Interaction between Auxin–binding Protein–I and RNA Polymerase II

Interactions between auxin–binding protein–I (ABP–I), purified from etiolated mung bean seedlings, and nuclear components from mung bean tissues were investigated. When NaCI–solubilized components of chromatin were put on an affinity column of ABP–I–Iinked Sepharose 4B, a small amount of the material was retained on the affinity column and was eluted with 1 M NaCl. RNA polymerase II activity was detected in the eluted fraction. Partially purified RNA polymerase II from mung bean nuclei and purified RNA polymerase II from wheat germ also bound to ABP–I. Indole–3–acetic acid was not necessary for the binding of RNA polymerase II to ABP–I. Acid–denatured ABP–I did not bind to RNA polymerase II from wheat germ. The addition of ABP–I to the reaction mixture for RNA synthesis in vitro caused a stimulation of the activity of wheat germ RNA polymerase.     

A Second-generation Protein–Protein Interaction Network of Helicobacter pylori

Helicobacter pylori infections cause gastric ulcers and play a major role in the development of gastric cancer. In 2001, the first protein interactome was published for this species, revealing over 1500 binary protein interactions resulting from 261 yeast two-hybrid screens. Here we roughly double the number of previously published interactions using an ORFeome-based, proteome-wide yeast two-hybrid screening strategy. We identified a total of 1515 protein–protein interactions, of which 1461 are new. The integration of all the interactions reported in H. pylori results in 3004 unique interactions that connect about 70% of its proteome. Excluding interactions of promiscuous proteins we derived from our new data a core network consisting of 908 interactions. We compared our data set to several other bacterial interactomes and experimentally benchmarked the conservation of interactions using 365 protein pairs (interologs) of E. coli of which one third turned out to be conserved in both species.Helicobacter pylori is a Gram-negative, microaerophilic bacterium that colonizes the stomach, an unusual highly acidic niche for microorganisms. In 1983, Warren and Marshall found it to be associated with gastric inflammation and duodenal ulcer disease (1, 2). A chronic infection with H. pylori can lead to development of stomach carcinoma and MALT lymphoma (reviewed in (3)). Hence, the World Health Organization has classified H. pylori as a class I carcinogen (4). It is estimated that half of the world′s population harbors H. pylori but with large variations in the geographical and socioeconomic distribution while causing annually 700,000 deaths worldwide (reviewed in (5)).The pathogenesis of H. pylori has been extensively studied, including the effector CagA, cytotoxin VacA, its adhesins and urease (reviewed in (3, 5–7)). The latter allows the bacterium to neutralize the stomach acid through ammonia production. However, H. pylori is not a classical model organism and thus many gaps in our knowledge still exist.The genome of H. pylori reference strain 26695 was completely sequenced in 1997 (8) and encodes 1587 proteins of which about 950 (61%) have been assigned functions (excluding “putatives”; Uniprot, CMR (9)). These numbers indicate that a large fraction of the proteins of H. pylori has not been functionally characterized.Protein–protein interactions (PPIs)¹ are required for nearly all biological processes. Unbiased interactomes are helpful to understand proteins or pathways and how they are linking poorly or uncharacterized proteins via their interactions. For instance, our study of the Treponema pallidum interactome (10) has led to the characterization of several previously “unknown” proteins such as YbeB, a ribosomal silencing factor (11), or TP0658, a regulator of flagellar translation and assembly (12, 13). However, only a few other comprehensive bacterial interactome studies have been published to date, including Campylobacter jejuni (14), Synechocystis sp. (15), Mycobacterium tuberculosis (16), Mesorhizobium loti (17), and recently Escherichia coli (18). In addition, partial interactomes are available for Bacillus subtilis (19) and H. pylori (20). Most of them used the yeast two-hybrid (Y2H) screening technology (21) which allows the pairwise detection of PPIs. Furthermore, a few other studies (22–25) systematically identified protein complexes and their compositions in bacteria.In 2001, Rain and colleagues have established a partial interactome of H. pylori, the first published protein interaction network of a bacterium (20). In this study, 261 bait constructs were screened against a random prey pool library resulting in the detection of over 1500 PPIs. Although this network likely represents a small fraction of all PPIs that occur in H. pylori, many downstream studies were motivated by these results (see below).Recent studies have disproved the notion that Y2H data sets are of poor quality (26, 27). Similarly, a high false-negative rate can be avoided by multiple Y2H expression vector systems (28–30) or protein fragments as opposed to full-length constructs (31). The aim of this study was to systematically screen the H. pylori proteome for binary protein interactions using a complementary approach to that of Rain et al. to produce an extended protein–protein interaction map of H. pylori. As a result, we have roughly doubled the number of known binary protein–protein interactions for H. pylori in this study.     

Computational Prediction of Protein–Protein Interaction Networks: Algo-rithms and Resources

Protein interactions play an important role in the discovery of protein functions and pathways in biological processes. This is especially true in case of the diseases caused by the loss of specific protein-protein interactions in the organism. The accuracy of experimental results in finding protein-protein interactions, however, is rather dubious and high throughput experimental results have shown both high false positive beside false negative information for protein interaction. Computational methods have attracted tremendous attention among biologists because of the ability to predict protein-protein interactions and validate the obtained experimental results. In this study, we have reviewed several computational methods for protein-protein interaction prediction as well as describing major databases, which store both predicted and detected protein-protein interactions, and the tools used for analyzing protein interaction networks and improving protein-protein interaction reliability.     

Dynamic Circadian Protein–Protein Interaction Networks Predict Temporal Organization of Cellular Functions

Essentially all biological processes depend on protein–protein interactions (PPIs). Timing of such interactions is crucial for regulatory function. Although circadian (∼24-hour) clocks constitute fundamental cellular timing mechanisms regulating important physiological processes, PPI dynamics on this timescale are largely unknown. Here, we identified 109 novel PPIs among circadian clock proteins via a yeast-two-hybrid approach. Among them, the interaction of protein phosphatase 1 and CLOCK/BMAL1 was found to result in BMAL1 destabilization. We constructed a dynamic circadian PPI network predicting the PPI timing using circadian expression data. Systematic circadian phenotyping (RNAi and overexpression) suggests a crucial role for components involved in dynamic interactions. Systems analysis of a global dynamic network in liver revealed that interacting proteins are expressed at similar times likely to restrict regulatory interactions to specific phases. Moreover, we predict that circadian PPIs dynamically connect many important cellular processes (signal transduction, cell cycle, etc.) contributing to temporal organization of cellular physiology in an unprecedented manner.     

Reconstructing Genome-Wide Protein–Protein Interaction Networks Using Multiple Strategies with Homologous Mapping

Background

One of the crucial steps toward understanding the biological functions of a cellular system is to investigate protein–protein interaction (PPI) networks. As an increasing number of reliable PPIs become available, there is a growing need for discovering PPIs to reconstruct PPI networks of interesting organisms. Some interolog-based methods and homologous PPI families have been proposed for predicting PPIs from the known PPIs of source organisms.

Results

Here, we propose a multiple-strategy scoring method to identify reliable PPIs for reconstructing the mouse PPI network from two well-known organisms: human and fly. We firstly identified the PPI candidates of target organisms based on homologous PPIs, sharing significant sequence similarities (joint E-value ≤ 1 × 10⁻⁴⁰), from source organisms using generalized interolog mapping. These PPI candidates were evaluated by our multiple-strategy scoring method, combining sequence similarities, normalized ranks, and conservation scores across multiple organisms. According to 106,825 PPI candidates in yeast derived from human and fly, our scoring method can achieve high prediction accuracy and outperform generalized interolog mapping. Experiment results show that our multiple-strategy score can avoid the influence of the protein family size and length to significantly improve PPI prediction accuracy and reflect the biological functions. In addition, the top-ranked and conserved PPIs are often orthologous/essential interactions and share the functional similarity. Based on these reliable predicted PPIs, we reconstructed a comprehensive mouse PPI network, which is a scale-free network and can reflect the biological functions and high connectivity of 292 KEGG modules, including 216 pathways and 76 structural complexes.

Conclusions

Experimental results show that our scoring method can improve the predicting accuracy based on the normalized rank and evolutionary conservation from multiple organisms. Our predicted PPIs share similar biological processes and cellular components, and the reconstructed genome-wide PPI network can reflect network topology and modularity. We believe that our method is useful for inferring reliable PPIs and reconstructing a comprehensive PPI network of an interesting organism.     

Mapping Plant Interactomes Using Literature Curated and Predicted Protein–Protein Interaction Data Sets

Most cellular processes are enabled by cohorts of interacting proteins that form dynamic networks within the plant proteome. The study of these networks can provide insight into protein function and provide new avenues for research. This article informs the plant science community of the currently available sources of protein interaction data and discusses how they can be useful to researchers. Using our recently curated IntAct Arabidopsis thaliana protein–protein interaction data set as an example, we discuss potentials and limitations of the plant interactomes generated to date. In addition, we present our efforts to add value to the interaction data by using them to seed a proteome-wide map of predicted protein subcellular locations.For well over two decades, plant scientists have studied protein interactions within plants using many different and evolving approaches. Their findings are represented by a large and growing corpus of peer-reviewed literature reflecting the increasing activity in this area of plant proteomic research. More recently, a number of predicted interactomes have been reported in plants and, while these predictions remain largely untested, they could act as a useful guide for future research. These studies have allowed researchers to better understand the function of protein complexes and to refine our understanding of protein function within the cell (Uhrig, 2006; Morsy et al., 2008). The extraction of protein interaction data from the literature and its standardized deposition and representation within publicly available databases remains a challenging task. Aggregating the data in databases allows researchers to leverage visualization, data mining, and integrative approaches to produce new insights that would be unachievable when the data are dispersed within largely inaccessible formats (Rodriguez et al., 2009).Currently, there are three databases that act as repositories of plant protein interaction data. These are IntAct (http://www.ebi.ac.uk/intact/; Aranda et al., 2010), The Arabidopsis Information Resource (TAIR; http://www.Arabidopsis.org/; Poole, 2007), and BioGRID (http://www.thebiogrid.org/; Breitkreutz et al., 2008). These databases curate experimentally established interactions available from the peer-reviewed literature (as opposed to predicted interactions, which will be discussed below). Each repository takes its own approach to the capture, storage, and representation of protein interaction data. TAIR focuses on Arabidopsis thaliana protein–protein interaction data exclusively; BioGRID currently focuses on the plant species Arabidopsis and rice (Oryza sativa), while IntAct attempts to capture protein interaction data from any plant species. Unlike the other repositories, IntAct follows a deep curation strategy that captures detailed experimental and biophysical details, such as binding regions and subcellular locations of interactions using controlled vocabularies (Aranda et al., 2010). While the majority of plant interaction data held by IntAct concern protein–protein interaction data in Arabidopsis, there is a small but growing content of interaction data relating to protein–DNA, protein–RNA, and protein–small molecule interactions, as well as interaction data from other plant species.Using the IntAct Arabidopsis data set as an example, we outline how the accumulating knowledge captured in these repositories can be used to further our understanding of the plant proteome. We compare the characteristics of predicted interactomes with the IntAct protein–protein interaction data set, which consists entirely of experimentally measured protein interactions, to gauge the predictive accuracy of these studies. Finally, we show how the IntAct data set can be used together with a recently developed Divide and Conquer k-Nearest Neighbors Method (DC-kNN; K. Lee et al., 2008) to predict the subcellular locations for most Arabidopsis proteins. This data set predicts high confidence subcellular locations for many unannotated Arabidopsis proteins and should act as a useful resource for future studies of protein function. Although this article focuses on the IntAct Arabidopsis protein–protein interaction data set, readers are also encouraged to explore the resources offered by our colleagues at TAIR and BioGRID.Each database employs its own system to report molecular interactions, as represented in the referenced source publications, and each avoids making judgments on interaction reliability or whether two participants in a complex have a direct interaction. Thus, the user should carefully filter these data sets for their specific purpose based on the full annotation of the data sets. In particular, the user should consider the experimental methods and independent observation of the same interaction in different publications when assessing the reliability and type of interaction of the proteins (e.g., direct or indirect). Confidence scoring schemes for interaction data are discussed widely in the literature (Yu and Finley, 2009).     

7 The Molecular Biology of Tryptophan Synthase: A Model for Protein–Protein Interaction

Abstract

The use of plastic produced from non-renewable resources constitutes a major environmental problem of the modern society. Polylactide polymers (PLA) have recently gained enormous attention as one possible substitution of petroleum derived polymers. A prerequisite for high quality PLA production is the provision of optically pure lactic acid, which cannot be obtained by chemical synthesis in an economical way. Microbial fermentation is therefore the commercial option to obtain lactic acid as monomer for PLA production. However, one major economic hurdle for commercial lactic acid production as basis for PLA is the costly separation procedure, which is needed to recover and purify the product from the fermentation broth. Yeasts, such as Saccharomyces cerevisiae (bakers yeast) offer themselves as production organisms because they can tolerate low pH and grow on mineral media what eases the purification of the acid. However, naturally yeasts do not produce lactic acid. By metabolic engineering, ethanol was exchanged with lactic acid as end product of fermentation. A vast amount of effort has been invested into the development of yeasts for lactic acid production since the first paper on this topic by Dequin and process insight. If pH stress is used as basis for DNA microarray analyses, in order to improve the host, what exactly is addressed? Growth? Or productivity? They might be connected, but can be negatively correlated. A better growing strain might not be a better producer. So if the question was growth, the answer might not be what was initially intended (productivity).

A major task for the future is to learn to ask the right questions – a lot of studies intended to lead to better productivity, did lead to interesting results, but NOT to better production strains.

Taking together what we learned from lactic acid production with yeasts, we see a bright future for bulk and fine chemical production with these versatile hosts.     

Peptide, Peptidomimetic, and Small-molecule Antagonists of the p53–HDM2 Protein–Protein Interaction

Modulation of intracellular protein–protein interactions has been – and remains – a challenging goal for the discovery and development of small-molecule therapeutic agents. Progress in the pharmacological targeting and understanding at the molecular level of one such interaction that is relevant to cancer drug research, viz. that between the tumour suppressor protein p53 and its negative regulator HDM2, is reviewed here. The first X-ray crystal structure of a complex between a small peptide from the trans-activation domain of p53 and the N-terminal domain of HDM2 was reported almost 10 years ago. The nature of this interaction, which involves just three residue side chains in the p53 peptide ligand and a compact hydrophobic binding pocket in the HDM2 receptor, together with the attractive concept of reactivating the anti-proliferative functions of p53 in tumour cells, has spurned a great deal of effort aimed at finding drug-like antagonists of this interaction. A variety of approaches, including both structure-guided peptidomimetic and de novo design, as well as high through-put screening campaigns, have provided a wealth of leads that might be turned into actual drugs. There is still some way to go as far as optimisation and preclinical development of such leads is concerned, but it is clear already now that antagonists of the p53–HDM2 protein–protein interaction have a good chance of ultimately being successful in providing a new anti-cancer therapy modality, both in monotherapy and to potentiate the effectiveness of existing chemotherapies.     

Cystatin B and SOD1: Protein–Protein Interaction and Possible Relation to Neurodegeneration

Cystatin B (CSTB), an inhibitor of the cysteine proteases, belongs to the cathepsin family and it is known to interact with a number of proteins involved in cytoskeletal organization. CSTB has an intrinsic tendency to form aggregates depending on the redox environment. The gene encoding for CSTB is frequently mutated in association with the rare neurodegenerative condition progressive myoclonus epilepsy. Increased levels of CSTB have been observed in the spinal cord of transgenic mice modeling SOD1-linked familial amyotrophic lateral sclerosis, a fatal neurodegenerative disease affecting motoneurons. In the present study, we have investigated the relationship occurring between the expression of SOD1 and CSTB either wild-type or double-cysteine substitution mutant (Cys 3 and Cys 64). Whether or not there is a physical interaction between the two proteins was also investigated in overexpression experiments using a human neuroblastoma cell line and mouse-immortalized motoneurons. Here we report evidences for a reciprocal influence of CSTB and SOD1 at the gene expression level and for a direct interaction of the two proteins.     

Assessing Coverage of Protein Interaction Data Using Capture–Recapture Models

Protein interaction networks comprise thousands of individual binary links between distinct proteins. Whilst these data have attracted considerable attention and been the focus of many different studies, the networks, their structure, function, and how they change over time are still not fully known. More importantly, there is still considerable uncertainty regarding their size, and the quality of the available data continues to be questioned. Here, we employ statistical models of the experimental sampling process, in particular capture–recapture methods, in order to assess the false discovery rate and size of protein interaction networks. We uses these methods to gauge the ability of different experimental systems to find the true binary interactome. Our model allows us to obtain estimates for the size and false-discovery rate from simple considerations regarding the number of repeatedly interactions, and provides suggestions as to how we can exploit this information in order to reduce the effects of noise in such data. In particular our approach does not require a reference dataset. We estimate that approximately more than half of the true physical interactome has now been sampled in yeast.     

Prediction of Protein–Protein Interaction Sites in Sequences and 3D Structures by Random Forests

Identifying interaction sites in proteins provides important clues to the function of a protein and is becoming increasingly relevant in topics such as systems biology and drug discovery. Although there are numerous papers on the prediction of interaction sites using information derived from structure, there are only a few case reports on the prediction of interaction residues based solely on protein sequence. Here, a sliding window approach is combined with the Random Forests method to predict protein interaction sites using (i) a combination of sequence- and structure-derived parameters and (ii) sequence information alone. For sequence-based prediction we achieved a precision of 84% with a 26% recall and an F-measure of 40%. When combined with structural information, the prediction performance increases to a precision of 76% and a recall of 38% with an F-measure of 51%. We also present an attempt to rationalize the sliding window size and demonstrate that a nine-residue window is the most suitable for predictor construction. Finally, we demonstrate the applicability of our prediction methods by modeling the Ras–Raf complex using predicted interaction sites as target binding interfaces. Our results suggest that it is possible to predict protein interaction sites with quite a high accuracy using only sequence information.     

Context-Dependent Remodeling of Rad51–DNA Complexes by Srs2 Is Mediated by a Specific Protein–Protein Interaction

The yeast Srs2 helicase removes Rad51 nucleoprotein filaments from single-stranded DNA (ssDNA), preventing DNA strand invasion and exchange by homologous recombination. This activity requires a physical interaction between Srs2 and Rad51, which stimulates ATP turnover in the Rad51 nucleoprotein filament and causes dissociation of Rad51 from ssDNA. Srs2 also possesses a DNA unwinding activity and here we show that assembly of more than one Srs2 molecule on the 3′ ssDNA overhang is required to initiate DNA unwinding. When Rad51 is bound on the double-stranded DNA, its interaction with Srs2 blocks the helicase (DNA unwinding) activity of Srs2. Thus, in different DNA contexts, the physical interaction of Rad51 with Srs2 can either stimulate or inhibit the remodeling functions of Srs2, providing a means for tailoring DNA strand exchange activities to enhance the fidelity of recombination.