Mapping protein family interactions: intramolecular and intermolecular protein family interaction repertoires in the PDB and yeast

In the postgenomic era, one of the most interesting and important challenges is to understand protein interactions on a large scale. The physical interactions between protein domains are fundamental to the workings of a cell: in multi-domain polypeptide chains, in multi-subunit proteins and in transient complexes between proteins that also exist independently. To study the large-scale patterns and evolution of interactions between protein domains, we view interactions between protein domains in terms of the interactions between structural families of evolutionarily related domains. This allows us to classify 8151 interactions between individual domains in the Protein Data Bank and the yeast Saccharomyces cerevisiae in terms of 664 types of interactions, between protein families. At least 51 interactions do not occur in the Protein Data Bank and can only be derived from the yeast data. The map of interactions between protein families has the form of a scale-free network, meaning that most protein families only interact with one or two other families, while a few families are extremely versatile in their interactions and are connected to many families. We observe that almost half of all known families engage in interactions with domains from their own family. We also see that the repertoires of interactions of domains within and between polypeptide chains overlap mostly for two specific types of protein families: enzymes and same-family interactions. This suggests that different types of protein interaction repertoires exist for structural, functional and regulatory reasons. Copyright 12001 Academic Press.     

Integrative approach for computationally inferring protein domain interactions

MOTIVATION: The current need for high-throughput protein interaction detection has resulted in interaction data being generated en masse through such experimental methods as yeast-two-hybrids and protein chips. Such data can be erroneous and they often do not provide adequate functional information for the detected interactions. Therefore, it is useful to develop an in silico approach to further validate and annotate the detected protein interactions. RESULTS: Given that protein-protein interactions involve physical interactions between protein domains, domain-domain interaction information can be useful for validating, annotating, and even predicting protein interactions. However, large-scale, experimentally determined domain-domain interaction data do not exist. Here, we describe an integrative approach to computationally derive putative domain interactions from multiple data sources, including protein interactions, protein complexes, and Rosetta Stone sequences. We further prove the usefulness of such an integrative approach by applying the derived domain interactions to predict and validate protein-protein interactions. AVAILABILITY: A database of putative protein domain interactions derived using the method described in this paper is available at http://interdom.lit.org.sg.     

Geometric cooperativity and anticooperativity of three-body interactions in native proteins

Characterizing multibody interactions of hydrophobic, polar, and ionizable residues in protein is important for understanding the stability of protein structures. We introduce a geometric model for quantifying 3-body interactions in native proteins. With this model, empirical propensity values for many types of 3-body interactions can be reliably estimated from a database of native protein structures, despite the overwhelming presence of pairwise contacts. In addition, we define a nonadditive coefficient that characterizes cooperativity and anticooperativity of residue interactions in native proteins by measuring the deviation of 3-body interactions from 3 independent pairwise interactions. It compares the 3-body propensity value from what would be expected if only pairwise interactions were considered, and highlights the distinction of propensity and cooperativity of 3-body interaction. Based on the geometric model, and what can be inferred from statistical analysis of such a model, we find that hydrophobic interactions and hydrogen-bonding interactions make nonadditive contributions to protein stability, but the nonadditive nature depends on whether such interactions are located in the protein interior or on the protein surface. When located in the interior, many hydrophobic interactions such as those involving alkyl residues are anticooperative. Salt-bridge and regular hydrogen-bonding interactions, such as those involving ionizable residues and polar residues, are cooperative. When located on the protein surface, these salt-bridge and regular hydrogen-bonding interactions are anticooperative, and hydrophobic interactions involving alkyl residues become cooperative. We show with examples that incorporating 3-body interactions improves discrimination of protein native structures against decoy conformations. In addition, analysis of cooperative 3-body interaction may reveal spatial motifs that can suggest specific protein functions.     

New methodologies for measuring protein interactions in vivo and in vitro

The identification and characterization of protein interactions is a key topic in current life science research; a huge variety of methodologies have been established in recent years to expedite research in this area. Generic methods have been established for monitoring protein interactions in vivo by protein fragment complementation and for screening protein interactions in vitro by highly parallel solid-phase techniques. Substantial progress has been made in identifying and characterizing interactions with and between membrane proteins. Studying protein interactions on the single-molecule level has become an important tool for understanding protein function in vivo and in vitro.     

The diversity of physical forces and mechanisms in intermolecular interactions

Intermolecular interactions became an inherent part of the structure-function paradigm. Therefore, the generalized concept of protein stability and interactions should consider the balance of stabilizing forces working in different types of intermolecular interactions. We consider here two 'extremes' of protein interactions, viral protein with high intrinsic disorder and hyperthermostable protein complexes. Intermolecular interactions provide folding upon binding as a part of function in the viral case, while they secure and stabilize specific native interfaces as a prerequisite for function in hyperthermostable complexes. We propose a generalized concept of protein stability and interactions, which includes intermolecular interactions comprising distinct combinations of stabilizing forces depending on the types of interacting partners.     

An integrated approach to the prediction of domain-domain interactions

Background  

The development of high-throughput technologies has produced several large scale protein interaction data sets for multiple species, and significant efforts have been made to analyze the data sets in order to understand protein activities. Considering that the basic units of protein interactions are domain interactions, it is crucial to understand protein interactions at the level of the domains. The availability of many diverse biological data sets provides an opportunity to discover the underlying domain interactions within protein interactions through an integration of these biological data sets.     

Latest developments in experimental and computational approaches to characterize protein–lipid interactions

Understanding the functional roles of all the molecules in cells is an ultimate goal of modern biology. An important facet is to understand the functional contributions from intermolecular interactions, both within a class of molecules (e.g. protein–protein) or between classes (e.g. protein‐DNA). While the technologies for analyzing protein–protein and protein–DNA interactions are well established, the field of protein–lipid interactions is still relatively nascent. Here, we review the current status of the experimental and computational approaches for detecting and analyzing protein–lipid interactions. Experimental technologies fall into two principal categories, namely solution‐based and array‐based methods. Computational methods include large–scale data‐driven analyses and predictions/dynamic simulations based on prior knowledge of experimentally identified interactions. Advances in the experimental technologies have led to improved computational analyses and vice versa, thereby furthering our understanding of protein–lipid interactions and their importance in biological systems.     

Applications of isothermal titration calorimetry in protein science

During the past decade, isothermal titration calorimetry (ITC) has developed from a specialist method for understanding molecular interactions and other biological processes within cells to a more robust, widely used method. Nowadays, ITC is used to investigate all types of protein interactions, including protein-protein interactions, protein-DNA/RNA interactions, protein-small molecule interactions and enzyme kinetics; it provides a direct route to the complete thermodynamic characterization of protein interactions. This review concentrates on the new applications of ITC in protein folding and misfolding, its traditional application in protein interactions, and an overview of what can be achieved in the field of protein science using this method and what developments are likely to occur in the near future. Also, this review discusses some new developments of ITC method in protein science, such as the reverse titration of ITC and the displacement method of ITC.     

Aromatic interactions in peptides: impact on structure and function

Aromatic interactions, including pi-pi, cation-pi, aryl-sulfur, and carbohydrate-pi interactions, have been shown to be prevalent in proteins through protein structure analysis, suggesting that they are important contributors to protein structure. However, the magnitude and significance of aromatic interactions is not defined by such studies. Investigation of aromatic interactions in the context of structured peptides has complemented studies of protein structure and has provided a wealth of information regarding the role of aromatic interactions in protein structure and function. Recent advances in this area are reviewed.     

The application of yeast hybrid systems in protein interaction analysis

Yeast hybrid systems have been widely used due to their convenience and low cost. Based on these systems, many methods have been developed to analyze protein–protein, protein–DNA and protein–RNA interactions. In this paper, we are reviewing these different yeast hybrid systems. According to the number of hybrid proteins, yeast hybrid systems can be divided into three categories, yeast one-hybrid, yeast two-hybrid and yeast three-hybrid systems. Alternatively, yeast hybrid systems can be categorized according to the subcellular localization of the protein interaction process in the cell into nuclear protein–protein interactions, cytosol protein–protein interactions and membrane protein–protein interactions. Throughout the review, we focus on the progress and limitations of each yeast hybrid system over the recent years.     

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。     

Examining post‐translational modification‐mediated protein–protein interactions using a chemical proteomics approach

Post‐translational modifications (PTM) of proteins can control complex and dynamic cellular processes via regulating interactions between key proteins. To understand these regulatory mechanisms, it is critical that we can profile the PTM‐dependent protein–protein interactions. However, identifying these interactions can be very difficult using available approaches, as PTMs can be dynamic and often mediate relatively weak protein–protein interactions. We have recently developed CLASPI (cross‐linking‐assisted and stable isotope labeling in cell culture‐based protein identification), a chemical proteomics approach to examine protein–protein interactions mediated by methylation in human cell lysates. Here, we report three extensions of the CLASPI approach. First, we show that CLASPI can be used to analyze methylation‐dependent protein–protein interactions in lysates of fission yeast, a genetically tractable model organism. For these studies, we examined trimethylated histone H3 lysine‐9 (H3K9Me₃)‐dependent protein–protein interactions. Second, we demonstrate that CLASPI can be used to examine phosphorylation‐dependent protein–protein interactions. In particular, we profile proteins recognizing phosphorylated histone H3 threonine‐3 (H3T3‐Phos), a mitotic histone “mark” appearing exclusively during cell division. Our approach identified survivin, the only known H3T3‐Phos‐binding protein, as well as other proteins, such as MCAK and KIF2A, that are likely to be involved in weak but selective interactions with this histone phosphorylation “mark”. Finally, we demonstrate that the CLASPI approach can be used to study the interplay between histone H3T3‐Phos and trimethylation on the adjacent residue lysine 4 (H3K4Me₃). Together, our findings indicate the CLASPI approach can be broadly applied to profile protein–protein interactions mediated by PTMs.     

A reliability measure of protein–protein interactions and a reliability measure-based search engine

Many methods developed for estimating the reliability of protein–protein interactions are based on the topology of protein–protein interaction networks. This paper describes a new reliability measure for protein–protein interactions, which does not rely on the topology of protein interaction networks, but expresses biological information on functional roles, sub-cellular localisations and protein classes as a scoring schema. The new measure is useful for filtering many spurious interactions, as well as for estimating the reliability of protein interaction data. In particular, the reliability measure can be used to search protein–protein interactions with the desired reliability in databases. The reliability-based search engine is available at http://yeast.hpid.org. We believe this is the first search engine for interacting proteins, which is made available to public. The search engine and the reliability measure of protein interactions should provide useful information for determining proteins to focus on.     

High-throughput and multiplexed protein array technology: protein-DNA and protein-protein interactions

Miniaturized protein arrays address protein interactions with various types of molecules in a high-throughput and multiplexed fashion. This review focuses on achievements in the analysis of protein-DNA and protein-protein interactions. The technological feasibility of protein arrays depends on the different factors that enable the arrayed proteins to recognize molecular partners and on the specificity of the interactions involved. Proteome-scale studies of molecular interactions require high-throughput approaches for both the production and purification of functionally active proteins. Various solutions have been proposed to avoid non-specific protein interactions on array supports and to monitor low-abundance molecules. The data accumulated indicate that this emerging technology is perfectly suited to resolve networks of protein interactions involved in complex physiological and pathological phenomena in different organisms and to develop sensitive tools for biomedical applications.     

Role for protein–protein interaction databases in human genetics

Proteomics and the study of protein–protein interactions are becoming increasingly important in our effort to understand human diseases on a system-wide level. Thanks to the development and curation of protein-interaction databases, up-to-date information on these interaction networks is accessible and publicly available to the scientific community. As our knowledge of protein–protein interactions increases, it is important to give thought to the different ways that these resources can impact biomedical research. In this article, we highlight the importance of protein–protein interactions in human genetics and genetic epidemiology. Since protein–protein interactions demonstrate one of the strongest functional relationships between genes, combining genomic data with available proteomic data may provide us with a more in-depth understanding of common human diseases. In this review, we will discuss some of the fundamentals of protein interactions, the databases that are publicly available and how information from these databases can be used to facilitate genome-wide genetic studies.     

Multiple Modes of Protein–Protein Interactions Promote RNP Granule Assembly

Eukaryotic cells are known to contain a wide variety of RNA–protein assemblies, collectively referred to as RNP granules. RNP granules form from a combination of RNA–RNA, protein–RNA, and protein–protein interactions. In addition, RNP granules are enriched in proteins with intrinsically disordered regions (IDRs), which are frequently appended to a well-folded domain of the same protein. This structural organization of RNP granule components allows for a diverse set of protein–protein interactions including traditional structured interactions between well-folded domains, interactions of short linear motifs in IDRs with the surface of well-folded domains, interactions of short motifs within IDRs that weakly interact with related motifs, and weak interactions involving at most transient ordering of IDRs and folded domains with other components. In addition, both well-folded domains and IDRs in granule components frequently interact with RNA and thereby can contribute to RNP granule assembly. We discuss the contribution of these interactions to liquid–liquid phase separation and the possible role of phase separation in the assembly of RNP granules. We expect that these principles also apply to other non-membrane bound organelles and large assemblies in the cell.     

Characterization of Ternary Protein Systems In?Vivo with Tricolor Heterospecies Partition Analysis

Tools and assays that characterize protein-protein interactions are of fundamental importance to biology, because protein assemblies play a critical role in the control and regulation of nearly every cellular process. The availability of fluorescent proteins has facilitated the direct and real-time observation of protein-protein interactions inside living cells, but existing methods are mostly limited to binary interactions between two proteins. Because of the scarcity of techniques capable of identifying ternary interactions, we developed tricolor heterospecies partition analysis. The technique is based on brightness analysis of fluorescence fluctuations from three fluorescent proteins that serve as protein labels. We identified three fluorescent proteins suitable for tricolor brightness experiments. In addition, we developed the theory of identifying interactions in a ternary protein system using tricolor heterospecies partition analysis. The theory was verified by experiments on well-characterized protein systems. A graphical representation of the heterospecies partition data was introduced to visualize interactions in ternary protein systems. Lastly, we performed fluorescence fluctuation experiments on cells expressing a coactivator and two nuclear receptors and applied heterospecies partition analysis to explore the interactions of this ternary protein system.     

Protein-protein interactions among the components of the biosynthetic machinery responsible for exopolysaccharide production in Streptococcus thermophilus MR-1C

Aim: This study identified protein–protein interactions among the biosynthetic machinery responsible for exopolysaccharide (EPS) production in Streptococcus thermophilus MR‐1C. Methods and Results: Protein–protein interactions were investigated using the yeast two‐hybrid system. A strong protein–protein interaction was detected between the transmembrane activation protein Wzd and the protein tyrosine kinase Wze. Weaker protein–protein interactions were detected between two duplicate Wze proteins and between Wze and the phosphotyrosine phosphatase Wzh. Protein–protein interactions involving a Wzd/Wze fusion protein and Wzd and Wze may indicate that these proteins form multi‐protein complexes. All combinations of the Wzh, Wzd, Wze, Wzg (regulation), CpsE (glycosyl‐1‐phosphate transferase), CpsS (polymerization), CpsL (unknown), CpsW (regulation) and CpsU (membrane translocation) were analysed for protein–protein interactions but no additional interactions were discovered using the yeast two‐hybrid system. Conclusions: Interactions among the phosphotyrosine phosphatase, tyrosine kinase, and transmembrane activation protein are important in the regulation of capsule biosynthesis in Strep. thermophilus MR‐1C. Significance and Impact of the Study: This study provides some valuable insight into the organization and interactions between the many proteins involved in EPS production. A better understanding of this process may facilitate the genetic manipulation of capsule production to impart desirable properties to dairy starter cultures.     

Selection of Protein–Protein Interactions of Desired Affinities with a Bandpass Circuit

We have developed a genetic circuit in Escherichia coli that can be used to select for protein–protein interactions of different strengths by changing antibiotic concentrations in the media. The genetic circuit links protein–protein interaction strength to β-lactamase activity while simultaneously imposing tuneable positive and negative selection pressure for β-lactamase activity. Cells only survive if they express interacting proteins with affinities that fall within set high- and low-pass thresholds; i.e. the circuit therefore acts as a bandpass filter for protein–protein interactions. We show that the circuit can be used to recover protein–protein interactions of desired affinity from a mixed population with a range of affinities. The circuit can also be used to select for inhibitors of protein–protein interactions of defined strength.