Structure of an ultraweak protein-protein complex and its crucial role in regulation of cell morphology and motility

Weak protein-protein interactions (PPIs) (K(D) > 10(-6) M) are critical determinants of many biological processes. However, in contrast to a large growing number of well-characterized, strong PPIs, the weak PPIs, especially those with K(D) > 10(-4) M, are poorly explored. Genome wide, there exist few 3D structures of weak PPIs with K(D) > 10(-4) M, and none with K(D) > 10(-3) M. Here, we report the NMR structure of an extremely weak focal adhesion complex (K(D) approximately 3 x 10(-3) M) between Nck-2 SH3 domain and PINCH-1 LIM4 domain. The structure exhibits a remarkably small and polar interface with distinct binding modes for both SH3 and LIM domains. Such an interface suggests a transient Nck-2/PINCH-1 association process that may trigger rapid focal adhesion turnover during integrin signaling. Genetic rescue experiments demonstrate that this interface is indeed involved in mediating cell shape change and migration. Together, the data provide a molecular basis for an ultraweak PPI in regulating focal adhesion dynamics during integrin signaling.     

Fluorescence resonance energy transfer-based technologies in the study of protein-protein interactions at the cell surface

Understanding of the molecular mechanisms of protein-protein interactions (PPIs) at the cell surface of living cells is fundamental to comprehend the functional meaning of a large number of cellular processes. Here we discuss how new methodological strategies derived from non-invasive fluorescence-based approaches (i.e. fluorescence resonance energy transfer, FRET) have been successfully developed to characterize plasma membrane PPIs. Importantly, these technologies alone - or in concert with complementary methods (i.e. SNAP-tag/TR-FRET, TIRF/FRET) - can become extremely powerful approaches for visualizing cell surface PPIs, even between more than two proteins and also in native tissues. Interestingly, these methods would also be relevant in drug discovery in order to develop new high-throughput screening approaches or to identify new therapeutic targets. Accordingly, herein we provide a thorough assessment on all biotechnological aspects, including strengths and weaknesses, of these fluorescence-based methodologies when applied in the study of PPIs occurring at the cell surface of living cells.     

Exploring weak, transient protein--protein interactions in crowded in vivo environments by in-cell nuclear magnetic resonance spectroscopy

Biology relies on functional interplay of proteins in the crowded and heterogeneous environment inside cells, and functional protein interactions are often weak and transient. Thus, methods that preserve these interactions and provide information about them are needed. In-cell nuclear magnetic resonance (NMR) spectroscopy is an attractive method for studying a protein's behavior in cells because it may provide residue-level structural and dynamic information, yet several factors limit the feasibility of protein NMR spectroscopy in cells; among them, slow rotational diffusion has emerged as the most important. In this paper, we seek to elucidate the causes of the dramatically slow protein tumbling in cells and in so doing to gain insight into how the intracellular viscosity and weak, transient interactions modulate protein mobility. To address these questions, we characterized the rotational diffusion of three model globular proteins in Escherichia coli cells using two-dimensional heteronuclear NMR spectroscopy. These proteins have a similar molecular size and globular fold but very different surface properties, and indeed, they show very different rotational diffusion in the E. coli intracellular environment. Our data are consistent with an intracellular viscosity approximately 8 times that of water, too low to be a limiting factor for observation of small globular proteins by in-cell NMR spectroscopy. Thus, we conclude that transient interactions with cytoplasmic components significantly and differentially affect the mobility of proteins and therefore their NMR detectability. Moreover, we suggest that an intricate interplay of total protein charge and hydrophobic interactions plays a key role in regulating these weak intermolecular interactions in cells.     

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。     

生物信息学方法预测蛋白质相互作用网络中的功能模块

蛋白质相互作用是大多数生命过程的基础。随着高通量实验技术和计算机预测方法的发展,在各种生物中已获得了数目十分庞大的蛋白质相互作用数据,如何从中提取出具有生物学意义的数据是一项艰巨的挑战。从蛋白质相互作用数据出发获得相互作用网络进而预测出其中的功能模块,对于蛋白质功能预测、揭示各种生化反应过程的分子机理都有着极大的帮助。我们分类概括了用生物信息学预测蛋白质相互作用功能模块的方法,以及对这些方法的评价,并介绍了蛋白质相互作用网络比较的一些方法。     

A discriminative approach for identifying domain–domain interactions from protein–protein interactions

Protein domains are functional and structural units of proteins. Therefore, identification of domain–domain interactions (DDIs) can provide insight into the biological functions of proteins. In this article, we propose a novel discriminative approach for predicting DDIs based on both protein–protein interactions (PPIs) and the derived information of non‐PPIs. We make a threefold contribution to the work in this area. First, we take into account non‐PPIs explicitly and treat the domain combinations that can discriminate PPIs from non‐PPIs as putative DDIs. Second, DDI identification is formalized as a feature selection problem, in which it tries to find out a minimum set of informative features (i.e., putative DDIs) that discriminate PPIs from non‐PPIs, which is plausible in biology and is able to predict DDIs in a systematic and accurate manner. Third, multidomain combinations including two‐domain combinations are taken into account in the proposed method, where multidomain cooperations may help proteins to interact with each other. Numerical results on several DDI prediction benchmark data sets show that the proposed discriminative method performs comparably well with other top algorithms with respect to overall performance, and outperforms other methods in terms of precision. The PPI data sets used for prediction of DDIs and prediction results can be found at http://csb.shu.edu.cn/dipd . Proteins 2010. © 2009 Wiley‐Liss, Inc.     

Photoactivated Localization Microscopy with Bimolecular Fluorescence Complementation (BiFC-PALM)

Protein-protein interactions (PPIs) are key molecular events to biology. However, it remains a challenge to visualize PPIs with sufficient resolution and sensitivity in cells because the resolution of conventional light microscopy is diffraction-limited to ~250 nm. By combining bimolecular fluorescence complementation (BiFC) with photoactivated localization microscopy (PALM), PPIs can be visualized in cells with single molecule sensitivity and nanometer spatial resolution. BiFC is a commonly used technique for visualizing PPIs with fluorescence contrast, which involves splitting of a fluorescent protein into two non-fluorescent fragments. PALM is a recent superresolution microscopy technique for imaging biological samples at the nanometer and single molecule scales, which uses phototransformable fluorescent probes such as photoactivatable fluorescent proteins (PA-FPs). BiFC-PALM was demonstrated by splitting PAmCherry1, a PA-FP compatible with PALM, for its monomeric nature, good single molecule brightness, high contrast ratio, and utility for stoichiometry measurements. When split between amino acids 159 and 160, PAmCherry1 can be made into a BiFC probe that reconstitutes efficiently at 37 °C with high specificity to PPIs and low non-specific reconstitution. Ras-Raf interaction is used as an example to show how BiFC-PALM helps to probe interactions at the nanometer scale and with single molecule resolution. Their diffusion can also be tracked in live cells using single molecule tracking (smt-) PALM. In this protocol, factors to consider when designing the fusion proteins for BiFC-PALM are discussed, sample preparation, image acquisition, and data analysis steps are explained, and a few exemplary results are showcased. Providing high spatial resolution, specificity, and sensitivity, BiFC-PALM is a useful tool for studying PPIs in intact biological samples.     

Coevolution is a short-distance force at the protein interaction level and correlates with the modular organization of protein networks

We investigated what roles coevolution plays in shaping yeast protein interaction network (PIN). We found that the extent of coevolution between two proteins decreases rapidly as their interacting distance on the PIN increases, suggesting coevolutionary constraint is a short-distance force at the molecular level. We also found that protein-protein interactions (PPIs) with strong coevolution tend to be enriched in interconnected clusters, whereas PPIs with weak coevolution are more frequently present at inter-cluster region. The findings indicate the close relationship between coevolution and modular organization of PINs, and may provide insights into evolution and modularity of cellular networks.     

Diversity of protein-protein interactions

In this review, we discuss the structural and functional diversity of protein-protein interactions (PPIs) based primarily on protein families for which three-dimensional structural data are available. PPIs play diverse roles in biology and differ based on the composition, affinity and whether the association is permanent or transient. In vivo, the protomer's localization, concentration and local environment can affect the interaction between protomers and are vital to control the composition and oligomeric state of protein complexes. Since a change in quaternary state is often coupled with biological function or activity, transient PPIs are important biological regulators. Structural characteristics of different types of PPIs are discussed and related to their physiological function, specificity and evolution.     

Computational prediction of host-pathogen protein-protein interactions

MOTIVATION: Infectious diseases such as malaria result in millions of deaths each year. An important aspect of any host-pathogen system is the mechanism by which a pathogen can infect its host. One method of infection is via protein-protein interactions (PPIs) where pathogen proteins target host proteins. Developing computational methods that identify which PPIs enable a pathogen to infect a host has great implications in identifying potential targets for therapeutics. RESULTS: We present a method that integrates known intra-species PPIs with protein-domain profiles to predict PPIs between host and pathogen proteins. Given a set of intra-species PPIs, we identify the functional domains in each of the interacting proteins. For every pair of functional domains, we use Bayesian statistics to assess the probability that two proteins with that pair of domains will interact. We apply our method to the Homo sapiens-Plasmodium falciparum host-pathogen system. Our system predicts 516 PPIs between proteins from these two organisms. We show that pairs of human proteins we predict to interact with the same Plasmodium protein are close to each other in the human PPI network and that Plasmodium pairs predicted to interact with same human protein are co-expressed in DNA microarray datasets measured during various stages of the Plasmodium life cycle. Finally, we identify functionally enriched sub-networks spanned by the predicted interactions and discuss the plausibility of our predictions. AVAILABILITY: Supplementary data are available at http://staff.vbi.vt.edu/dyermd/publications/dyer2007a.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Predicting permanent and transient protein–protein interfaces

Protein–protein interactions (PPIs) are involved in diverse functions in a cell. To optimize functional roles of interactions, proteins interact with a spectrum of binding affinities. Interactions are conventionally classified into permanent and transient, where the former denotes tight binding between proteins that result in strong complexes, whereas the latter compose of relatively weak interactions that can dissociate after binding to regulate functional activity at specific time point. Knowing the type of interactions has significant implications for understanding the nature and function of PPIs. In this study, we constructed amino acid substitution models that capture mutation patterns at permanent and transient type of protein interfaces, which were found to be different with statistical significance. Using the substitution models, we developed a novel computational method that predicts permanent and transient protein binding interfaces (PBIs) in protein surfaces. Without knowledge of the interacting partner, the method uses a single query protein structure and a multiple sequence alignment of the sequence family. Using a large dataset of permanent and transient proteins, we show that our method, BindML+, performs very well in protein interface classification. A very high area under the curve (AUC) value of 0.957 was observed when predicted protein binding sites were classified. Remarkably, near prefect accuracy was achieved with an AUC of 0.991 when actual binding sites were classified. The developed method will be also useful for protein design of permanent and transient PBIs. © Proteins 2013. © 2012 Wiley Periodicals, Inc.     

Uncovering post-translational modification-associated protein–protein interactions

In living systems, the chemical space and functional repertoire of proteins are dramatically expanded through the post-translational modification (PTM) of various amino acid residues. These modifications frequently trigger unique protein–protein interactions (PPIs) – for example with reader proteins that directly bind the modified amino acid residue – which leads to downstream functional outcomes. The modification of a protein can also perturb its PPI network indirectly, for example, through altering its conformation or subcellular localization. Uncovering the network of unique PTM-triggered PPIs is essential to fully understand the roles of an ever-expanding list of PTMs in our biology. In this review, we discuss established strategies and current challenges associated with this endeavor.     

Protein-protein interactions in plants

The study of protein-protein interactions (PPIs) is essential to uncover unknown functions of proteins at the molecular level and to gain insight into complex cellular networks. Affinity purification and mass spectrometry (AP-MS), yeast two-hybrid, imaging approaches and numerous diverse databases have been developed as strategies to analyze PPIs. The past decade has seen an increase in the number of identified proteins with the development of MS and large-scale proteome analyses. Consequently, the false-positive protein identification rate has also increased. Therefore, the general consensus is to confirm PPI data using one or more independent approaches for an accurate evaluation. Furthermore, identifying minor PPIs is fundamental for understanding the functions of transient interactions and low-abundance proteins. Besides establishing PPI methodologies, we are now seeing the development of new methods and/or improvements in existing methods, which involve identifying minor proteins by MS, multidimensional protein identification technology or OFFGEL electrophoresis analyses, one-shot analysis with a long column or filter-aided sample preparation methods. These advanced techniques should allow thousands of proteins to be identified, whereas in-depth proteomic methods should permit the identification of transient binding or PPIs with weak affinity. Here, the current status of PPI analysis is reviewed and some advanced techniques are discussed briefly along with future challenges for plant proteomics.     

Annotating activation/inhibition relationships to protein-protein interactions using gene ontology relations

Background

Signaling pathways can be reconstructed by identifying ‘effect types’ (i.e. activation/inhibition) of protein-protein interactions (PPIs). Effect types are composed of ‘directions’ (i.e. upstream/downstream) and ‘signs’ (i.e. positive/negative), thereby requiring directions as well as signs of PPIs to predict signaling events from PPI networks. Here, we propose a computational method for systemically annotating effect types to PPIs using relations between functional information of proteins.

Results

We used regulates, positively regulates, and negatively regulates relations in Gene Ontology (GO) to predict directions and signs of PPIs. These relations indicate both directions and signs between GO terms so that we can project directions and signs between relevant GO terms to PPIs. Independent test results showed that our method is effective for predicting both directions and signs of PPIs. Moreover, our method outperformed a previous GO-based method that did not consider the relations between GO terms. We annotated effect types to human PPIs and validated several highly confident effect types against literature. The annotated human PPIs are available in Additional file 2 to aid signaling pathway reconstruction and network biology research.

Conclusions

We annotated effect types to PPIs by using regulates, positively regulates, and negatively regulates relations in GO. We demonstrated that those relations are effective for predicting not only signs, but also directions of PPIs. The usefulness of those relations suggests their potential applications to other types of interactions such as protein-DNA interactions.

     

Detection of membrane protein–protein interaction in planta based on dual‐intein‐coupled tripartite split‐GFP association

Despite the great interest in identifying protein–protein interactions (PPIs) in biological systems, only a few attempts have been made at large‐scale PPI screening in planta. Unlike biochemical assays, bimolecular fluorescence complementation allows visualization of transient and weak PPIs in vivo at subcellular resolution. However, when the non‐fluorescent fragments are highly expressed, spontaneous and irreversible self‐assembly of the split halves can easily generate false positives. The recently developed tripartite split‐GFP system was shown to be a reliable PPI reporter in mammalian and yeast cells. In this study, we adapted this methodology, in combination with the β‐estradiol‐inducible expression cassette, for the detection of membrane PPIs in planta. Using a transient expression assay by agroinfiltration of Nicotiana benthamiana leaves, we demonstrate the utility of the tripartite split‐GFP association in plant cells and affirm that the tripartite split‐GFP system yields no spurious background signal even with abundant fusion proteins readily accessible to the compartments of interaction. By validating a few of the Arabidopsis PPIs, including the membrane PPIs implicated in phosphate homeostasis, we proved the fidelity of this assay for detection of PPIs in various cellular compartments in planta. Moreover, the technique combining the tripartite split‐GFP association and dual‐intein‐mediated cleavage of polyprotein precursor is feasible in stably transformed Arabidopsis plants. Our results provide a proof‐of‐concept implementation of the tripartite split‐GFP system as a potential tool for membrane PPI screens in planta.     

Adding Protein Context to the Human Protein-Protein Interaction Network to Reveal Meaningful Interactions

Interactions of proteins regulate signaling, catalysis, gene expression and many other cellular functions. Therefore, characterizing the entire human interactome is a key effort in current proteomics research. This challenge is complicated by the dynamic nature of protein-protein interactions (PPIs), which are conditional on the cellular context: both interacting proteins must be expressed in the same cell and localized in the same organelle to meet. Additionally, interactions underlie a delicate control of signaling pathways, e.g. by post-translational modifications of the protein partners - hence, many diseases are caused by the perturbation of these mechanisms. Despite the high degree of cell-state specificity of PPIs, many interactions are measured under artificial conditions (e.g. yeast cells are transfected with human genes in yeast two-hybrid assays) or even if detected in a physiological context, this information is missing from the common PPI databases. To overcome these problems, we developed a method that assigns context information to PPIs inferred from various attributes of the interacting proteins: gene expression, functional and disease annotations, and inferred pathways. We demonstrate that context consistency correlates with the experimental reliability of PPIs, which allows us to generate high-confidence tissue- and function-specific subnetworks. We illustrate how these context-filtered networks are enriched in bona fide pathways and disease proteins to prove the ability of context-filters to highlight meaningful interactions with respect to various biological questions. We use this approach to study the lung-specific pathways used by the influenza virus, pointing to IRAK1, BHLHE40 and TOLLIP as potential regulators of influenza virus pathogenicity, and to study the signalling pathways that play a role in Alzheimer''s disease, identifying a pathway involving the altered phosphorylation of the Tau protein. Finally, we provide the annotated human PPI network via a web frontend that allows the construction of context-specific networks in several ways.     

Global geometric affinity for revealing high fidelity protein interaction network

Protein-protein interaction (PPI) network analysis presents an essential role in understanding the functional relationship among proteins in a living biological system. Despite the success of current approaches for understanding the PPI network, the large fraction of missing and spurious PPIs and a low coverage of complete PPI network are the sources of major concern. In this paper, based on the diffusion process, we propose a new concept of global geometric affinity and an accompanying computational scheme to filter the uncertain PPIs, namely, reduce the spurious PPIs and recover the missing PPIs in the network. The main concept defines a diffusion process in which all proteins simultaneously participate to define a similarity metric (global geometric affinity (GGA)) to robustly reflect the internal connectivity among proteins. The robustness of the GGA is attributed to propagating the local connectivity to a global representation of similarity among proteins in a diffusion process. The propagation process is extremely fast as only simple matrix products are required in this computation process and thus our method is geared toward applications in high-throughput PPI networks. Furthermore, we proposed two new approaches that determine the optimal geometric scale of the PPI network and the optimal threshold for assigning the PPI from the GGA matrix. Our approach is tested with three protein-protein interaction networks and performs well with significant random noises of deletions and insertions in true PPIs. Our approach has the potential to benefit biological experiments, to better characterize network data sets, and to drive new discoveries.     

Mammalian Hippo signalling: a kinase network regulated by protein-protein interactions

The Hippo signal transduction cascade controls cell growth, proliferation and death, all of which are frequently deregulated in tumour cells. Since initial studies in Drosophila melanogaster were instrumental in defining Hippo signalling, the machinery was named after the central Ste20-like kinase Hippo. Moreover, given that loss of Hippo signalling components Hippo, Warts, and Mats resulted in uncontrolled tissue overgrowth, Hippo signalling was defined as a tumour-suppressor cascade. Significantly, all of the core factors of Hippo signalling have mammalian orthologues that functionally compensate for loss of their counterparts in Drosophila. Furthermore, studies in Drosophila and mammalian cell systems showed that Hippo signalling represents a kinase cascade that is tightly regulated by PPIs (protein-protein interactions). Several Hippo signalling molecules contain SARAH (Salvador/RASSF1A/Hippo) domains that mediate specific PPIs, thereby influencing the activities of MST1/2 (mammalian Ste20-like serine/threonine kinase 1/2) kinases, the human Hippo orthologues. Moreover, WW domains are present in several Hippo factors, and these domains also serve as interaction surfaces for regulatory PPIs in Hippo signalling. Finally, the kinase activities of LATS1/2 (large tumour-suppressor kinase 1/2), the human counterparts of Warts, are controlled by binding to hMOB1 (human Mps one binder protein 1), the human Mats. Therefore Hippo signalling is regulated by PPIs on several levels. In the present paper, I review the current understanding of how these regulatory PPIs are regulated and contribute to the functionality of Hippo signalling.