Large-scale proteomic analysis of membrane proteins

Proteomic analysis of membrane proteins is a promising approach for the identification of novel drug targets and/or disease biomarkers. Despite notable technological developments, obstacles related to extraction and solublization of membrane proteins are encountered. A critical discussion of the different preparative methods of membrane proteins is offered in relation to downstream proteomic applications, mainly gel-based analyses and mass spectrometry. Frequently, unknown proteins are identified by high-throughput profiling of membrane proteins. In search for novel membrane proteins, analysis of protein sequences using computational tools is performed to predict the presence of transmembrane domains. This review also presents these bioinformatic tools with the human proteome as a case study. Along with technological innovations, advancements in the areas of sample preparation and computational prediction of membrane proteins will lead to exciting discoveries.     

Large-scale proteomic analysis of membrane proteins

Proteomic analysis of membrane proteins is a promising approach for the identification of novel drug targets and/or disease biomarkers. Despite notable technological developments, obstacles related to extraction and solublization of membrane proteins are encountered. A critical discussion of the different preparative methods of membrane proteins is offered in relation to downstream proteomic applications, mainly gel-based analyses and mass spectrometry. Frequently, unknown proteins are identified by high-throughput profiling of membrane proteins. In search for novel membrane proteins, analysis of protein sequences using computational tools is performed to predict the presence of transmembrane domains. This review also presents these bioinformatic tools with the human proteome as a case study. Along with technological innovations, advancements in the areas of sample preparation and computational prediction of membrane proteins will lead to exciting discoveries.     

waveTM: wavelet-based transmembrane segment prediction

waveTM is a web tool for the prediction of transmembrane segments in alpha-helical membrane proteins. Prediction is performed by a dynamic programming algorithm on wavelet-denoised 'hydropathy' signals. Users submit a protein sequence and receive interactively the results. Topology prediction can also be obtained in conjunction with the algorithm OrienTM. A web server that implements the waveTM algorithm is freely available at http://bioinformatics.biol.uoa.gr/waveTM.     

Structural bioinformatics prediction of membrane-binding proteins

Membrane-binding peripheral proteins play important roles in many biological processes, including cell signaling and membrane trafficking. Unlike integral membrane proteins, these proteins bind the membrane mostly in a reversible manner. Since peripheral proteins do not have canonical transmembrane segments, it is difficult to identify them from their amino acid sequences. As a first step toward genome-scale identification of membrane-binding peripheral proteins, we built a kernel-based machine learning protocol. Key features of known membrane-binding proteins, including electrostatic properties and amino acid composition, were calculated from their amino acid sequences and tertiary structures, which were then incorporated into the support vector machine to perform the classification. A data set of 40 membrane-binding proteins and 230 non-membrane-binding proteins was used to construct and validate the protocol. Cross-validation and holdout evaluation of the protocol showed that the accuracy of the prediction reached up to 93.7% and 91.6%, respectively. The protocol was applied to the prediction of membrane-binding properties of four C2 domains from novel protein kinases C. Although these C2 domains have 50% sequence identity, only one of them was predicted to bind the membrane, which was verified experimentally with surface plasmon resonance analysis. These results suggest that our protocol can be used for predicting membrane-binding properties of a wide variety of modular domains and may be further extended to genome-scale identification of membrane-binding peripheral proteins.     

The use of functional domains to improve transmembrane protein topology prediction

Transmembrane proteins affect vital cellular functions and pathogenesis, and are a focus of drug design. It is difficult to obtain diffraction quality crystals to study transmembrane protein structure. Computational tools for transmembrane protein topology prediction fill in the gap between the abundance of transmembrane proteins and the scarcity of known membrane protein structures. Their prediction accuracy is still inadequate: TMHMM, the current state-of-the-art method, has less than 52% accuracy in topology prediction on one set of transmembrane proteins of known topology. Based on the observation that there are functional domains that occur preferentially internal or external to the membrane, we have extended the model of TMHMM to incorporate functional domains, using a probabilistic approach originally developed for computational gene finding. Our extension is better than TMHMM in predicting the topology of transmembrane proteins. As prediction of functional domain improves, our system's prediction accuracy will likely improve as well.     

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.     

Comprehensive proteomic analysis of membrane proteins in Toxoplasma gondii

Toxoplasma gondii (T. gondii) is an obligate intracellular protozoan parasite that is an important human and animal pathogen. Experimental information on T. gondii membrane proteins is limited, and the majority of gene predictions with predicted transmembrane motifs are of unknown function. A systematic analysis of the membrane proteome of T. gondii is important not only for understanding this parasite's invasion mechanism(s), but also for the discovery of potential drug targets and new preventative and therapeutic strategies. Here we report a comprehensive analysis of the membrane proteome of T. gondii, employing three proteomics strategies: one-dimensional gel liquid chromatography-tandem MS analysis (one-dimensional gel electrophoresis LC-MS/MS), biotin labeling in conjunction with one-dimensional gel LC-MS/MS analysis, and a novel strategy that combines three-layer "sandwich" gel electrophoresis with multidimensional protein identification technology. A total of 2241 T. gondii proteins with at least one predicted transmembrane segment were identified and grouped into 841 sequentially nonredundant protein clusters, which account for 21.8% of the predicted transmembrane protein clusters in the T. gondii genome. A large portion (42%) of the identified T. gondii membrane proteins are hypothetical proteins. Furthermore, many of the membrane proteins validated by mass spectrometry are unique to T. gondii or to the Apicomplexa, providing a set of gene predictions ripe for experimental investigation, and potentially suitable targets for the development of therapeutic strategies.     

Development and application of a two-phase, on-membrane digestion method in the analysis of membrane proteome

Analysis of membrane proteins, particularly integral membrane proteins, still presents a great challenge due to their poor water solubility and low abundance though much effort has been devoted to the solubilization and enrichment of the protein class. In this paper, a two-phase, on-membrane digestion method was developed and applied in the analysis of rat liver membrane proteome. The two-phase system was constituted by mixing n-butanol and 25 mM NH4HCO3. Comparative experiments indicated that the proteins on membranes could be digested in the two-phase system more efficiently than in both 60% methanol and 25 mM NH4HCO3 solutions under the same conditions, thereby improving the identification of the membrane proteins. When the established two-phase system and CapLC-MS/MS was used to analyze rat liver membrane proteome, a total of 411 membrane proteins were identified, more than 80% of which were transmembrane proteins with 1-12 mapped transmembrane domains (TMDs). Because of its extraction and dissolution actions, the two-phase on-membrane digestion system we developed could efficiently improve the digestion and removal of adsorbed nonmembrane proteins, and remarkably increase the number and coverage of identified membrane proteins, particularly the transmembrane proteins. Using our procedure to identify a complementary protein set from all fractions of the two-phase system could achieve a higher coverage of the membrane proteome.     

kPROT: a knowledge-based scale for the propensity of residue orientation in transmembrane segments. Application to membrane protein structure prediction

Modeling of integral membrane proteins and the prediction of their functional sites requires the identification of transmembrane (TM) segments and the determination of their angular orientations. Hydrophobicity scales predict accurately the location of TM helices, but are less accurate in computing angular disposition. Estimating lipid-exposure propensities of the residues from statistics of solved membrane protein structures has the disadvantage of relying on relatively few proteins. As an alternative, we propose here a scale of knowledge-based Propensities for Residue Orientation in Transmembrane segments (kPROT), derived from the analysis of more than 5000 non-redundant protein sequences. We assume that residues that tend to be exposed to the membrane are more frequent in TM segments of single-span proteins, while residues that prefer to be buried in the transmembrane bundle interior are present mainly in multi-span TMs. The kPROT value for each residue is thus defined as the logarithm of the ratio of its proportions in single and multiple TM spans. The scale is refined further by defining it for three discrete sections of the TM segment; namely, extracellular, central, and intracellular. The capacity of the kPROT scale to predict angular helical orientation was compared to that of alternative methods in a benchmark test, using a diversity of multi-span alpha-helical transmembrane proteins with a solved 3D structure. kPROT yielded an average angular error of 41 degrees, significantly lower than that of alternative scales (62 degrees -68 degrees ). The new scale thus provides a useful general tool for modeling and prediction of functional residues in membrane proteins. A WWW server (http://bioinfo.weizmann.ac.il/kPROT) is available for automatic helix orientation prediction with kPROT.     

Introductory Lecture: In Vitro Translation Analysis of Integral Membrane Proteins

Abstract

A method of in vitro translation scanning was applied to a variety of polytopic integral membrane proteins, a transition metal P type ATPase from Helicobacter pylori, the SERCA 2 ATPase, the gastric H⁺,K⁺ ATPase, the CCK-A receptor and the human ileal bile acid transporter. This method used vectors containing the N terminal region of the gastric H⁺,K⁺ ATPase or the N terminal region of the CCK-A receptor, coupled via a linker region to the last 177 amino acids of the β-subunit of the gastric H⁺,K⁺ ATPase. The latter contains 5 potential N-linked glycosylation sites. Translation of vectors containing the cDNA encoding one, two or more putative transmembrane domains in the absence or presence of microsomes allowed determination of signal anchor or stop transfer properties of the putative transmembrane domains by the molecular weight shift on SDS PAGE. The P type ATPase from Helicobacter pylori showed the presence of 8 transmembrane segments with this method. The SERCA 2 Ca²⁺ ATPase with this method had 9 transmembrane co-translational insertion domains and coupled with other evidence these data resulted in a 11 transmembrane segment model. Translation of segments of the gastric H⁺,K⁺ ATPase provided evidence for only 7 transmembrane segments but coupled with other data established a 10 membrane segment model. The G7 protein, the CCK-A receptor showed the presence of 6 of the 7 transmembrane segments postulated for this protein. Translation of segments of the human ileal bile acid transporter showed the presence of 8 membrane insertion domains. However, translation of the intact protein provided evidence for an odd number of transmembrane segments, resulting in a tentative model containing 7 or 9 transmembrane segments. Neither G7 type protein appeared to have an arrangement of sequential topogenic signals consistent with the final assembled protein. This method provides a useful addition to methods of determining membrane domains of integral membrane proteins but must in general be utilized with other methods to establish the number of transmembrane α-helices.     

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.     

Identification of new intrinsic proteins in Arabidopsis plasma membrane proteome

Identification and characterization of anion channel genes in plants represent a goal for a better understanding of their central role in cell signaling, osmoregulation, nutrition, and metabolism. Though channel activities have been well characterized in plasma membrane by electrophysiology, the corresponding molecular entities are little documented. Indeed, the hydrophobic protein equipment of plant plasma membrane still remains largely unknown, though several proteomic approaches have been reported. To identify new putative transport systems, we developed a new proteomic strategy based on mass spectrometry analyses of a plasma membrane fraction enriched in hydrophobic proteins. We produced from Arabidopsis cell suspensions a highly purified plasma membrane fraction and characterized it in detail by immunological and enzymatic tests. Using complementary methods for the extraction of hydrophobic proteins and mass spectrometry analyses on mono-dimensional gels, about 100 proteins have been identified, 95% of which had never been found in previous proteomic studies. The inventory of the plasma membrane proteome generated by this approach contains numerous plasma membrane integral proteins, one-third displaying at least four transmembrane segments. The plasma membrane localization was confirmed for several proteins, therefore validating such proteomic strategy. An in silico analysis shows a correlation between the putative functions of the identified proteins and the expected roles for plasma membrane in transport, signaling, cellular traffic, and metabolism. This analysis also reveals 10 proteins that display structural properties compatible with transport functions and will constitute interesting targets for further functional studies.     

Target selection for structural genomics based on combining fold recognition and crystallisation prediction methods: application to the human proteome

The objective of this study is to automatically identify regions of the human proteome that are suitable for 3D structure determination by X-ray crystallography and to annotate them according to their likelihood to produce diffraction quality crystals. The results provide a powerful tool for structural genomics laboratories who wish to select human proteins based on the statistical likelihood of crystallisation success. Combining fold recognition and crystallisation prediction algorithms enables the efficient calculation of the crystallisability of the entire human proteome. This novel study estimates that there are approximately 40,000 crystallisable regions in the human proteome. Currently, only 15% of these regions (approx. 6,000 sequences) have been solved to at least 95% sequence identity. The remaining unsolved regions have been categorised into 5 crystallisation classes and an integral membrane protein (IMP) class, based on established structure prediction, crystallisation prediction and transmembrane (TM) helix prediction algorithms. Approximately 750 unsolved regions (2% of the proteome) have been identified as having a PDB fold representative (template) and an ‘optimal’ likelihood of crystallisation. At the other end of the spectrum, more than 10,500 non-IMP regions with a PDB template are classified as ‘very difficult’ to crystallise (26%) and almost 2,500 regions (6%) were predicted to contain at least 3 TM helices. The 3D-SPECS (3D Structural Proteomics Explorer with Crystallisation Scores) website contains crystallisation predictions for the entire human proteome and can be found at .     

The membrane proteome of Halobacterium salinarum

The identification of 114 integral membrane proteins from Halobacterium salinarum was achieved using liquid chromatography/tandem mass spectrometric (LC/MS/MS) techniques, representing 20% of the predicted alpha-helical transmembrane proteins of the genome. For this experiment, a membrane preparation with only minor contamination by soluble proteins was prepared. From this membrane preparation a number of peripheral membrane proteins were identified by the classical two dimensional gel electrophoresis (2-DE) approach, but identification of integral membrane proteins largely failed with only a very few being identified. By use of a fluorescently labeled membrane preparation, we document that this is caused by an irreversible precipitation of the membrane proteins upon isoelectric focusing (IEF). Attempts to overcome this problem by using alternative IEF methods and IEF strip solubilisation techniques were not successful, and we conclude that the classical 2-DE approach is not suited for the identification of integral membrane proteins. Computational analysis showed that the identification of integral membrane proteins is further complicated by the generation of tryptic peptides, which are unfavorable for matrix assisted laser desorption/ionization time of flight mass spectrometric peptide mass fingerprint analysis. Together with the result from the analysis of the cytosolic proteome (see preceding paper), we could identify 34% (943) of all gene products in H. salinarum which can be theoretically expressed. This is a cautious estimate as very stringent criteria were applied for identification. These results are available under www.halolex.mpg.de.     

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.     

In-depth analysis of the thylakoid membrane proteome of Arabidopsis thaliana chloroplasts: new proteins, new functions, and a plastid proteome database

An extensive analysis of the Arabidopsis thaliana peripheral and integral thylakoid membrane proteome was performed by sequential extractions with salt, detergent, and organic solvents, followed by multidimensional protein separation steps (reverse-phase HPLC and one- and two-dimensional electrophoresis gels), different enzymatic and nonenzymatic protein cleavage techniques, mass spectrometry, and bioinformatics. Altogether, 154 proteins were identified, of which 76 (49%) were alpha-helical integral membrane proteins. Twenty-seven new proteins without known function but with predicted chloroplast transit peptides were identified, of which 17 (63%) are integral membrane proteins. These new proteins, likely important in thylakoid biogenesis, include two rubredoxins, a potential metallochaperone, and a new DnaJ-like protein. The data were integrated with our analysis of the lumenal-enriched proteome. We identified 83 out of 100 known proteins of the thylakoid localized photosynthetic apparatus, including several new paralogues and some 20 proteins involved in protein insertion, assembly, folding, or proteolysis. An additional 16 proteins are involved in translation, demonstrating that the thylakoid membrane surface is an important site for protein synthesis. The high coverage of the photosynthetic apparatus and the identification of known hydrophobic proteins with low expression levels, such as cpSecE, Ohp1, and Ohp2, indicate an excellent dynamic resolution of the analysis. The sequential extraction process proved very helpful to validate transmembrane prediction. Our data also were cross-correlated to chloroplast subproteome analyses by other laboratories. All data are deposited in a new curated plastid proteome database (PPDB) with multiple search functions (http://cbsusrv01.tc.cornell.edu/users/ppdb/). This PPDB will serve as an expandable resource for the plant community.     

Comparison of SDS- and methanol-assisted protein solubilization and digestion methods for Escherichia coli membrane proteome analysis by 2-D LC-MS/MS

Both organic solvent and surfactant have been used for dissolving membrane proteins for shotgun proteomics. In this work, two methods of protein solubilization, namely using 60% methanol or 1% SDS, to dissolve and analyze the inner membrane fraction of an Escherichia coli K12 cell lysate were compared. A total of 358 proteins (1417 unique peptides) from the methanol-solubilized protein mixture and 299 proteins (892 peptides) from the SDS-solubilized sample-were identified by using trypsin digestion and 2-D LC-ESI MS/MS. It was found that the methanol method detected more hydrophobic peptides, resulting in a greater number of proteins identified, than the SDS method. We found that 159 out of 358 proteins (44%) and 120 out of 299 proteins (40%) detected from the methanol- and SDS-solubilized samples, respectively, are integral membrane proteins. Among the 190 integral membrane proteins 70 were identified exclusively in the methanol-solubilized sample, 89 were identified by both methods, and only 31 proteins were exclusively identified by the SDS method. It is shown that the integral membrane proteins reflected the theoretical proteome for number of transmembrane helices, length, functional class, and topology, indicating there was no bias in the proteins identified.     

Enrichment of integral membrane proteins for proteomic analysis using liquid chromatography-tandem mass spectrometry

An increasing number of proteomic strategies rely on liquid chromatography-tandem mass spectrometry (LC-MS/MS) to detect and identify constituent peptides of enzymatically digested proteins obtained from various organisms and cell types. However, sample preparation methods for isolating membrane proteins typically involve the use of detergents and chaotropes that often interfere with chromatographic separation and/or electrospray ionization. To address this problem, a sample preparation method combining carbonate extraction, surfactant-free organic solvent-assisted solubilization, and proteolysis was developed and demonstrated to target the membrane subproteome of Deinococcus radiodurans. Out of 503 proteins identified, 135 were recognized as hydrophobic on the basis of their calculated hydropathy values (GRAVY index), corresponding to coverage of 15% of the predicted hydrophobic proteome. Using the PSORT algorithm, 53 of the proteins identified were classified as integral outer membrane proteins and 215 were classified as integral cytoplasmic membrane proteins. All identified integral cytoplasmic membrane proteins had from 1 to 16 mapped transmembrane domains (TMDs), and 65% of those containing four or more mapped TMDs were identified by at least one hydrophobic membrane spanning peptide. The extensive coverage of the membrane subproteome (24%) by identification of highly hydrophobic proteins containing multiple TMDs validates the efficacy of the described sample preparation technique to isolate and solubilize hydrophobic integral membrane proteins from complex protein mixtures.     

Toward the complete membrane proteome: high coverage of integral membrane proteins through transmembrane peptide detection

To attain a comprehensive membrane proteome of two strains of Corynebacterium glutamicum (l-lysine-producing and the characterized model strains), both sample pretreatment and analysis methods were optimized. Isolated bacterial membranes were digested with trypsin/cyanogen bromide or trypsin/chymotrypsin, and a complementary protein set was identified using the multidimensional protein identification technology (MudPIT). Besides a distinct number of cytosolic or membrane-associated proteins, the combined data analysis from both digests yielded 326 integral membrane proteins ( approximately 50% of all predicted) covering membrane proteins both with small and large numbers of transmembrane helices. Also membrane proteins with a high GRAVY score were identified, and basic and acidic membrane proteins were evenly represented. A significant increase in hydrophobic peptides with distinctly higher sequence coverage of transmembrane regions was achieved by trypsin/chymotrypsin digestion in an organic solvent. The percentage of identified membrane proteins increased with protein size, yielding 80% of all membrane proteins above 60 kDa. Most prominently, almost all constituents of the respiratory chain and a high number of ATP-binding cassette transport systems were identified. This newly developed protocol is suitable for the quantitative comparison of membrane proteomes and will be especially useful for applications such as monitoring protein expression under different growth and fermentation conditions in bacteria such as C. glutamicum. Moreover with more than 50% coverage of all predicted membrane proteins (including the non-expressed species) this improved method has the potential for a close-to-complete coverage of membrane proteomes in general.