Protein-protein interaction in transport

The transport of histidine in the gram negative bacterium S. typhimurium has been studied over a number of years and found to occur through five transport systems (Ames, 1972). Of these, the one with the highest affinity has been studied in detail from the genetic, physiological and biochemical point of view. This system, known as the high-affinity histidine permease, is composed of two subsystems, the J-P and K-P systems, which have a component in common, the P protein, presumed to be membrane-bound. The J-P system, moreover, is known to require the presence of a periplasmic histidine-binding protein, the J protein. The J protein is coded for by the hisJ gene and the P protein is coded for by the hisP gene. Both of these genes have been mapped at 75 min on the Salmonella chromosomal map. Adjacent to them is a regulatory gene, the dhuA gene. The periplasmic histidine-binding protein J has been shown to interact directly with the second component of transport, the P protein (Ames and Spudich, 1976). In accordance with this, histidine-binding protein J has been shown to contain, besides the histidine-binding site, a second site, essential for function, the interaction site (Kustu and Ames, 1974). We have recently shown that a mutant J protein with a defective interaction site but an intact histidine-binding site cannot function in histidine transport, unless an appropriate compensating mutation is introduced in the P protein. The interaction between the J and P proteins is an obligatory step in transport. The mutation in the interaction site of the J protein has been shown to map in the hisJ gene, and the compensating supressor mutation in the P protein has been shown to map in the hisP gene. Our contention that the J and P proteins engage in a functional interaction assumes further strength from other studies on protein-protein interaction in bacteriophage development and in ribosomal structure. Among the possible functions of the J-P interaction in histidine transport, a likely one is the transmission of information to the P protein, concerning whether or not the histidine-binding site on the J protein is occupied. Appropriate conformational changes then can occur in either the J or the P protein, or both, such that the histidine is released in the correct location and direction on the inside of the cell. This could occur either by a pore-formation mechanism or by binding-site translocation. Another alternative is that the P protein is part of an energy transducing mechanism in which energy is transmitted to the J protein, through the interaction site, as a prerequisite for the J protein participation in translocation. Among the interesting findings coming out of this work, is also the fact that the P protein performs a central function in transport being involved in the permeation of other substrates besides histidine. It is likely that other binding proteins besides the J protein require the P protein. Thus an interesting question which we are trying to answer at present is whether the P protein has separate interaction sites for each of these other binding proteins requiring its function, or whether they all interact at one common site.     

Protein-protein interaction through beta-strand addition

Protein-protein interactions have essential roles at almost every level of organization and communication in living cells. During complex formation, proteins can interact via covalent, surface-surface or peptide-surface contacts. Many protein complexes are now known to involve the binding of linear motifs in one of the binding partners. An emerging mechanism of such non-covalent peptide-surface interaction involves the donation or addition of a beta strand in the ligand to a beta sheet or a beta strand in the receptor. Such 'beta-strand addition' contacts can dictate or modulate binding specificity and affinity, or can be used in more promiscuous protein-protein contacts. Three main classes of beta-strand addition can be distinguished: beta-sheet augmentation; beta-strand insertion and fold complementation; and beta-strand zippering. A survey of protein-protein complexes in the protein data bank identifies beta-strand additions in many important metabolic pathways. Targeting these interactions might, thus, provide novel routes for rational drug design.     

Protein-protein interaction regulates proteins' mechanical stability

Elastomeric proteins are molecular springs found not only in a variety of biological machines and tissues, but also in biomaterials of superb mechanical properties. Regulating the mechanical stability of elastomeric proteins is not only important for a range of biological processes, but also critical for the use of engineered elastomeric proteins as building blocks to construct nanomechanical devices and novel materials of well-defined mechanical properties. Here we demonstrate that protein-protein interactions can potentially serve as an effective means to regulate the mechanical properties of elastomeric proteins. We show that the binding of fragments of IgG antibody to a small protein, GB1, can significantly enhance the mechanical stability of GB1. The regulation of the mechanical stability of GB1 by IgG fragments is not through direct modification of the interactions in the mechanically key region of GB1; instead, it is accomplished via the long-range coupling between the IgG binding site and the mechanically key region of GB1. Although Fc and Fab bind GB1 at different regions of GB1, their binding to GB1 can increase the mechanical stability of GB1 significantly. Using alanine point mutants of GB1, we show that the amplitude of mechanical stability enhancement of GB1 by Fc does not correlate with the binding affinity, suggesting that binding affinity only affects the population of GB1/human Fc (hFc) complex at a given concentration of hFc, but does not affect the intrinsic mechanical stability of the GB1/hFc complex. Furthermore, our results indicate that the mechanical stability enhancement by IgG fragments is robust and can tolerate sequence/structural perturbation to GB1. Our results demonstrate that the protein-protein interaction is an efficient approach to regulate the mechanical stability of GB1-like proteins and we anticipate that this new methodology will help to develop novel elastomeric proteins with tunable mechanical stability and compliance.     

Protein-protein interaction based on pairwise similarity

Background  

Protein-protein interaction (PPI) is essential to most biological processes. Abnormal interactions may have implications in a number of neurological syndromes. Given that the association and dissociation of protein molecules is crucial, computational tools capable of effectively identifying PPI are desirable. In this paper, we propose a simple yet effective method to detect PPI based on pairwise similarity and using only the primary structure of the protein. The PPI based on Pairwise Similarity (PPI-PS) method consists of a representation of each protein sequence by a vector of pairwise similarities against large subsequences of amino acids created by a shifting window which passes over concatenated protein training sequences. Each coordinate of this vector is typically the E-value of the Smith-Waterman score. These vectors are then used to compute the kernel matrix which will be exploited in conjunction with support vector machines.     

Protein-protein interaction hotspots carved into sequences

Protein-protein interactions, a key to almost any biological process, are mediated by molecular mechanisms that are not entirely clear. The study of these mechanisms often focuses on all residues at protein-protein interfaces. However, only a small subset of all interface residues is actually essential for recognition or binding. Commonly referred to as "hotspots," these essential residues are defined as residues that impede protein-protein interactions if mutated. While no in silico tool identifies hotspots in unbound chains, numerous prediction methods were designed to identify all the residues in a protein that are likely to be a part of protein-protein interfaces. These methods typically identify successfully only a small fraction of all interface residues. Here, we analyzed the hypothesis that the two subsets correspond (i.e., that in silico methods may predict few residues because they preferentially predict hotspots). We demonstrate that this is indeed the case and that we can therefore predict directly from the sequence of a single protein which residues are interaction hotspots (without knowledge of the interaction partner). Our results suggested that most protein complexes are stabilized by similar basic principles. The ability to accurately and efficiently identify hotspots from sequence enables the annotation and analysis of protein-protein interaction hotspots in entire organisms and thus may benefit function prediction and drug development. The server for prediction is available at http://www.rostlab.org/services/isis.     

Protein-protein interaction networks in the spinocerebellar ataxias

A large yeast two-hybrid study investigating whether the proteins mutated in different forms of spinocerebellar ataxia have interacting protein partners in common suggests that some forms do share common pathways, and will provide a valuable resource for future work on these diseases.     

Protein-protein interaction in low water organic media

Trypsin either modified with polyethylene glycol or as a suspended powder was used to catalyze digestion of protein substrates in benzene in order to get insight into protein-protein interactions in water-immiscible organic media. Depending on whether suspended or soluble trypsin was used, catalysis was found to proceed differently. In the first case, the amount of water in the reaction mixture (up to 1% v/v) appeared to be critical, and adsorption of water from the reaction medium by the protein substrate allowed it to behave as a hydrophilic support material comparable to that involved in immobilized enzymes. In the latter case, the presence of an additional nucleophile was a prerequisite for catalysis to proceed, and thus both water and nucleophile concentrations had some influence on trypsin activity. Phe-NH(2) was the most potent nucleophile for proteolysis catalyzed by polyethylene glycol-modified trypsin in organic media containing 1-2% water (v/v). The organic solvent-soluble enzyme was found to bind reversibly to the protein substrate as a function of both extent of hydration of the reaction medium and time of incubation. The overall results strongly suggested that modified trypsin catalyzed peptide bond hydrolysis at the protein substrate-organic solvent interface. Peptide mapping of bovine insulin digest by reversed-phase high-performance liquid chromatography definitely showed that enzyme-catalyzed proteolysis did occur in organic solvents with a concomitant and significant transpeptidation reaction.     

Protein-protein interaction networks: from interactions to networks

The goal of interaction proteomics that studies the protein-protein interactions of all expressed proteins is to understand biological processes that are strictly regulated by these interactions. The availability of entire genome sequences of many organisms and high-throughput analysis tools has led scientists to study the entire proteome (Pandey and Mann, 2000). There are various high-throughput methods for detecting protein interactions such as yeast two-hybrid approach and mass spectrometry to produce vast amounts of data that can be utilized to decipher protein functions in complicated biological networks. In this review, we discuss recent developments in analytical methods for large-scale protein interactions and the future direction of interaction proteomics.     

Protein-protein interaction site mapping using NMR-detected mutational scanning

We demonstrate a novel NMR method for the mapping of protein–protein interaction sites. In our approach protein–protein binding sites are mapped by competition binding experiments using indirect NMR reporter technology and Ala positional scanning. The methodology provides high sensitivity, ease of implementation and high-throughput capabilities. The feasibility of the technique is demonstrated with an application to the β-Catenin/Tcf4 complex.     

Protein-protein interaction as a predictor of subcellular location

Background  

Many biological processes are mediated by dynamic interactions between and among proteins. In order to interact, two proteins must co-occur spatially and temporally. As protein-protein interactions (PPIs) and subcellular location (SCL) are discovered via separate empirical approaches, PPI and SCL annotations are independent and might complement each other in helping us to understand the role of individual proteins in cellular networks. We expect reliable PPI annotations to show that proteins interacting in vivo are co-located in the same cellular compartment. Our goal here is to evaluate the potential of using PPI annotation in determining SCL of proteins in human, mouse, fly and yeast, and to identify and quantify the factors that contribute to this complementarity.     

Protein-protein interaction networks as miners of biological discovery

Protein-protein interactions (PPIs) form the basis of a myriad of biological pathways and mechanism, such as the formation of protein complexes or the components of signaling cascades. Here, we reviewed experimental methods for identifying PPI pairs, including yeast two-hybrid (Y2H), mass spectrometry (MS), co-localization, and co-immunoprecipitation. Furthermore, a range of computational methods leveraging biochemical properties, evolution history, protein structures and more have enabled identification of additional PPIs. Given the wealth of known PPIs, we reviewed important network methods to construct and analyze networks of PPIs. These methods aid biological discovery through identifying hub genes and dynamic changes in the network, and have been thoroughly applied in various fields of biological research. Lastly, we discussed the challenges and future direction of research utilizing the power of PPI networks.     

Protein-protein interaction between monomers of coliphage HK022 excisionase

Excisionase (Xis) is an accessory protein that is required for the site-specific excision reaction of the coliphages HK022 and lambda. Xis binds in a strong cooperative manner to two tandem binding sites (X1 and X2) located on the P arm of the attachment (att) sites on the phage genome. As a result of crosslinking experiments in vivo and in vitro of Xis-overexpressing cells, by gel filtration of purified Xis and by FRET analyses we show that Xis monomers of HK022 interact and form dimers that are not dependent on the single Cys residue of the protein and on the presence of DNA. The formation of the dimers may explain the strong binding cooperativity of Xis to its sites on DNA.     

Protein-protein interaction site prediction based on conditional random fields

MOTIVATION: We are motivated by the fast-growing number of protein structures in the Protein Data Bank with necessary information for prediction of protein-protein interaction sites to develop methods for identification of residues participating in protein-protein interactions. We would like to compare conditional random fields (CRFs)-based method with conventional classification-based methods that omit the relation between two labels of neighboring residues to show the advantages of CRFs-based method in predicting protein-protein interaction sites. RESULTS: The prediction of protein-protein interaction sites is solved as a sequential labeling problem by applying CRFs with features including protein sequence profile and residue accessible surface area. The CRFs-based method can achieve a comparable performance with state-of-the-art methods, when 1276 nonredundant hetero-complex protein chains are used as training and test set. Experimental result shows that CRFs-based method is a powerful and robust protein-protein interaction site prediction method and can be used to guide biologists to make specific experiments on proteins. AVAILABILITY: http://www.insun.hit.edu.cn/~mhli/site_CRFs/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.