A workflow for peptide-based proteomics in a poorly sequenced plant: a case study on the plasma membrane proteome of banana

Membrane proteins are an interesting class of proteins because of their functional importance. Unfortunately their analysis is hampered by low abundance and poor solubility in aqueous media. Since shotgun methods are high-throughput and partly overcome these problems, they are preferred for membrane proteomics. However, their application in non-model plants demands special precautions to prevent false positive identification of proteins. In the current paper, a workflow for membrane proteomics in banana, a poorly sequenced plant, is proposed. The main steps of this workflow are (i) optimization of the peptide separation, (ii) performing de novo sequencing to allow a sequence homology search and (iii) visualization of identified peptide-protein associations using Cytoscape to remove redundancy and wrongly assigned peptides, based on species-specific information. By applying this workflow, integral plasma membrane proteins from banana leaves were successfully identified.     

A combinatorial pattern discovery approach for the prediction of membrane dipping (re-entrant) loops

MOTIVATION: Membrane dipping loops are sections of membrane proteins that reside in the membrane but do not traverse from one side to the other, rather they enter and leave the same side of the membrane. We applied a combinatorial pattern discovery approach to sets of sequences containing at least one characterised structure described as possessing a membrane dipping loop. Discovered patterns were found to be composed of residues whose biochemical role is known to be essential for function of the protein, thus validating our approach. TMLOOP (http://membraneproteins.swan.ac.uk/TMLOOP) was implemented to predict membrane dipping loops in polytopic membrane proteins. TMLOOP applies discovered patterns as weighted predictive rules in a collective motif method (a variation of the single motif method), to avoid inherent limitations of single motif methods in detecting distantly related proteins. The collective motif method applies several, partially overlapping patterns, which pertain to the same sequence region, allowing proteins containing small variations to be detected. The approach achieved 92.4% accuracy in sensitivity and 100% reliability in specificity. TMLOOP was applied to the Swiss-Prot database, identifying 1392 confirmed membrane dipping loops, 75 plausible membrane dipping loops hitherto uncharacterised by topology prediction methods or experimental approaches and 128 false positives (8.0%).     

An improved hidden Markov model for transmembrane protein detection and topology prediction and its applications to complete genomes

MOTIVATION: Knowledge of the transmembrane helical topology can help identify binding sites and infer functions for membrane proteins. However, because membrane proteins are hard to solubilize and purify, only a very small amount of membrane proteins have structure and topology experimentally determined. This has motivated various computational methods for predicting the topology of membrane proteins. RESULTS: We present an improved hidden Markov model, TMMOD, for the identification and topology prediction of transmembrane proteins. Our model uses TMHMM as a prototype, but differs from TMHMM by the architecture of the submodels for loops on both sides of the membrane and also by the model training procedure. In cross-validation experiments using a set of 83 transmembrane proteins with known topology, TMMOD outperformed TMHMM and other existing methods, with an accuracy of 89% for both topology and locations. In another experiment using a separate set of 160 transmembrane proteins, TMMOD had 84% for topology and 89% for locations. When utilized for identifying transmembrane proteins from non-transmembrane proteins, particularly signal peptides, TMMOD has consistently fewer false positives than TMHMM does. Application of TMMOD to a collection of complete genomes shows that the number of predicted membrane proteins accounts for approximately 20-30% of all genes in those genomes, and that the topology where both the N- and C-termini are in the cytoplasm is dominant in these organisms except for Caenorhabditis elegans. AVAILABILITY: http://liao.cis.udel.edu/website/servers/TMMOD/     

Enhanced membrane protein topology prediction using a hierarchical classification method and a new scoring function

The prediction of transmembrane (TM) helix and topology provides important information about the structure and function of a membrane protein. Due to the experimental difficulties in obtaining a high-resolution model, computational methods are highly desirable. In this paper, we present a hierarchical classification method using support vector machines (SVMs) that integrates selected features by capturing the sequence-to-structure relationship and developing a new scoring function based on membrane protein folding. The proposed approach is evaluated on low- and high-resolution data sets with cross-validation, and the topology (sidedness) prediction accuracy reaches as high as 90%. Our method is also found to correctly predict both the location of TM helices and the topology for 69% of the low-resolution benchmark set. We also test our method for discrimination between soluble and membrane proteins and achieve very low overall false positive (0.5%) and false negative rates (0 to approximately 1.2%). Lastly, the analysis of the scoring function suggests that the topogeneses of single-spanning and multispanning TM proteins have different levels of complexity, and the consideration of interloop topogenic interactions for the latter is the key to achieving better predictions. This method can facilitate the annotation of membrane proteomes to extract useful structural and functional information. It is publicly available at http://bio-cluster.iis.sinica.edu.tw/~bioapp/SVMtop.     

A multiplexed quantitative strategy for membrane proteomics: opportunities for mining therapeutic targets for autosomal dominant polycystic kidney disease

Toward multiplexed, comprehensive, and robust quantitation of the membrane proteome, we report a strategy combining gel-assisted digestion, iTRAQ (isobaric tags for relative and absolute quantitation) labeling, and LC-MS/MS. Quantitation of four independently purified membrane fractions from HeLa cells gave high accuracy (<8% error) and precision (<12% relative S.D.), demonstrating a high degree of consistency and reproducibility of this quantitation platform. Under stringent identification criteria (false discovery rate = 0%), the strategy efficiently quantified membrane proteins; as many as 520 proteins (91%) were membrane proteins, each quantified based on an average of 14.1 peptides per integral membrane protein. In addition to significant improvements in signal intensity for most quantified proteins, most remarkably, topological analysis revealed that the biggest improvement was achieved in detection of transmembrane peptides from integral membrane proteins with up to 19 transmembrane helices. To the best of our knowledge, this level of coverage exceeds that achieved previously using MS and provides superior quantitation accuracy compared with other methods. We applied this approach to the first proteomics delineation of phenotypic expression in a mouse model of autosomal dominant polycystic kidney disease (ADPKD). By characterizing kidney cell plasma membrane from wild-type versus PKD1 knock-out mice, 791 proteins were quantified, and 67 and 37 proteins showed > or =2-fold up-regulation and down-regulation, respectively. Some of these differentially expressed membrane proteins are involved in the mechanisms underlying major abnormalities in ADPKD, including epithelial cell proliferation and apoptosis, cell-cell and cell-matrix interactions, ion and fluid secretion, and membrane protein polarity. Among these proteins, targeting therapeutics to certain transporters/receptors, such as epidermal growth factor receptor, has proven effective in preclinical studies of ADPKD; others are known drug targets in various diseases. Our method demonstrates how comparative membrane proteomics can provide insight into the molecular mechanisms underlying ADPKD and the identification of potential drug targets, which may lead to new therapeutic opportunities to prevent or retard the disease.     

Effect of 2MEGA labeling on membrane proteome analysis using LC-ESI QTOF MS

One of the challenges associated with large-scale proteome analysis using tandem mass spectrometry (MS/MS) and automated database searching is to reduce the number of false positive identifications without sacrificing the number of true positives found. In this work, a systematic investigation of the effect of 2MEGA labeling (N-terminal dimethylation after lysine guanidination) on the proteome analysis of a membrane fraction of an Escherichia coli cell extract by 2-dimensional liquid chromatography MS/MS is presented. By a large-scale comparison of MS/MS spectra of native peptides with those from the 2MEGA-labeled peptides, the labeled peptides were found to undergo facile fragmentation with enhanced a1 or a1-related (a(1)-17 and a(1)-45) ions derived from all N-terminal amino acids in the MS/MS spectra; these ions are usually difficult to detect in the MS/MS spectra of nonderivatized peptides. The 2MEGA labeling alleviated the biased detection of arginine-terminated peptides that is often observed in MALDI and ESI MS experiments. 2MEGA labeling was found not only to increase the number of peptides and proteins identified but also to generate enhanced a1 or a1-related ions as a constraint to reduce the number of false positive identifications. In total, 640 proteins were identified from the E. coli membrane fraction, with each protein identified based on peptide mass and sequence match of one or more peptides using MASCOT database search algorithm from the MS/MS spectra generated by a quadrupole time-of-flight mass spectrometer. Among them, the subcellular locations of 336 proteins are presently known, including 258 membrane and membrane-associated proteins (76.8%). Among the classified proteins, there was a dramatic increase in the total number of integral membrane proteins identified in the 2MEGA-labeled sample (153 proteins) versus the unlabeled sample (77 proteins).     

On filtering false positive transmembrane protein predictions

While helical transmembrane (TM) region prediction tools achieve high (>90%) success rates for real integral membrane proteins, they produce a considerable number of false positive hits in sequences of known nontransmembrane queries. We propose a modification of the dense alignment surface (DAS) method that achieves a substantial decrease in the false positive error rate. Essentially, a sequence that includes possible transmembrane regions is compared in a second step with TM segments in a sequence library of documented transmembrane proteins. If the performance of the query sequence against the library of documented TM segment-containing sequences in this test is lower than an empirical threshold, it is classified as a non-transmembrane protein. The probability of false positive prediction for trusted TM region hits is expressed in terms of E-values. The modified DAS method, the DAS-TMfilter algorithm, has an unchanged high sensitivity for TM segments ( approximately 95% detected in a learning set of 128 documented transmembrane proteins). At the same time, the selectivity measured over a non-redundant set of 526 soluble proteins with known 3D structure is approximately 99%, mainly because a large number of falsely predicted single membrane-pass proteins are eliminated by the DAS-TMfilter algorithm.     

Comprehensive yeast proteome analysis using a capillary isoelectric focusing-based multidimensional separation platform coupled with ESI-MS/MS

As demonstrated in this study, a CIEF-based multidimensional separation platform not only is compatible with the detergent-based membrane protein preparation protocol, but also achieves both the largest yeast membrane proteome coverage and the most comprehensive analysis of the yeast proteome to date. By using a 1% false discovery rate for total peptide identifications, a total of 2513 distinct yeast proteins are identified from the SDS-solubilized fraction with an average of 5.4 peptides leading to each protein identification. Among proteins identified from the SDS-solubilized fraction, 407 proteins are predicted to contain at least two or more transmembrane domains using TMHMM (www.cbs.dtu.dk/services/TMHMM-2.0/), corresponding to 46% yeast membrane proteome coverage. Only four additional membrane proteins are identified in the soluble and urea-solubilized fractions, affirming the utility of SDS extraction for enriching the membrane proteome. By combining proteome results obtained from the soluble, urea-solubilized, and SDS-solubilized fractions, a single yeast proteome analysis yields the identification of 3632 distinct yeast proteins, corresponding to 55% theoretical yeast proteome coverage or 70% of proteins predicted to be expressed during log-phase growth in rich media.     

Improved detection of homologous membrane proteins by inclusion of information from topology predictions

A total of 20%-25% of the proteins in a typical genome are helical membrane proteins. The transmembrane regions of these proteins have markedly different properties when compared with globular proteins. This presents a problem when homology search algorithms optimized for globular proteins are applied to membrane proteins. Here we present modifications of the standard Smith-Waterman and profile search algorithms that significantly improve the detection of related membrane proteins. The improvement is based on the inclusion of information about predicted transmembrane segments in the alignment algorithm. This is done by simply increasing the alignment score if two residues predicted to belong to transmembrane segments are aligned with each other. Benchmarking over a test set of G-protein-coupled receptor sequences shows that the number of false positives is significantly reduced in this way, both when closely related and distantly related proteins are searched for.     

Co-evolving residues in membrane proteins

MOTIVATION: The analysis of co-evolving residues has been exhaustively evaluated for the prediction of intramolecular amino acid contacts in soluble proteins. Although a variety of different methods for the detection of these co-evolving residues have been developed, the fraction of correctly predicted contacts remained insufficient for their reliable application in the construction of structural models. Membrane proteins, which constitute between one-fourth and one-third of all proteins in an organism, were only considered in few individual case studies. RESULTS: We present the first general study of correlated mutations in alpha-helical membrane proteins. Using seven different prediction algorithms, we extracted co-evolving residues for 14 membrane proteins having a solved 3D structure. On average, distances between correlated pairs of residues lying on different transmembrane segments were found to be significantly smaller compared to a random prediction. Covariation of residues was frequently found in direct sequence neighborhood to helix-helix contacts. Based on the results obtained from individual prediction methods, we constructed a consensus prediction for every protein in the dataset that combines obtained correlations from different prediction algorithms and simultaneously removes likely false positives. Using this consensus prediction, 53% of all predicted residue pairs were found within one helix turn of an observed helix-helix contact. Based on the combination of co-evolving residues detected with the four best prediction algorithms, interacting helices could be predicted with a specificity of 83% and sensitivity of 42%. AVAILABILITY: http://webclu.bio.wzw.tum.de/helixcorr/     

Improved efficiency sos recruitment system: expression of the mammalian GAP reduces isolation of Ras GTPase false positives.

The Sos recruitment system (SRS) is a novel genetic method for detecting protein-protein interactions. The method is based on localizing Sos, a Ras guanyl nucleotide exchange factor (GEF), to the plasma membrane through interaction between two fusion proteins. Mammalian Ras can bypass the requirement for a functional Ras GEF and represents a predictable false positive in this system. This report demonstrates that introduction of mammalian GTPase activating protein (mGAP) reduces the isolation of Ras false positives in SRS screens of mammalian cDNA libraries, thereby significantly enhancing the efficiency of the system.     

Mapping membrane protein structure with fluorescence

Membrane proteins regulate many cellular processes including signaling cascades, ion transport, membrane fusion, and cell-to-cell communications. Understanding the architecture and conformational fluctuations of these proteins is critical to understanding their regulation and functions. Fluorescence methods including intensity mapping, fluorescence resonance energy transfer (FRET), and photo-induced electron transfer, allow for targeted measurements of domains within membrane proteins. These methods can reveal how a protein is structured and how it transitions between different conformational states. Here, I will review recent work done using fluorescence to map the structures of membrane proteins, focusing on how each of these methods can be applied to understanding the dynamic nature of individual membrane proteins and protein complexes.     

Iteration method for predicting essential proteins based on orthology and protein-protein interaction networks

ABSTRACT: BACKGROUND: Identification of essential proteins plays a significant role in understanding minimal requirements for the cellular survival and development. Many computational methods have been proposed for predicting essential proteins by using the topological features of protein-protein interaction (PPI) networks. However, most of these methods ignored intrinsic biological meaning of proteins. Moreover, PPI data contains many false positives and false negatives. To overcome these limitations, recently many research groups have started to focus on identification of essential proteins by integrating PPI networks with other biological information. However, none of their methods has widely been acknowledged. RESULTS: By considering the facts that essential proteins are more evolutionarily conserved than nonessential proteins and essential proteins frequently bind each other, we propose an iteration method for predicting essential proteins by integrating the orthology with PPI networks, named by ION. Differently from other methods, ION identifies essential proteins depending on not only the connections between proteins but also their orthologous properties and features of their neighbors. ION is implemented to predict essential proteins in S. cerevisiae. Experimental results show that ION can achieve higher identification accuracy than eight other existing centrality methods in terms of area under the curve (AUC). Moreover, ION identifies a large amount of essential proteins which have been ignored by eight other existing centrality methods because of their low-connectivity. Many proteins ranked in top 100 by ION are both essential and belong to the complexes with certain biological functions. Furthermore, no matter how many reference organisms were selected, ION outperforms all eight other existing centrality methods. While using as many as possible reference organisms can improve the performance of ION. Additionally, ION also shows good prediction performance in E.Coli K-12. CONCLUSIONS: The accuracy of predicting essential proteins can be improved by integrating the orthology with PPI networks.     

The interactions between mitochondria and sarcoplasmic reticulum and the proteome characterization of mitochondrion‐associated membrane from rabbit skeletal muscle

To obtain a comprehensive understanding of proteins involved in mitochondrion‐sarcoplasmic reticulum (SR) linking, a catalog of proteins from mitochondrion‐associated membrane (MAM) of New Zealand white rabbit skeletal muscle were analyzed by an optimized shotgun proteomic method. The membrane fractions were prepared by differential centrifugation and separated by 1D electrophoresis followed by a highly reproducible, automated LC‐MS/MS on the hybrid linear ion trap (LTQ)‐Orbitrap mass spectrometer. By integrating as low as 1% false discovery rate as one of the features for quality control method, 459 proteins were identified from both of the two independent MAM preparations. Protein pI value, molecular weight range, and transmembrane region were calculated using bioinformatics softwares. One hundred one proteins were recognized as membrane proteins. This protein database suggested that the MAM preparations composed of proteins from mitochondrion, SR, and transverse‐tubule. This result indicated mitochondria physically linked with SR in rabbit skeletal muscle, voltage‐dependent anion channel 1 (VDAC1), VDAC2, and VDAC3 might participate in formation of the tethers between SR and mitochondria.     

Proficiency testing of water microbiology laboratories in The Netherlands

In a 3-year period, four series of simulated water samples containing selected test strains were distributed to more than 50 laboratories in The Netherlands for bacteriological testing. Participating laboratories examined the samples by enrichment or membrane filtration methods, or both, for total coliform organisms, thermotolerant coliform organisms, faecal streptococci and standard plate counts (37 degrees and 22 degrees C) according to Dutch standard methods. The results were quantitatively satisfactory: the distribution of positive and negative results with subsamples conformed to stochastic variation; the standard deviation of membrane or plate counts was usually in the range which may be expected from a Poisson distribution, and there was good correspondence between average counts in participating laboratories and those expected from controls in the organizing laboratory. Problems of a qualitative nature were frequently encountered, however. Among them were a false positive response with a strain of Enterobacter cloacae in the thermotolerant coliform test; a false positive result with Clostridium perfringens in enrichment tests for total or thermotolerant coliform organisms and false positive results with Micrococcus varians in the faecal streptococcus test by membrane filtration. It is concluded that quality assessment should be a consistent activity in water microbiology laboratories. For this purpose, stable and well characterized reference materials are needed.     

A guideline to proteome-wide α-helical membrane protein topology predictions

For current state-of-the-art methods, the prediction of correct topology of membrane proteins has been reported to be above 80%. However, this performance has only been observed in small and possibly biased data sets obtained from protein structures or biochemical assays. Here, we test a number of topology predictors on an "unseen" set of proteins of known structure and also on four "genome-scale" data sets, including one recent large set of experimentally validated human membrane proteins with glycosylated sites. The set of glycosylated proteins is also used to examine the ability of prediction methods to separate membrane from nonmembrane proteins. The results show that methods utilizing multiple sequence alignments are overall superior to methods that do not. The best performance is obtained by TOPCONS, a consensus method that combines several of the other prediction methods. The best methods to distinguish membrane from nonmembrane proteins belong to the "Phobius" group of predictors. We further observe that the reported high accuracies in the smaller benchmark sets are not quite maintained in larger scale benchmarks. Instead, we estimate the performance of the best prediction methods for eukaryotic membrane proteins to be between 60% and 70%. The low agreement between predictions from different methods questions earlier estimates about the global properties of the membrane proteome. Finally, we suggest a pipeline to estimate these properties using a combination of the best predictors that could be applied in large-scale proteomics studies of membrane proteins.     

The synaptic vesicle proteome: a comparative study in membrane protein identification

A proteomic analysis of the synaptic vesicle was undertaken to obtain a better understanding of vesicle regulation. Synaptic vesicles primarily consist of integral membrane proteins that are not well resolved on traditional isoelectric focusing/two-dimensional gel electrophoresis (IEF/2-DE) gels and are resistant to in-gel digestion with trypsin thereby reducing the number of peptides available for mass spectrometric analysis. To address these limitations, two complementary 2-DE methods were investigated in the proteome analysis: (a) IEF/sodium dodecyl sulfate-polyacrylamide gel electrophoresis (IEF/SDS-PAGE) for resolution of soluble proteins and, (b) Benzyl hexadecyl ammonium chloride/SDS-PAGE (16-BAC/SDS-PAGE) for resolution of integral membrane proteins. The IEF/SDS-PAGE method provided superior resolution of soluble proteins, but could only resolve membrane proteins with a single transmembrane domain. The 16-BAC/SDS-PAGE method improved separation, resolution and identification of integral membrane proteins with up to 12 transmembrane domains. Trypsin digestion of the integral membrane proteins was poor and fewer peptides were identified from these proteins. Analysis of both the peptide mass fingerprint and the tandem mass spectra using electrospray ionization quadrupole-time of flight mass spectrometry led to the positive identification of integral membrane proteins. Using both 2-DE separation methods, a total of 36 proteins were identified including seven integral membrane proteins, 17 vesicle regulatory proteins and four proteins whose function in vesicles is not yet known.     

Estimation of membrane proteins in the human proteome

Genomics and proteomics have added valuable information to our knowledgebase of the human biological system including the discovery of therapeutic targets and disease biomarkers. However, molecular profiling studies commonly result in the identification of novel proteins of unknown localization. A class of proteins of special interest is membrane proteins, in particular plasma membrane proteins. Despite their biological and medical significance, the 3-dimensional structures of less than 1% of plasma membrane proteins have been determined. In order to aid in identification of membrane proteins, a number of computational methods have been developed. These tools operate by predicting the presence of transmembrane segments. Here, we utilized five topology prediction methods (TMHMM, SOSUI, waveTM, HMMTOP, and TopPred II) in order to estimate the ratio of integral membrane proteins in the human proteome. These methods employ different algorithms and include a newly-developed method (waveTM) that has yet to be tested on a large proteome database. Since these tools are prone for error mainly as a result of falsely predicting signal peptides as transmembrane segments, we have utilized an additional method, SignalP. Based on our analyses, the ratio of human proteins with transmembrane segments is estimated to fall between 15% and 39% with a consensus of 13%. Agreement among the programs is reduced further when both a positive identification of a membrane protein and the number of transmembrane segments per protein are considered. Such a broad range of prediction depends on the selectivity of the individual method in predicting integral membrane proteins. These methods can play a critical role in determining protein structure and, hence, identifying suitable drug targets in humans.