pairs, which again identified hydrophobicity as a dominant property in 71% of the thermophilic proteins. Further, the notion of surrounding hydrophobicity, which characterizes the hydrophobic behavior of residues in a protein environment, has been applied to the three‐dimensional structures of elongation factor‐Tu proteins and we found that the thermophilic proteins are enriched with a hydrophobic environment. The results obtained in this work highlight the importance of hydrophobicity as the dominating characteristic in the stability of thermophilic proteins, and we anticipate this will be useful in our attempts to engineering thermostable proteins. © Proteins 2013. © 2012 Wiley Periodicals, Inc.     

Evaluation of different biological data and computational classification methods for use in protein interaction prediction

Protein–protein interactions play a key role in many biological systems. High‐throughput methods can directly detect the set of interacting proteins in yeast, but the results are often incomplete and exhibit high false‐positive and false‐negative rates. Recently, many different research groups independently suggested using supervised learning methods to integrate direct and indirect biological data sources for the protein interaction prediction task. However, the data sources, approaches, and implementations varied. Furthermore, the protein interaction prediction task itself can be subdivided into prediction of (1) physical interaction, (2) co‐complex relationship, and (3) pathway co‐membership. To investigate systematically the utility of different data sources and the way the data is encoded as features for predicting each of these types of protein interactions, we assembled a large set of biological features and varied their encoding for use in each of the three prediction tasks. Six different classifiers were used to assess the accuracy in predicting interactions, Random Forest (RF), RF similarity‐based k‐Nearest‐Neighbor, Naïve Bayes, Decision Tree, Logistic Regression, and Support Vector Machine. For all classifiers, the three prediction tasks had different success rates, and co‐complex prediction appears to be an easier task than the other two. Independently of prediction task, however, the RF classifier consistently ranked as one of the top two classifiers for all combinations of feature sets. Therefore, we used this classifier to study the importance of different biological datasets. First, we used the splitting function of the RF tree structure, the Gini index, to estimate feature importance. Second, we determined classification accuracy when only the top‐ranking features were used as an input in the classifier. We find that the importance of different features depends on the specific prediction task and the way they are encoded. Strikingly, gene expression is consistently the most important feature for all three prediction tasks, while the protein interactions identified using the yeast‐2‐hybrid system were not among the top‐ranking features under any condition. Proteins 2006. © 2006 Wiley‐Liss, Inc.     

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.