A versatile vector for mycobacterial protein production with a functional minimized acetamidase regulon

Recombinant protein expression is a prerequisite for diverse investigations of proteins at the molecular level. For targets from Mycobacterium tuberculosis it is favorable to use M. smegmatis as an expression host, a species from the same genus. In the respective shuttle vectors, target gene expression is controlled by the complex tetra‐cistronic acetamidase regulon. As a result, the size of those vectors is large, rendering them of limited use, especially when the target proteins are expressed from multi‐cistronic operons. Therefore, in the current work we present a versatile new expression vector in which the acetamidase regulon has been minimized by deleting the two genes amiD and amiS. We assessed the functional properties of the resulting vector pMyCA and compared it with those of the existing vector pMyNT that contains the full‐length acetamidase regulon. We analyzed the growth features and protein expression patterns of M. smegmatis cultures transformed with both vectors. In addition, we created mCherry expression constructs to spectroscopically monitor the expression properties of both vectors. Our experiments showed that the minimized vector exhibited several advantages over the pMyNT vector. First, the overall yield of expressed protein is higher due to the higher yield of bacterial mass. Second, the heterologous expression was regulated more tightly, offering an expression tool for diverse target proteins. Third, it is suitable for large multi‐protein complexes that are expressed from multi‐cistronic operons. Additionally, our results propose a new understanding of the regulation mechanism of the acetamidase regulon with the potential to construct more optimized vectors in the future.     

Positioning of chemosensory proteins and FtsZ through the Rhodobacter sphaeroides cell cycle

Bacterial chemotaxis depends on signalling through large protein complexes. Each cell must inherit a complex on division, suggesting some co‐ordination with cell division. In Escherichia coli the membrane‐spanning chemosensory complexes are polar and new static complexes form at pre‐cytokinetic sites, ensuring positioning at the new pole after division and suggesting a role for the bacterial cytoskeleton. Rhodobacter sphaeroides has both membrane‐associated and cytoplasmic, chromosome‐associated chemosensory complexes. We followed the relative positions of the two chemosensory complexes, FtsZ and MreB in aerobic and in photoheterotrophic R. sphaeroides cells using fluorescence microscopy. FtsZ forms polar spots after cytokinesis, which redistribute to the midcell forming nodes from which FtsZ extends circumferentially to form the Z‐ring. Membrane‐associated chemosensory proteins form a number of dynamic unit‐clusters with mature clusters containing about 1000 CheW₃ proteins. Individual clusters diffuse randomly within the membrane, accumulating at new poles after division but not colocalizing with either FtsZ or MreB. The cytoplasmic complex colocalizes with FtsZ at midcells in new‐born cells. Before cytokinesis one complex moves to a daughter cell, followed by the second moving to the other cell. These data indicate that two homologous complexes use different mechanisms to ensure partitioning, and neither complex utilizes FtsZ or MreB for positioning.     

An integrated workflow for charting the human interaction proteome: insights into the PP2A system

Protein complexes represent major functional units for the execution of biological processes. Systematic affinity purification coupled with mass spectrometry (AP‐MS) yielded a wealth of information on the compendium of protein complexes expressed in Saccharomyces cerevisiae. However, global AP‐MS analysis of human protein complexes is hampered by the low throughput, sensitivity and data robustness of existing procedures, which limit its application for systems biology research. Here, we address these limitations by a novel integrated method, which we applied and benchmarked for the human protein phosphatase 2A system. We identified a total of 197 protein interactions with high reproducibility, showing the coexistence of distinct classes of phosphatase complexes that are linked to proteins implicated in mitosis, cell signalling, DNA damage control and more. These results show that the presented analytical process will substantially advance throughput and reproducibility in future systematic AP‐MS studies on human protein complexes.     

Detergent addition to tryptic digests and ion mobility separation prior to MS/MS improves peptide yield and protein identification for in situ proteomic investigation of frozen and formalin‐fixed paraffin‐embedded adenocarcinoma tissue sections

The identification of proteins involved in tumour progression or which permit enhanced or novel therapeutic targeting is essential for cancer research. Direct MALDI analysis of tissue sections is rapidly demonstrating its potential for protein imaging and profiling in the investigation of a range of disease states including cancer. MALDI‐mass spectrometry imaging (MALDI‐MSI) has been used here for direct visualisation and in situ characterisation of proteins in breast tumour tissue section samples. Frozen MCF7 breast tumour xenograft and human formalin‐fixed paraffin‐embedded breast cancer tissue sections were used. An improved protocol for on‐tissue trypsin digestion is described incorporating the use of a detergent, which increases the yield of tryptic peptides for both fresh frozen and formalin‐fixed paraffin‐embedded tumour tissue sections. A novel approach combining MALDI‐MSI and ion mobility separation MALDI‐tandem mass spectrometry imaging for improving the detection of low‐abundance proteins that are difficult to detect by direct MALDI‐MSI analysis is described. In situ protein identification was carried out directly from the tissue section by MALDI‐MSI. Numerous protein signals were detected and some proteins including histone H3, H4 and Grp75 that were abundant in the tumour region were identified.     

A framework for mapping,visualisation and automatic model creation of signal‐transduction networks

Intracellular signalling systems are highly complex. This complexity makes handling, analysis and visualisation of available knowledge a major challenge in current signalling research. Here, we present a novel framework for mapping signal‐transduction networks that avoids the combinatorial explosion by breaking down the network in reaction and contingency information. It provides two new visualisation methods and automatic export to mathematical models. We use this framework to compile the presently most comprehensive map of the yeast MAP kinase network. Our method improves previous strategies by combining (I) more concise mapping adapted to empirical data, (II) individual referencing for each piece of information, (III) visualisation without simplifications or added uncertainty, (IV) automatic visualisation in multiple formats, (V) automatic export to mathematical models and (VI) compatibility with established formats. The framework is supported by an open source software tool that facilitates integration of the three levels of network analysis: definition, visualisation and mathematical modelling. The framework is species independent and we expect that it will have wider impact in signalling research on any system.     

Fabp4‐CreER lineage tracing revealstwo distinctive coronary vascular populations

Over the last two decades, genetic lineage tracing has allowed for the elucidation of the cellular origins and fates during both embryogenesis and in pathological settings in adults. Recent lineage tracing studies using Apln‐CreER tool indicated that a large number of post‐natal coronary vessels do not form from pre‐existing vessels. Instead, they form de novo after birth, which represents a coronary vascular population (CVP) distinct from the pre‐existing one. Herein, we present new coronary vasculature lineage tracing results using a novel tool, Fabp4‐CreER. Our results confirm the distinct existence of two unique CVPs. The 1^st CVP, which is labelled by Fabp4‐CreER, arises through angiogenic sprouting of pre‐existing vessels established during early embryogenesis. The 2^nd CVP is not labelled by Fabp4, suggesting that these vessels form de novo, rather than through expansion of the 1^st CVP. These results support the de novo formation of vessels in the post‐natal heart, which has implications for studies in cardiovascular disease and heart regeneration.     

Pitfalls of hypothesis tests and model selection on bootstrap samples: Causes and consequences in biometrical applications

The bootstrap method has become a widely used tool applied in diverse areas where results based on asymptotic theory are scarce. It can be applied, for example, for assessing the variance of a statistic, a quantile of interest or for significance testing by resampling from the null hypothesis. Recently, some approaches have been proposed in the biometrical field where hypothesis testing or model selection is performed on a bootstrap sample as if it were the original sample. P‐values computed from bootstrap samples have been used, for example, in the statistics and bioinformatics literature for ranking genes with respect to their differential expression, for estimating the variability of p‐values and for model stability investigations. Procedures which make use of bootstrapped information criteria are often applied in model stability investigations and model averaging approaches as well as when estimating the error of model selection procedures which involve tuning parameters. From the literature, however, there is evidence that p‐values and model selection criteria evaluated on bootstrap data sets do not represent what would be obtained on the original data or new data drawn from the overall population. We explain the reasons for this and, through the use of a real data set and simulations, we assess the practical impact on procedures relevant to biometrical applications in cases where it has not yet been studied. Moreover, we investigate the behavior of subsampling (i.e., drawing from a data set without replacement) as a potential alternative solution to the bootstrap for these procedures.     

CRK: An evolutionary approach for distinguishing biologically relevant interfaces from crystal contacts

Protein crystals contain two different types of interfaces: biologically relevant ones, observed in protein–protein complexes and oligomeric proteins, and nonspecific ones, corresponding to crystal lattice contacts. Because of the increasing complexity of the objects being tackled in structural biology, distinguishing biological contacts from crystal contacts is not always a trivial task and can lead to wrong interpretation of macromolecular structures. We devised an approach (CRK, core‐rim K_a/K_s ratio) for distinguishing biologically relevant interfaces from nonspecific ones. Given a protein–protein interface, CRK finds a set of homologs to the sequences of the proteins involved in the interface, retrieves and aligns the corresponding coding sequences, on which it carries out a residue‐by‐residue K_a/K_s ratio (ω) calculation. It divides interface residues into a “rim” and a “core” set and analyzes the selection pressure on the residues belonging to the two sets. We developed and tested CRK on different datasets and test cases, consisting of biologically relevant contacts, nonspecific ones or of both types. The method proves very effective in distinguishing the two categories of interfaces, with an overall accuracy rate of 84%. As it relies on different principles when compared with existing tools, CRK is optimally suited to be used in combination with them. In addition, CRK has potential applications in the validation of structures of oligomeric proteins and protein complexes. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

In planta chemical cross‐linking and mass spectrometry analysis of protein structure and interaction in Arabidopsis

Site‐specific chemical cross‐linking in combination with mass spectrometry analysis has emerged as a powerful proteomic approach for studying the three‐dimensional structure of protein complexes and in mapping protein–protein interactions (PPIs). Building on the success of MS analysis of in vitro cross‐linked proteins, which has been widely used to investigate specific interactions of bait proteins and their targets in various organisms, we report a workflow for in vivo chemical cross‐linking and MS analysis in a multicellular eukaryote. This approach optimizes the in vivo protein cross‐linking conditions in Arabidopsis thaliana, establishes a MudPIT procedure for the enrichment of cross‐linked peptides, and develops an integrated software program, exhaustive cross‐linked peptides identification tool (ECL), to identify the MS spectra of in planta chemical cross‐linked peptides. In total, two pairs of in vivo cross‐linked peptides of high confidence have been identified from two independent biological replicates. This work demarks the beginning of an alternative proteomic approach in the study of in vivo protein tertiary structure and PPIs in multicellular eukaryotes.     

Feature selection and classification of protein–protein complexes based on their binding affinities using machine learning approaches

Protein–protein interactions are intrinsic to virtually every cellular process. Predicting the binding affinity of protein–protein complexes is one of the challenging problems in computational and molecular biology. In this work, we related sequence features of protein–protein complexes with their binding affinities using machine learning approaches. We set up a database of 185 protein–protein complexes for which the interacting pairs are heterodimers and their experimental binding affinities are available. On the other hand, we have developed a set of 610 features from the sequences of protein complexes and utilized Ranker search method, which is the combination of Attribute evaluator and Ranker method for selecting specific features. We have analyzed several machine learning algorithms to discriminate protein‐protein complexes into high and low affinity groups based on their K_d values. Our results showed a 10‐fold cross‐validation accuracy of 76.1% with the combination of nine features using support vector machines. Further, we observed accuracy of 83.3% on an independent test set of 30 complexes. We suggest that our method would serve as an effective tool for identifying the interacting partners in protein–protein interaction networks and human–pathogen interactions based on the strength of interactions. Proteins 2014; 82:2088–2096. © 2014 Wiley Periodicals, Inc.     

Accurate measurements of dynamics and reproducibility in small genetic networks

Quantification of gene expression has become a central tool for understanding genetic networks. In many systems, the only viable way to measure protein levels is by immunofluorescence, which is notorious for its limited accuracy. Using the early Drosophila embryo as an example, we show that careful identification and control of experimental error allows for highly accurate gene expression measurements. We generated antibodies in different host species, allowing for simultaneous staining of four Drosophila gap genes in individual embryos. Careful error analysis of hundreds of expression profiles reveals that less than ～20% of the observed embryo‐to‐embryo fluctuations stem from experimental error. These measurements make it possible to extract not only very accurate mean gene expression profiles but also their naturally occurring fluctuations of biological origin and corresponding cross‐correlations. We use this analysis to extract gap gene profile dynamics with ～1 min accuracy. The combination of these new measurements and analysis techniques reveals a twofold increase in profile reproducibility owing to a collective network dynamics that relays positional accuracy from the maternal gradients to the pair‐rule genes.     

Unraveling the dynamics of protein interactions with quantitative mass spectrometry

Knowledge of structure and dynamics of proteins and protein complexes is important to unveil the molecular basis and mechanisms involved in most biological processes. Protein complex dynamics can be defined as the changes in the composition of a protein complex during a cellular process. Protein dynamics can be defined as conformational changes in a protein during enzyme activation, for example, when a protein binds to a ligand or when a protein binds to another protein. Mass spectrometry (MS) combined with affinity purification has become the analytical tool of choice for mapping protein–protein interaction networks and the recent developments in the quantitative proteomics field has made it possible to identify dynamically interacting proteins. Furthermore, hydrogen/deuterium exchange MS is emerging as a powerful technique to study structure and conformational dynamics of proteins or protein assemblies in solution. Methods have been developed and applied for the identification of transient and/or weak dynamic interaction partners and for the analysis of conformational dynamics of proteins or protein complexes. This review is an overview of existing and recent developments in studying the overall dynamics of in vivo protein interaction networks and protein complexes using MS-based methods.     

Computational alanine scanning with linear scaling semiempirical quantum mechanical methods

Alanine scanning is a powerful experimental tool for understanding the key interactions in protein–protein interfaces. Linear scaling semiempirical quantum mechanical calculations are now sufficiently fast and robust to allow meaningful calculations on large systems such as proteins, RNA and DNA. In particular, they have proven useful in understanding protein–ligand interactions. Here we ask the question: can these linear scaling quantum mechanical methods developed for protein–ligand scoring be useful for computational alanine scanning? To answer this question, we assembled 15 protein–protein complexes with available crystal structures and sufficient alanine scanning data. In all, the data set contains ΔΔGs for 400 single point alanine mutations of these 15 complexes. We show that with only one adjusted parameter the quantum mechanics‐based methods outperform both buried accessible surface area and a potential of mean force and compare favorably to a variety of published empirical methods. Finally, we closely examined the outliers in the data set and discuss some of the challenges that arise from this examination. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

An oligogalacturonide‐derived molecular probe demonstrates the dynamics of calcium‐mediated pectin complexation in cell walls of tip‐growing structures

Pectic homogalacturonan (HG) is one of the main constituents of plant cell walls. When processed to low degrees of esterification, HG can form complexes with divalent calcium ions. These macromolecular structures (also called egg boxes) play an important role in determining the biomechanics of cell walls and in mediating cell‐to‐cell adhesion. Current immunological methods enable only steady‐state detection of egg box formation in situ. Here we present a tool for efficient real‐time visualisation of available sites for HG crosslinking within cell wall microdomains. Our approach is based on calcium‐mediated binding of fluorescently tagged long oligogalacturonides (OGs) with endogenous de‐esterified HG. We established that more than seven galacturonic acid residues in the HG chain are required to form a stable complex with endogenous HG through calcium complexation in situ, confirming a recently suggested thermodynamic model. Using defined carbohydrate microarrays, we show that the long OG probe binds exclusively to HG that has a very low degree of esterification and in the presence of divalent ions. We used this probe to study real‐time dynamics of HG during elongation of Arabidopsis pollen tubes and root hairs. Our results suggest a different spatial organisation of incorporation and processing of HG in the cell walls of these two tip‐growing structures.     

Genetic engineering of thylakoid protein complexes by chloroplast transformation in Chlamydomonas reinhardtii

Chloroplast transformation of Chlamydomonas reinhardtii has developed into a powerful tool for studying the structure, function and assembly of thylakoid protein complexes in a eukaryotic organism. In this article we review the progress that is being made in the development of procedures for efficient chloroplast transformation. This focuses on the development of selectable markers and the use of Chlamydomonas mutants, individually lacking thylakoid protein complexes, as recipients. Chloroplast transformation has now been used to engineer all four major thylakoid protein complexes, photosystem II, photosystem I, cytochrome b ₆/f and ATP synthase. These results are discussed with an emphasis on new insights into assembly and function of these complexes in chloroplasts as compared with their prokaryotic counterparts.Abbreviations ENDOR electron nuclear double resonance - ESEEM electron spin echo envelope modulation - LHC light harvesting complex - PSI Photosystem I - PS II Photosystem II - P₆₈₀ primary electron donor in PS II - P₇₀₀ primary electron donor in PS I     

A Novel Core‐Attachment–Based Method to Identify Dynamic Protein Complexes Based on Gene Expression Profiles and PPI Networks

Cellular functions are always performed by protein complexes. At present, many approaches have been proposed to identify protein complexes from protein–protein interaction (PPI) networks. Some approaches focus on detecting local dense subgraphs in PPI networks which are regarded as protein‐complex cores, then identify protein complexes by including local neighbors. However, from gene expression profiles at different time points or tissues it is known that proteins are dynamic. Therefore, identifying dynamic protein complexes should become very important and meaningful. In this study, a novel core‐attachment–based method named CO‐DPC to detect dynamic protein complexes is presented. First, CO‐DPC selects active proteins according to gene expression profiles and the 3‐sigma principle, and constructs dynamic PPI networks based on the co‐expression principle and PPI networks. Second, CO‐DPC detects local dense subgraphs as the cores of protein complexes and then attach close neighbors of these cores to form protein complexes. In order to evaluate the method, the method and the existing algorithms are applied to yeast PPI networks. The experimental results show that CO‐DPC performs much better than the existing methods. In addition, the identified dynamic protein complexes can match very well and thus become more meaningful for future biological study.     

CPredictor3.0: detecting protein complexes from PPI networks with expression data and functional annotations

Background

Effectively predicting protein complexes not only helps to understand the structures and functions of proteins and their complexes, but also is useful for diagnosing disease and developing new drugs. Up to now, many methods have been developed to detect complexes by mining dense subgraphs from static protein-protein interaction (PPI) networks, while ignoring the value of other biological information and the dynamic properties of cellular systems.

Results

In this paper, based on our previous works CPredictor and CPredictor2.0, we present a new method for predicting complexes from PPI networks with both gene expression data and protein functional annotations, which is called CPredictor3.0. This new method follows the viewpoint that proteins in the same complex should roughly have similar functions and are active at the same time and place in cellular systems. We first detect active proteins by using gene express data of different time points and cluster proteins by using gene ontology (GO) functional annotations, respectively. Then, for each time point, we do set intersections with one set corresponding to active proteins generated from expression data and the other set corresponding to a protein cluster generated from functional annotations. Each resulting unique set indicates a cluster of proteins that have similar function(s) and are active at that time point. Following that, we map each cluster of active proteins of similar function onto a static PPI network, and get a series of induced connected subgraphs. We treat these subgraphs as candidate complexes. Finally, by expanding and merging these candidate complexes, the predicted complexes are obtained.We evaluate CPredictor3.0 and compare it with a number of existing methods on several PPI networks and benchmarking complex datasets. The experimental results show that CPredictor3.0 achieves the highest F1-measure, which indicates that CPredictor3.0 outperforms these existing method in overall.

Conclusion

CPredictor3.0 can serve as a promising tool of protein complex prediction.

     

A land plant‐specific thylakoid membrane protein contributes to photosystem II maintenance in Arabidopsis thaliana

The structure and function of photosystem II (PSII) are highly susceptible to photo‐oxidative damage induced by high‐fluence or fluctuating light. However, many of the mechanistic details of how PSII homeostasis is maintained under photoinhibitory light remain to be determined. We describe an analysis of the Arabidopsis thaliana gene At5g07020, which encodes an unannotated integral thylakoid membrane protein. Loss of the protein causes altered PSII function under high‐irradiance light, and hence it is named ‘Maintenance of PSII under High light 1’ (MPH1). The MPH1 protein co‐purifies with PSII core complexes and co‐immunoprecipitates core proteins. Consistent with a role in PSII structure, PSII complexes (supercomplexes, dimers and monomers) of the mph1 mutant are less stable in plants subjected to photoinhibitory light. Accumulation of PSII core proteins is compromised under these conditions in the presence of translational inhibitors. This is consistent with the hypothesis that the mutant has enhanced PSII protein damage rather than defective repair. These data are consistent with the distribution of the MPH1 protein in grana and stroma thylakoids, and its interaction with PSII core complexes. Taken together, these results strongly suggest a role for MPH1 in the protection and/or stabilization of PSII under high‐light stress in land plants.