Prediction of helix-helix contacts and interacting helices in polytopic membrane proteins using neural networks

Despite rapidly increasing numbers of available 3D structures, membrane proteins still account for less than 1% of all structures in the Protein Data Bank. Recent high-resolution structures indicate a clearly broader structural diversity of membrane proteins than initially anticipated, motivating the development of reliable structure prediction methods specifically tailored for this class of molecules. One important prediction target capturing all major aspects of a protein's 3D structure is its contact map. Our analysis shows that computational methods trained to predict residue contacts in globular proteins perform poorly when applied to membrane proteins. We have recently published a method to identify interacting alpha-helices in membrane proteins based on the analysis of coevolving residues in predicted transmembrane regions. Here, we present a substantially improved algorithm for the same problem, which uses a newly developed neural network approach to predict helix-helix contacts. In addition to the input features commonly used for contact prediction of soluble proteins, such as windowed residue profiles and residue distance in the sequence, our network also incorporates features that apply to membrane proteins only, such as residue position within the transmembrane segment and its orientation toward the lipophilic environment. The obtained neural network can predict contacts between residues in transmembrane segments with nearly 26% accuracy. It is therefore the first published contact predictor developed specifically for membrane proteins performing with equal accuracy to state-of-the-art contact predictors available for soluble proteins. The predicted helix-helix contacts were employed in a second step to identify interacting helices. For our dataset consisting of 62 membrane proteins of solved structure, we gained an accuracy of 78.1%. Because the reliable prediction of helix interaction patterns is an important step in the classification and prediction of membrane protein folds, our method will be a helpful tool in compiling a structural census of membrane proteins.     

An introduction to membrane proteins

α-Helical membrane proteins are important for many biological functions. Due to physicochemical constraints, the structures of membrane proteins differ from the structure of soluble proteins. Historically, membrane protein structures were assumed to be more or less two-dimensional, consisting of long, straight, membrane-spanning parallel helices packed against each other. However, during the past decade, a number of the new membrane protein structures cast doubt on this notion. Today, it is evident that the structures of many membrane proteins are equally complex as for many soluble proteins. Here, we review this development and discuss the consequences for our understanding of membrane protein biogenesis, folding, evolution, and bioinformatics.     

A simple statistical method for discriminating outer membrane proteins with better accuracy

MOTIVATION: Discriminating outer membrane proteins from other folding types of globular and membrane proteins is an important task both for identifying outer membrane proteins from genomic sequences and for the successful prediction of their secondary and tertiary structures. RESULTS: We have systematically analyzed the amino acid composition of globular proteins from different structural classes and outer membrane proteins. We found that the residues, Glu, His, Ile, Cys, Gln, Asn and Ser, show a significant difference between globular and outer membrane proteins. Based on this information, we have devised a statistical method for discriminating outer membrane proteins from other globular and membrane proteins. Our approach correctly picked up the outer membrane proteins with an accuracy of 89% for the training set of 337 proteins. On the other hand, our method has correctly excluded the globular proteins at an accuracy of 79% in a non-redundant dataset of 674 proteins. Furthermore, the present method is able to correctly exclude alpha-helical membrane proteins up to an accuracy of 80%. These accuracy levels are comparable to other methods in the literature, and this is a simple method, which could be used for dissecting outer membrane proteins from genomic sequences. The influence of protein size, structural class and specific residues for discrimination is discussed.     

Using GO-PseAA predictor to identify membrane proteins and their types

Cell membranes are crucial to the life of a cell. Although the basic structure of biological membrane is provided by the lipid bilayer, most of the specific functions are carried out by membrane proteins. Knowledge of membrane protein type often offers important clues toward determining the function of an uncharacterized protein. Therefore, predicting the type of a membrane protein from its primary sequence, or even just identifying whether the uncharacterized protein belongs to a membrane protein or not, is an important and challenging problem in bioinformatics and proteomics. To deal with these problems, the GO-PseAA predictor is introduced that is operated in a hybridization space by combining the gene ontology and pseudo amino acid composition. Meanwhile, to test the prediction quality, a dataset was constructed that contains 6476 non-membrane proteins and 5122 membrane proteins classified into five different types. To avoid redundancy and bias, none of the proteins included has > or = 40% sequence identity to any other. It has been observed that the overall success rate by the jackknife cross-validation test in identifying non-membrane proteins and membrane proteins was 94.76%, and that in identifying the five membrane protein types was 95.84%. The high success rates suggest that the GO-PseAA predictor can catch the core feature of the statistical samples concerned and may become an automated high throughput toll in molecular and cell biology.     

Discrimination of outer membrane proteins by a new measure of information discrepancy

Discriminating outer membrane proteins for globular proteins (GPs) and other types of membrane proteins from genomic sequences is an important and hot topic. In this paper, a measure based on information discrepancy is proposed and applied to the discrimination of outer membrane proteins. It differs from previous methods which are based on amino acid composition. Our approach focuses on the comparison of subsequence distributions and takes into account the effect of residue order in protein primary structures. As a result, the new approach outperforms all previous methods on the same benchmark datasets. In particular, we show that the proposed approach has correctly identified the outer membrane proteins at an accuracy of 99% for the training set of 337 proteins and has correctly excluded the GPs at an accuracy of 86% in a non-redundant dataset of 668 proteins. Furthermore, this method is able to correctly exclude alpha-helical membrane proteins at an accuracy of 100%.     

Progress in structure prediction of alpha-helical membrane proteins

Transmembrane (TM) proteins comprise 20-30% of the genome but, because of experimental difficulties, they represent less than 1% of the Protein Data Bank. The dearth of membrane protein structures makes computational prediction a potentially important means of obtaining novel structures. Recent advances in computational methods have been combined with experimental data to constrain the modeling of three-dimensional structures. Furthermore, threading and ab initio modeling approaches that were effective for soluble proteins have been applied to TM domains. Surprisingly, experimental structures, proteomic analyses and bioinformatics have revealed unexpected architectures that counter long-held views on TM protein structure and stability. Future computational and experimental studies aimed at understanding the thermodynamic and evolutionary bases of these architectural details will greatly enhance predictive capabilities.     

Conformational preference functions for predicting helices in membrane proteins

A suite of FORTRAN programs, PREF, is described for calculating preference functions from the data base of known protein structures and for comparing smoothed profiles of sequence-dependent preferences in proteins of unknown structure. Amino acid preferences for a secondary structure are considered as functions of a sequence environment. Sequence environment of amino acid residue in a protein is defined as an average over some physical, chemical, or statistical property of its primary structure neighbors. The frequency distribution of sequence environments in the data base of soluble protein structures is approximately normal for each amino acid type of known secondary conformation. An analytical expression for the dependence of preferences on sequence environment is obtained after each frequency distribution is replaced by corresponding Gaussian function. The preference for the α-helical conformation increases for each amino acid type with the increase of sequence environment of buried solvent-accessible surface areas. We show that a set of preference functions based on buried surface area is useful for predicting folding motifs in α-class proteins and in integral membrane proteins. The prediction accuracy for helical residues is 79% for 5 integral membrane proteins and 74% for 11 α-class soluble proteins. Most residues found in transmembrane segments of membrane proteins with known α-helical structure are predicted to be indeed in the helical conformation because of very high middle helix preferences. Both extramembrane and transmembrane helices in the photosynthetic reaction center M and L subunits are correctly predicted. We point out in the discussion that our method of conformational preference functions can identify what physical properties of the amino acids are important in the formation of particular secondary structure elements. © 1993 John Wiley & Sons, Inc.     

Discrimination of outer membrane proteins using support vector machines

MOTIVATION: Discriminating outer membrane proteins from other folding types of globular and membrane proteins is an important task both for dissecting outer membrane proteins (OMPs) from genomic sequences and for the successful prediction of their secondary and tertiary structures. RESULTS: We have developed a method based on support vector machines using amino acid composition and residue pair information. Our approach with amino acid composition has correctly predicted the OMPs with a cross-validated accuracy of 94% in a set of 208 proteins. Further, this method has successfully excluded 633 of 673 globular proteins and 191 of 206 alpha-helical membrane proteins. We obtained an overall accuracy of 92% for correctly picking up the OMPs from a dataset of 1087 proteins belonging to all different types of globular and membrane proteins. Furthermore, residue pair information improved the accuracy from 92 to 94%. This accuracy of discriminating OMPs is higher than that of other methods in the literature, which could be used for dissecting OMPs from genomic sequences. AVAILABILITY: Discrimination results are available at http://tmbeta-svm.cbrc.jp.     

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.     

Discrimination of outer membrane proteins using machine learning algorithms

Discriminating outer membrane proteins (OMPs) from other folding types of globular and membrane proteins is an important task both for identifying OMPs from genomic sequences and for the successful prediction of their secondary and tertiary structures. In this work, we have analyzed the performance of different methods, based on Bayes rules, logistic functions, neural networks, support vector machines, decision trees, etc. for discriminating OMPs. We found that most of the machine learning techniques discriminate OMPs with similar accuracy. The neural network-based method could discriminate the OMPs from other proteins [globular/transmembrane helical (TMH)] at the fivefold cross-validation accuracy of 91.0% in a dataset of 1,088 proteins. The accuracy of discriminating globular proteins is 88.8% and that of TMH proteins is 93.7%. Further, the neural network method is tested with globular proteins belonging to 30 different folding types and it could successfully exclude 95% of the considered proteins. The proteins with SAM domain such as knottins, rubredoxin, and thioredoxin folds are eliminated with 100% accuracy. These accuracy levels are comparable to or better than other methods in the literature. We suggest that this method could be effectively used to discriminate OMPs and for detecting OMPs in genomic sequences.     

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.     

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.     

NMR structures of polytopic integral membrane proteins

Membrane proteins represent up to 30% of the proteins in all organisms, they are involved in many biological processes and are the molecular targets for around 50% of validated drugs. Despite this, membrane proteins represent less than 1% of all high-resolution protein structures due to various challenges associated with applying the main biophysical techniques used for protein structure determination. Recent years have seen an explosion in the number of high-resolution structures of membrane proteins determined by NMR spectroscopy, especially for those with multiple transmembrane-spanning segments. This is a review of the structures of polytopic integral membrane proteins determined by NMR spectroscopy up to the end of the year 2010, which includes both β-barrel and α-helical proteins from a number of different organisms and with a range in types of function. It also considers the challenges associated with performing structural studies by NMR spectroscopy on membrane proteins and how some of these have been overcome, along with its exciting potential for contributing new knowledge about the molecular mechanisms of membrane proteins, their roles in human disease, and for assisting drug design.     

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.     

NMR of membrane proteins in micelles and bilayers: the FXYD family proteins

Determining the atomic resolution structures of membrane proteins is of particular interest in contemporary structural biology. Helical membrane proteins constitute one-third of the expressed proteins encoded in a genome, many drugs have membrane-bound proteins as their receptors, and mutations in membrane proteins result in human diseases. Although integral membrane proteins provide daunting technical challenges for all methods of protein structure determination, nuclear magnetic resonance (NMR) spectroscopy can be an extremely versatile and powerful method for determining their structures and characterizing their dynamics, in lipid environments that closely mimic the cell membranes. Once milligram amounts of isotopically labeled protein are expressed and purified, micelle samples can be prepared for solution NMR analysis, and lipid bilayer samples can be prepared for solid-state NMR analysis. The two approaches are complementary and can provide detailed structural and dynamic information. This paper describes the steps for membrane protein structure determination using solution and solid-state NMR. The methods for protein expression and purification, sample preparation and NMR experiments are described and illustrated with examples from the FXYD proteins, a family of regulatory subunits of the Na,K-ATPase.     

Characterization of low abundant membrane proteins using the protein sequence tag technology

About 25% of open reading frames in fully sequenced genomes are estimated to encode transmembrane proteins that represent valuable targets for drugs. However, the global analysis of membrane proteins has been proven to be problematic, e.g., because of their very amphiphilic nature. In this paper, we show that the recently published Protein Sequence Tag (PST) technology combined with an efficient sample preparation is a powerful method to perform protein analysis of highly enriched membrane fractions. The PST approach is a gel-free proteomics tool for the analysis of proteins, which relies on a "sampling" strategy by isolating N-terminal protein sequence tags from cyanogen bromide cleaved proteins. The identification of these N-terminal PST peptides is based on LC-MS/MS. The effectiveness of the technology is demonstrated for a membrane fraction, which was isolated from crude mitochondria of yeast after alkaline sodium carbonate treatment. The PST approach performed on this fraction analyzed 148 proteins, whereas 84% are identified as membrane proteins. More interestingly, among these membrane proteins 56% are predicted to be of low abundance. These encouraging results are an important step toward the development of a quantitative PST approach (qPST) for the differential display of membrane protein analysis.     

On the accuracy of homology modeling and sequence alignment methods applied to membrane proteins

In this study, we investigate the extent to which techniques for homology modeling that were developed for water-soluble proteins are appropriate for membrane proteins as well. To this end we present an assessment of current strategies for homology modeling of membrane proteins and introduce a benchmark data set of homologous membrane protein structures, called HOMEP. First, we use HOMEP to reveal the relationship between sequence identity and structural similarity in membrane proteins. This analysis indicates that homology modeling is at least as applicable to membrane proteins as it is to water-soluble proteins and that acceptable models (with C alpha-RMSD values to the native of 2 A or less in the transmembrane regions) may be obtained for template sequence identities of 30% or higher if an accurate alignment of the sequences is used. Second, we show that secondary-structure prediction algorithms that were developed for water-soluble proteins perform approximately as well for membrane proteins. Third, we provide a comparison of a set of commonly used sequence alignment algorithms as applied to membrane proteins. We find that high-accuracy alignments of membrane protein sequences can be obtained using state-of-the-art profile-to-profile methods that were developed for water-soluble proteins. Improvements are observed when weights derived from the secondary structure of the query and the template are used in the scoring of the alignment, a result which relies on the accuracy of the secondary-structure prediction of the query sequence. The most accurate alignments were obtained using template profiles constructed with the aid of structural alignments. In contrast, a simple sequence-to-sequence alignment algorithm, using a membrane protein-specific substitution matrix, shows no improvement in alignment accuracy. We suggest that profile-to-profile alignment methods should be adopted to maximize the accuracy of homology models of membrane proteins.     

Integral membrane proteins and dynamic organization of the nuclear envelope

The nuclear envelope is a complex structure consisting of nuclear membranes, nuclear pore complexes and lamina. Several integral membrane proteins specific to the nuclear pore membrane and the inner nuclear membrane are known. Pore membrane proteins are probably important for organization and assembly of the nuclear pore complex, while proteins of the inner nuclear membrane are likely to play major roles in the structure and dynamics of the nuclear lamina and chromatin. Biochemical studies are now identifying potential binding partners for some of these integral membrane proteins, and analysis of nuclear envelope assembly at the end of mitosis is providing important insights into their functions.     

Influence of amino acid properties for discriminating outer membrane proteins at better accuracy

Discriminating outer membrane proteins (OMPs) from other folding types of globular and membrane proteins is an important task both for identifying outer membrane proteins from genomic sequences and for the successful prediction of their secondary and tertiary structures. In this work, we have analyzed the influence of physico-chemical, energetic and conformational properties of amino acid residues for discriminating outer membrane proteins using different machine learning algorithms, such as, Bayes rules, Logistic functions, Neural networks, Support vector machines, Decision trees, etc. We observed that most of the properties have discriminated the OMPs with similar accuracy. The neural network method with the property, free energy change could discriminate the OMPs from other folding types of globular and membrane proteins at the 5-fold cross-validation accuracy of 94.4% in a dataset of 1,088 proteins, which is better than that obtained with amino acid composition. The accuracy of discriminating globular proteins is 94.3% and that of transmembrane helical (TMH) proteins is 91.8%. Further, the neural network method is tested with globular proteins belonging to 30 major folding types and it could successfully exclude 99.4% of the considered 1612 non-redundant proteins. These accuracy levels are comparable to or better than other methods in the literature. We suggest that this method could be effectively used to discriminate OMPs and for detecting OMPs in genomic sequences.     

NMR structures of polytopic integral membrane proteins

Abstract

Membrane proteins represent up to 30% of the proteins in all organisms, they are involved in many biological processes and are the molecular targets for around 50% of validated drugs. Despite this, membrane proteins represent less than 1% of all high-resolution protein structures due to various challenges associated with applying the main biophysical techniques used for protein structure determination. Recent years have seen an explosion in the number of high-resolution structures of membrane proteins determined by NMR spectroscopy, especially for those with multiple transmembrane-spanning segments. This is a review of the structures of polytopic integral membrane proteins determined by NMR spectroscopy up to the end of the year 2010, which includes both β-barrel and α-helical proteins from a number of different organisms and with a range in types of function. It also considers the challenges associated with performing structural studies by NMR spectroscopy on membrane proteins and how some of these have been overcome, along with its exciting potential for contributing new knowledge about the molecular mechanisms of membrane proteins, their roles in human disease, and for assisting drug design.