Protein–Protein and Protein–Membrane Associations in the Lignin Pathway

Supramolecular organization of enzymes is proposed to orchestrate metabolic complexity and help channel intermediates in different pathways. Phenylpropanoid metabolism has to direct up to 30% of the carbon fixed by plants to the biosynthesis of lignin precursors. Effective coupling of the enzymes in the pathway thus seems to be required. Subcellular localization, mobility, protein–protein, and protein–membrane interactions of four consecutive enzymes around the main branch point leading to lignin precursors was investigated in leaf tissues of Nicotiana benthamiana and cells of Arabidopsis thaliana. CYP73A5 and CYP98A3, the two Arabidopsis cytochrome P450s (P450s) catalyzing para- and meta-hydroxylations of the phenolic ring of monolignols were found to colocalize in the endoplasmic reticulum (ER) and to form homo- and heteromers. They moved along with the fast remodeling plant ER, but their lateral diffusion on the ER surface was restricted, likely due to association with other ER proteins. The connecting soluble enzyme hydroxycinnamoyltransferase (HCT), was found partially associated with the ER. Both HCT and the 4-coumaroyl-CoA ligase relocalized closer to the membrane upon P450 expression. Fluorescence lifetime imaging microscopy supports P450 colocalization and interaction with the soluble proteins, enhanced by the expression of the partner proteins. Protein relocalization was further enhanced in tissues undergoing wound repair. CYP98A3 was the most effective in driving protein association.     

Protein–Protein Docking: Past,Present, and Future

The Protein Journal - The biological significance of proteins attracted the scientific community in exploring their characteristics. The studies shed light on the interaction patterns and functions...     

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.     

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.     

Protein kinase Cζ and glucose uptake

Protein kinase Cζ (PKCζ) is a member of the PKC family, serving downstream of insulin receptor and phosphatidylinositol (PI) 3-kinase. Many evidences suggest that PKCζ plays a very important role in activating glucose transport response. Not only insulin but also glucose and exercise can activate PKCζ through diverse pathways. PKCζ activation and activity are impaired with insulin resistance in muscle and adipose tissues of type II diabetes individuals, but heightened in liver tissue, wherein it also increases lipid synthesis mediated by SREBP-1c (sterol-regulatory element-binding protein). Many studies have focused on linkage between PKCζ and GLUT4 translocation and activation. Exploring the molecular mechanisms and pathways by which PKCζ mediates glucose transport will highlight the insulin-signaling pathway. Published in Russian in Biokhimiya, 2006, Vol. 71, No. 7, pp. 869–875. Co-first authors.     

MetaGO: Predicting Gene Ontology of Non-homologous Proteins Through Low-Resolution Protein Structure Prediction and Protein–Protein Network Mapping

Homology-based transferal remains the major approach to computational protein function annotations, but it becomes increasingly unreliable when the sequence identity between query and template decreases below 30%. We propose a novel pipeline, MetaGO, to deduce Gene Ontology attributes of proteins by combining sequence homology-based annotation with low-resolution structure prediction and comparison, and partner's homology-based protein–protein network mapping. The pipeline was tested on a large-scale set of 1000 non-redundant proteins from the CAFA3 experiment. Under the stringent benchmark conditions where templates with > 30% sequence identity to the query are excluded, MetaGO achieves average F-measures of 0.487, 0.408, and 0.598, for Molecular Function, Biological Process, and Cellular Component, respectively, which are significantly higher than those achieved by other state-of-the-art function annotations methods. Detailed data analysis shows that the major advantage of the MetaGO lies in the new functional homolog detections from partner's homology-based network mapping and structure-based local and global structure alignments, the confidence scores of which can be optimally combined through logistic regression. These data demonstrate the power of using a hybrid model incorporating protein structure and interaction networks to deduce new functional insights beyond traditional sequence homology-based referrals, especially for proteins that lack homologous function templates. The MetaGO pipeline is available at http://zhanglab.ccmb.med.umich.edu/MetaGO/.     

Direct Binding and Regulation of RhoA Protein by Cyclic GMP-dependent Protein Kinase Iα

Vascular smooth muscle cell (VSMC) tone is regulated by the state of myosin light chain (MLC) phosphorylation, which is in turn regulated by the balance between MLC kinase and MLC phosphatase (MLCP) activities. RhoA activates Rho kinase, which phosphorylates the regulatory subunit of MLC phosphatase, thereby inhibiting MLC phosphatase activity and increasing contraction and vascular tone. Nitric oxide is an important mediator of VSMC relaxation and vasodilation, which acts by increasing cyclic GMP (cGMP) levels in VSMC, thereby activating cGMP-dependent protein kinase Iα (PKGIα). PKGI is known to phosphorylate Rho kinase, preventing Rho-mediated inhibition of MLC phosphatase, promoting vasorelaxation, although the molecular mechanisms that mediate this are unclear. Here we identify RhoA as a target of activated PKGIα and show further that PKGIα binds directly to RhoA, inhibiting its activation and translocation. In protein pulldown and immunoprecipitation experiments, binding of RhoA and PKGIα was demonstrated via a direct interaction between the amino terminus of RhoA (residues 1–44), containing the switch I domain of RhoA, and the amino terminus of PKGIα (residues 1–59), which includes a leucine zipper heptad repeat motif. Affinity assays using cGMP-immobilized agarose showed that only activated PKGIα binds RhoA, and a leucine zipper mutant PKGIα was unable to bind RhoA even if activated. Furthermore, a catalytically inactive mutant of PKGIα bound RhoA but did not prevent RhoA activation and translocation. Collectively, these results support that RhoA is a PKGIα target and that direct binding of activated PKGIα to RhoA is central to cGMP-mediated inhibition of the VSMC Rho kinase contractile pathway.     

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).     

A Pipeline for Determining Protein–Protein Interactions and Proximities in the Cellular Milieu

It remains extraordinarily challenging to elucidate endogenous protein-protein interactions and proximities within the cellular milieu. The dynamic nature and the large range of affinities of these interactions augment the difficulty of this undertaking. Among the most useful tools for extracting such information are those based on affinity capture of target bait proteins in combination with mass spectrometric readout of the co-isolated species. Although highly enabling, the utility of affinity-based methods is generally limited by difficulties in distinguishing specific from nonspecific interactors, preserving and isolating all unique interactions including those that are weak, transient, or rapidly exchanging, and differentiating proximal interactions from those that are more distal. Here, we have devised and optimized a set of methods to address these challenges. The resulting pipeline involves flash-freezing cells in liquid nitrogen to preserve the cellular environment at the moment of freezing; cryomilling to fracture the frozen cells into intact micron chunks to allow for rapid access of a chemical reagent and to stabilize the intact endogenous subcellular assemblies and interactors upon thawing; and utilizing the high reactivity of glutaraldehyde to achieve sufficiently rapid stabilization at low temperatures to preserve native cellular interactions. In the course of this work, we determined that relatively low molar ratios of glutaraldehyde to reactive amines within the cellular milieu were sufficient to preserve even labile and transient interactions. This mild treatment enables efficient and rapid affinity capture of the protein assemblies of interest under nondenaturing conditions, followed by bottom-up MS to identify and quantify the protein constituents. For convenience, we have termed this approach Stabilized Affinity Capture Mass Spectrometry. Here, we demonstrate that Stabilized Affinity Capture Mass Spectrometry allows us to stabilize and elucidate local, distant, and transient protein interactions within complex cellular milieux, many of which are not observed in the absence of chemical stabilization.Insights into many cellular processes require detailed information about interactions between the participating proteins. However, the analysis of such interactions can be challenging because of the often-diverse physicochemical properties and the abundances of the constituent proteins, as well as the sometimes wide range of affinities and complex dynamics of the interactions. One of the key challenges has been acquiring information concerning transient, low affinity interactions in highly complex cellular milieux (3, 4).Methods that allow elucidation of such information include co-localization microscopy (5), fluorescence protein Förster resonance energy transfer (4), immunoelectron microscopy (5), yeast two-hybrid (6), and affinity capture (7, 8). Among these, affinity capture (AC)¹ has the unique potential to detect all specific in vivo interactions simultaneously, including those that interact both directly and indirectly. In recent times, the efficacy of such affinity isolation experiments has been greatly enhanced through the use of sensitive modern mass spectrometric protein identification techniques (9). Nevertheless, AC suffers from several shortcomings. These include the problem of 1) distinguishing specific from nonspecific interactors (10, 11); 2) preserving and isolating all unique interactions including those that are weak and/or transient, as well as those that exchange rapidly (10, 12, 13); and 3) differentiating proximal from more distant interactions (14).We describe here an approach to address these issues, which makes use of chemical stabilization of protein assemblies in the complex cellular milieu prior to AC. Chemical stabilization is an emerging technique for stabilizing and elucidating protein associations both in vitro (15–20) and in vivo (3, 12, 14, 21–29), with mass spectrometric (MS) readout of the AC proteins and their connectivities. Such chemical stabilization methods are indeed well-established and are often used in electron microscopy for preserving complexes and subcellular structures both in the cellular milieu (3) and in purified complexes (30, 31), wherein the most reliable, stable, and established stabilization reagents is glutaraldehyde. Recently, glutaraldehyde has been applied in the “GraFix” protocol in which purified protein complexes are subjected to centrifugation through a density gradient that also contains a gradient of glutaraldehyde (30, 31), allowing for optimal stabilization of authentic complexes and minimization of nonspecific associations and aggregation. GraFix has also been combined with mass spectrometry on purified complexes bound to EM grids to obtain a compositional analysis of the complexes (32), thereby raising the possibility that glutaraldehyde can be successfully utilized in conjunction with AC in complex cellular milieux directly.In this work, we present a robust pipeline for determining specific protein-protein interactions and proximities from cellular milieux. The first steps of the pipeline involve the well-established techniques of flash freezing the cells of interest in liquid nitrogen and cryomilling, which have been known for over a decade (33, 34) to preserve the cellular environment, as well as having shown outstanding performance when used in analysis of macromolecular interactions in yeast (35–39), bacterial (40, 41), trypanosome (42), mouse (43), and human (44–47) systems. The resulting frozen powder, composed of intact micron chunks of cells that have great surface area and outstanding solvent accessibility, is well suited for rapid low temperature chemical stabilization using glutaraldehyde. We selected glutaraldehyde for our procedure based on the fact that it is a very reactive stabilizing reagent, even at lower temperatures, and because it has already been shown to stabilize enzymes in their functional state (48–50). We employed highly efficient, rapid, single stage affinity capture (36, 51) for isolation and bottom-up MS for analysis of the macromolecular assemblies of interest (52–54). For convenience, we have termed this approach Stabilized Affinity-Capture Mass Spectrometry (SAC-MS).     

Mutations in Nicastrin Protein Differentially Affect Amyloid β-Peptide Production and Notch Protein Processing

The γ-secretase complex is responsible for intramembrane processing of over 60 substrates and is involved in Notch signaling as well as in the generation of the amyloid β-peptide (Aβ). Aggregated forms of Aβ have a pathogenic role in Alzheimer disease and, thus, reducing the Aβ levels by inhibiting γ-secretase is a possible treatment strategy for Alzheimer disease. Regrettably, clinical trials have shown that inhibition of γ-secretase results in Notch-related side effects. Therefore, it is of great importance to find ways to inhibit amyloid precursor protein (APP) processing without disturbing vital signaling pathways such as Notch. Nicastrin (Nct) is part of the γ-secretase complex and has been proposed to be involved in substrate recognition and selection. We have investigated how the four evenly spaced and conserved cysteine residues in the Nct ectodomain affect APP and Notch processing. We mutated these cysteines to serines and analyzed them in cells lacking endogenous Nct. We found that two mutants, C213S (C2) and C230S (C3), differentially affected APP and Notch processing. Both the formation of Aβ and the intracellular domain of amyloid precursor protein (AICD) were reduced, whereas the production of Notch intracellular domain (NICD) was maintained on a high level, although C230S (C3) showed impaired complex assembly. Our data demonstrate that single residues in a γ-secretase component besides presenilin are able to differentially affect APP and Notch processing.     

Protein Components of Bacteriophages λ and λ Virulent

Parallel studies have been made of the protein coats of the temperate bacteriophage λ and of a deletion mutant, λ virulent. A new method for preparing ghosts of both phages by the action of Cu⁺⁺ is described. Protein ghosts of both phages can be dissolved in citrate at pH values below 3, more rapidly in the presence of 8 m urea. Both phages yielded three apparently identical protein components which can be separated by thin-layer gel filtration and thin-layer gel electrophoresis. The protein of molecular weight 47,000 ± 1,500 represents about 55% of the protein of the ghosts and is therefore likely to be the subunit of the head. The other proteins of molecular weight 30,000 ± 1,500 and 16,000 ± 1,500 represent approximately 25% and 20% of the protein, respectively. Amino acid analyses of the ghosts from the two phages have been carried out and show no significant differences. The buoyant density of phage λ virulent is 0.016 g/ml less than that of λ. Since no differences have been found in the protein components of the two phages, this indicates that the virulent mutant contains approximately 16% less deoxyribonucleic acid than the temperate phage.     

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.     

Protein Homeostasis, Aging and Alzheimer’s Disease

Alzheimer's disease (AD) is one key medical challenge of the aging society and despite a great amount of effort and a huge collection of acquired data on molecular mechanisms that are associated with the onset and progression of this devastating disorder, no causal therapy is in sight. The two main hypotheses of AD, the amyloid cascade hypothesis and the Tau hypothesis, are still in the focus of AD research. With aging as the accepted main risk factor of the most important non familial and late onset sporadic forms of AD, it is now mandatory to discuss more intensively aspects of cellular aging and aging biochemistry and its impact on neurodegeneration. Since aging is accompanied by changes in cellular protein homeostasis and an increasing demand for protein degradation, aspects of protein folding, misfolding, refolding and, importantly, protein degradation need to be linked to AD pathogenesis. This is the purpose of this short review.     

Protein Production and Crystallization at SECSG – An Overview

Using a high degree of automation, the Southeast Collaboratory for Structural Genomics (SECSG) has developed high throughput pipelines for protein production, and crystallization using a two-tiered approach. Primary, or tier-1, protein production focuses on producing proteins for members of large Pfam families that lack a representative structure in the Protein Data Bank. Target genomes are Pyrococcus furiosus and Caenorhabditis elegans. Selected human proteins are also under study. Tier-2 protein production, or target rescue, focuses on those tier-1 proteins, which either fail to crystallize or give poorly diffracting crystals. This two tier approach is more efficient since it allows the primary protein production groups to focus on the production of new targets while the tier-2 efforts focus on providing additional sample for further studies and modified protein for structure determination. Both efforts feed the SECSG high throughput crystallization pipeline, which is capable of screening over 40 proteins per week. Details of the various pipelines in use by the SECSG for protein production and crystallization, as well as some examples of target rescue are described.