Beta barrel trans-membrane proteins: Enhanced prediction using a Bayesian approach

Membrane proteins, which constitute approximately 20% of most genomes, form two main classes: alpha helical and beta barrel transmembrane proteins. Using methods based on Bayesian Networks, a powerful approach for statistical inference, we have sought to address beta-barrel topology prediction. The beta-barrel topology predictor reports individual strand accuracies of 88.6%. The method outlined here represents a potentially important advance in the computational determination of membrane protein topology.     

A predictor of membrane class: Discriminating alpha-helical and beta-barrel membrane proteins from non-membranous proteins

Accurate protein structure prediction remains an active objective of research in bioinformatics. Membrane proteins comprise approximately 20% of most genomes. They are, however, poorly tractable targets of experimental structure determination. Their analysis using bioinformatics thus makes an important contribution to their on-going study. Using a method based on Bayesian Networks, which provides a flexible and powerful framework for statistical inference, we have addressed the alignment-free discrimination of membrane from non-membrane proteins. The method successfully identifies prokaryotic and eukaryotic alpha-helical membrane proteins at 94.4% accuracy, beta-barrel proteins at 72.4% accuracy, and distinguishes assorted non-membranous proteins with 85.9% accuracy. The method here is an important potential advance in the computational analysis of membrane protein structure. It represents a useful tool for the characterisation of membrane proteins with a wide variety of potential applications.     

Experimentally based topology models for E. coli inner membrane proteins

Membrane protein topology predictions can be markedly improved by the inclusion of even very limited experimental information. We have recently introduced an approach for the production of reliable topology models based on a combination of experimental determination of the location (cytoplasmic or periplasmic) of a protein's C terminus and topology prediction. Here, we show that determination of the location of a protein's C terminus, rather than some internal loop, is the best strategy for large-scale topology mapping studies. We further report experimentally based topology models for 31 Escherichia coli inner membrane proteins, using methodology suitable for genome-scale studies.     

α-螺旋跨膜蛋白拓扑结构预测方法的评价

在基因组数据中,有20%～30%的产物被预测为跨膜蛋白,本文通过对膜蛋白拓扑结构预测方法进行分析,并评价其结果,为选择更合适的拓扑结构预测方法预测膜蛋白结构。通过对目前已有的拓扑结构预测方法的评价分析,可以为我们在实际工作中提供重要的参考。比如对一个未知拓扑结构的跨膜蛋白序列,我们可以先进行是否含有信号肽的预测,参考Polyphobius和SignalP两种方法,若两种方法预测结果不一致,综合上述对两种方法的评价,Polyphobius预测的综合能力较好,可取其预测的结果,一旦确定含有信号肽,则N端必然位于膜外侧。然后结合序列的长度,判断蛋白是单跨膜还是多重跨膜,即可参照上述评价结果,选择合适的拓扑结构预测方法进行预测。     

Recognition of transmembrane segments in proteins: review and consistency-based benchmarking of internet servers

Membrane proteins perform a number of crucial functions as transporters, receptors, and components of enzyme complexes. Identification of membrane proteins and prediction of their topology is thus an important part of genome annotation. We present here an overview of transmembrane segments in protein sequences, summarize data from large-scale genome studies, and report results of benchmarking of several popular internet servers.     

BPROMPT: A consensus server for membrane protein prediction

Protein structure prediction is a cornerstone of bioinformatics research. Membrane proteins require their own prediction methods due to their intrinsically different composition. A variety of tools exist for topology prediction of membrane proteins, many of them available on the Internet. The server described in this paper, BPROMPT (Bayesian PRediction Of Membrane Protein Topology), uses a Bayesian Belief Network to combine the results of other prediction methods, providing a more accurate consensus prediction. Topology predictions with accuracies of 70% for prokaryotes and 53% for eukaryotes were achieved. BPROMPT can be accessed at http://www.jenner.ac.uk/BPROMPT.     

Energetics, stability, and prediction of transmembrane helices

We show that the peptide backbone of an alpha-helix places a severe thermodynamic constraint on transmembrane (TM) stability. Neglect of this constraint by commonly used hydrophobicity scales underlies the notorious uncertainty of TM helix prediction by sliding-window hydropathy plots of membrane protein (MP) amino acid sequences. We find that an experiment-based whole-residue hydropathy scale (WW scale), which includes the backbone constraint, identifies TM helices of membrane proteins with an accuracy greater than 99 %. Furthermore, it correctly predicts the minimum hydrophobicity required for stable single-helix TM insertion observed in Escherichia coli. In order to improve membrane protein topology prediction further, we introduce the augmented WW (aWW) scale, which accounts for the energetics of salt-bridge formation. An important issue for genomic analysis is the ability of the hydropathy plot method to distinguish membrane from soluble proteins. We find that the method falsely predicts 17 to 43 % of a set of soluble proteins to be MPs, depending upon the hydropathy scale used.     

Prediction of partial membrane protein topologies using a consensus approach

We have developed a method to reliably identify partial membrane protein topologies using the consensus of five topology prediction methods. When evaluated on a test set of experimentally characterized proteins, we find that approximately 90% of the partial consensus topologies are correctly predicted in membrane proteins from prokaryotic as well as eukaryotic organisms. Whole-genome analysis reveals that a reliable partial consensus topology can be predicted for approximately 70% of all membrane proteins in a typical bacterial genome and for approximately 55% of all membrane proteins in a typical eukaryotic genome. The average fraction of sequence length covered by a partial consensus topology is 44% for the prokaryotic proteins and 17% for the eukaryotic proteins in our test set, and similar numbers are found when the algorithm is applied to whole genomes. Reliably predicted partial topologies may simplify experimental determinations of membrane protein topology.     

Protein Topology Prediction Algorithms Systematically Investigated in the Yeast Saccharomyces cerevisiae

Membrane proteins perform a variety of functions, all crucially dependent on their orientation in the membrane. However, neither the exact number of transmembrane domains (TMDs) nor the topology of most proteins have been experimentally determined. Due to this, most scientists rely primarily on prediction algorithms to determine topology and TMD assignments. Since these can give contradictory results, single‐algorithm‐based predictions are unreliable. To map the extent of potential misanalysis, the predictions of nine algorithms on the yeast proteome are compared and it is found that they have little agreement when predicting TMD number and termini orientation. To view all predictions in parallel, a webpage called TopologYeast: http://www.weizmann.ac.il/molgen/TopologYeast was created. Each algorithm is compared with experimental data and a poor agreement is found. The analysis suggests that more systematic data on protein topology are required to increase the training sets for prediction algorithms and to have accurate knowledge of membrane protein topology.     

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%).     

Determining Membrane Protein Topology Using Fluorescence Protease Protection (FPP)

The correct topology and orientation of integral membrane proteins are essential for their proper function, yet such information has not been established for many membrane proteins. A simple technique called fluorescence protease protection (FPP) is presented, which permits the determination of membrane protein topology in living cells. This technique has numerous advantages over other methods for determining protein topology, in that it does not require the availability of multiple antibodies against various domains of the membrane protein, does not require large amounts of protein, and can be performed on living cells. The FPP method employs the spatially confined actions of proteases on the degradation of green fluorescent protein (GFP) tagged membrane proteins to determine their membrane topology and orientation. This simple approach is applicable to a wide variety of cell types, and can be used to determine membrane protein orientation in various subcellular organelles such as the mitochondria, Golgi, endoplasmic reticulum and components of the endosomal/recycling system. Membrane proteins, tagged on either the N-termini or C-termini with a GFP fusion, are expressed in a cell of interest, which is subject to selective permeabilization using the detergent digitonin. Digitonin has the ability to permeabilize the plasma membrane, while leaving intracellular organelles intact. GFP moieties exposed to the cytosol can be selectively degraded through the application of protease, whereas GFP moieties present in the lumen of organelles are protected from the protease and remain intact. The FPP assay is straightforward, and results can be obtained rapidly.     

膜蛋白的拓扑学

膜蛋白的拓扑学是研究膜蛋白三维结构的出发点．利用融合蛋白和化学修饰等实验技术已确定了很多膜蛋白的拓扑学．对膜蛋白的转运与插膜的研究确定可能存在两类插膜元件．对已知拓扑学的膜蛋白的统计分析以及蛋白质工程的研究表明存在膜蛋白拓扑学的内正规则．目前已形成预测膜蛋白的拓扑学的比较可靠的策略,这在反向生物学上具有重要意义.但要进行三维结构的预测还有许多路要走．     

Improved membrane protein topology prediction by domain assignments

Topology predictions for integral membrane proteins can be substantially improved if parts of the protein can be constrained to a given in/out location relative to the membrane using experimental data or other information. Here, we have identified a set of 367 domains in the SMART database that, when found in soluble proteins, have compartment-specific localization of a kind relevant for membrane protein topology prediction. Using these domains as prediction constraints, we are able to provide high-quality topology models for 11% of the membrane proteins extracted from 38 eukaryotic genomes. Two-thirds of these proteins are single spanning, a group of proteins for which current topology prediction methods perform particularly poorly.     

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.     

Mass spectrometry accelerates membrane protein analysis

Cellular membranes are composed of proteins and glyco- and phospholipids and play an indispensible role in maintaining cellular integrity and homeostasis, by physically restricting biochemical processes within cells and providing protection. Membrane proteins perform many essential functions, which include operating as transporters, adhesion-anchors, receptors, and enzymes. Recent advancements in proteomic mass spectrometry have resulted in substantial progress towards the determination of the plasma membrane (PM) proteome, resolution of membrane protein topology, establishment of numerous receptor protein complexes, identification of ligand-receptor pairs, and the elucidation of signaling networks originating at the PM. Here, we discuss the recent accelerated success of discovery-based proteomic pipelines for the establishment of a complete membrane proteome.     

A sequence-profile-based HMM for predicting and discriminating beta barrel membrane proteins

MOTIVATION: Membrane proteins are an abundant and functionally relevant subset of proteins that putatively include from about 15 up to 30% of the proteome of organisms fully sequenced. These estimates are mainly computed on the basis of sequence comparison and membrane protein prediction. It is therefore urgent to develop methods capable of selecting membrane proteins especially in the case of outer membrane proteins, barely taken into consideration when proteome wide analysis is performed. This will also help protein annotation when no homologous sequence is found in the database. Outer membrane proteins solved so far at atomic resolution interact with the external membrane of bacteria with a characteristic beta barrel structure comprising different even numbers of beta strands (beta barrel membrane proteins). In this they differ from the membrane proteins of the cytoplasmic membrane endowed with alpha helix bundles (all alpha membrane proteins) and need specialised predictors. RESULTS: We develop a HMM model, which can predict the topology of beta barrel membrane proteins using, as input, evolutionary information. The model is cyclic with 6 types of states: two for the beta strand transmembrane core, one for the beta strand cap on either side of the membrane, one for the inner loop, one for the outer loop and one for the globular domain state in the middle of each loop. The development of a specific input for HMM based on multiple sequence alignment is novel. The accuracy per residue of the model is 83% when a jack knife procedure is adopted. With a model optimisation method using a dynamic programming algorithm seven topological models out of the twelve proteins included in the testing set are also correctly predicted. When used as a discriminator, the model is rather selective. At a fixed probability value, it retains 84% of a non-redundant set comprising 145 sequences of well-annotated outer membrane proteins. Concomitantly, it correctly rejects 90% of a set of globular proteins including about 1200 chains with low sequence identity (<30%) and 90% of a set of all alpha membrane proteins, including 188 chains.     

Structural fragment clustering reveals novel structural and functional motifs in <Emphasis Type="Italic">α</Emphasis>-helical transmembrane proteins

Background  

A large proportion of an organism's genome encodes for membrane proteins. Membrane proteins are important for many cellular processes, and several diseases can be linked to mutations in them. With the tremendous growth of sequence data, there is an increasing need to reliably identify membrane proteins from sequence, to functionally annotate them, and to correctly predict their topology.     

Membrane insertion pathway of annexin B12: thermodynamic and kinetic characterization by fluorescence correlation spectroscopy and fluorescence quenching

Experimental determination of the free energy stabilizing the structure of membrane proteins in their native lipid environment is undermined by the lack of appropriate methods and suitable model systems. Annexin B12 (ANX) is a soluble protein which reversibly inserts into lipid membranes under mildly acidic conditions, which makes it a good experimental model for thermodynamic studies of folding and stability of membrane proteins. Here we apply fluorescence correlation spectroscopy for quantitative analysis of ANX partitioning into large unilamellar vesicles containing either 25% or 75% anionic lipids. Membrane binding of ANX results in changes of autocorrelation time and amplitude, both of which are used in quantitative analysis. The thermodynamic scheme describing acid-induced membrane interactions of ANX considers two independent processes: pH-dependent formation of a membrane-competent form near the membrane interface and its insertion into the lipid bilayer. Our novel fluorescence lifetime topology method demonstrates that the insertion proceeds via an interfacial refolded intermediate state, which can be stabilized by anionic lipids. Lipid titration measurements are used to determine the free energy of both transmembrane insertion and interfacial penetration, which are found to be similar, approximately -10-12 kcal/mol. The formation of the membrane-competent form, examined in a lipid saturation experiment, was found to depend on the local proton concentration near the membrane interface, occurring with pK = 4.3 and involving the protonation of two residues. Our results demonstrate that fluorescence correlation spectroscopy is a convenient tool for the quantitative characterization of the energetics of transmembrane insertion and that pH-triggered ANX insertion is a useful model for studying the thermodynamic stability of membrane 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.     

Site-specific solvent exposure analysis of a membrane protein using unnatural amino acids and 19F nuclear magnetic resonance

Membrane proteins play an essential role in cellular metabolism, transportation and signal transduction across cell membranes. The scarcity of membrane protein structures has thus far prevented a full understanding of their molecular mechanisms. Preliminary topology studies and residue solvent exposure analysis have the potential to provide valuable information on membrane proteins of unknown structure. Here, a (19)F-containing unnatural amino acid (trimethylfluoro-phenylalanine, tfmF) was applied to accomplish site-specific (19)F spin incorporation at different sites in diacylglycerol kinase (DAGK, an Escherichia coli membrane protein) for site-specific solvent exposure analysis. Due to isotope effect on (19)F spins, a standard curve for (19)F-tfmF chemical shifts was drawn for varying solvent H(2)O/D(2)O ratios. Further site-specific (19)F solvent isotope shift analysis was conducted for DAGK to distinguish residues in water-soluble loops, interfacial areas or hydrophobic membrane regions. This site-specific solvent exposure analysis method could be applied for further topological analysis of other membrane proteins.