Prediction of the exposure status of transmembrane beta barrel residues from protein sequence

We present BTMX (Beta barrel TransMembrane eXposure), a computational method to predict the exposure status (i.e. exposed to the bilayer or hidden in the protein structure) of transmembrane residues in transmembrane beta barrel proteins (TMBs). BTMX predicts the exposure status of known TM residues with an accuracy of 84.2% over 2,225 residues and provides a confidence score for all predictions. Predictions made are in concert with the fact that hydrophobic residues tend to be more exposed to the bilayer. The biological relevance of the input parameters is also discussed. The highest prediction accuracy is obtained when a sliding window comprising three residues with similar C(α)-C(β) vector orientations is employed. The prediction accuracy of the BTMX method on a separate unseen non-redundant test dataset is 78.1%. By employing out-pointing residues that are exposed to the bilayer, we have identified various physico-chemical properties that show statistically significant differences between the beta strands located at the oligomeric interfaces compared to the non-oligomeric strands. The BTMX web server generates colored, annotated snake-plots as part of the prediction results and is available under the BTMX tab at http://service.bioinformatik.uni-saarland.de/tmx-site/. Exposure status prediction of TMB residues may be useful in 3D structure prediction of TMBs.     

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.     

HTP: a neural network-based method for predicting the topology of helical transmembrane domains in proteins

In this paper we describe a microcomputer program (HTP) forpredicting the location and orientation of -helical transmemhranesegments in integral membrane proteins. HTP is a neural network-basedtool which gives as output the protein membrane topology basedon the statistical propensity of residues to be located in externaland internal loops. This method, which uses single protein sequencesas input to the network system, correctly predicts the topologyof 71 out of 92 membrane proteins of putative membrane orientation,independently of the protein source.     

A consensus algorithm to screen genomes for novel families of transmembrane beta barrel proteins

The ability to search sequence datasets for membrane spanning proteins is an important requirement for genome annotation. However, the development of algorithms to identify novel types of transmembrane beta-barrel (TMB) protein has proven substantially harder than for transmembrane helical proteins, owing to a shorter TM domain in which only alternate residues are hydrophobic. Although recent reports have described important improvements in the development of such algorithms, there is still concern over their ability to confidently screen genomes. Here we describe a new algorithm combining composition and hidden Markov model topology based classifiers (called TMB-Hunt2), which achieves a crossvalidation accuracy of >95%, with 96.7% precision and 94.2% recall. An overview is given of the algorithm design, with a thorough assessment of performance and application to a number of genomes. Of particular note is that TMB/extracellular protein discrimination is significantly more difficult than TMB/cytoplasmic protein discrimination, with the predictor correctly rejecting just 74% of extracellular proteins, in comparison to 98% of cytoplasmic proteins. Focus is given to directions for further improvements in TMB/non-TMB protein discrimination, with a call for the development of standardized tests and assessments of such algorithms. Tools and datasets are made available through a website called TMB-Web (http://www.bioinformatics.leeds.ac.uk/TMB-Web/TMB-Hunt2).     

On the derivation of propensity scales for predicting exposed transmembrane residues of helical membrane proteins

Helical membrane proteins (HMPs) play a crucial role in diverse physiological processes. Given the difficulty in determining their structures by experimental techniques, it is desired to develop computational methods for predicting the burial status of transmembrane residues. Deriving a propensity scale for the 20 amino acids to be exposed to the lipid bilayer from known structures is central to developing such methods. A fundamental problem in this regard is what would be the optimal way of deriving propensity scales. Here, we show that this problem can be reformulated such that an optimal scale is straightforwardly obtained in an analytical fashion. The derived scale favorably compares with others in terms of both algorithmic optimality and practical prediction accuracy. It also allows interesting insights into the structural organization of HMPs. Furthermore, the presented approach can be applied to other bioinformatics problems of HMPs, too. All the data sets and programs used in the study and detailed primary results are available upon request.     

Prediction of the burial status of transmembrane residues of helical membrane proteins

Background  

Helical membrane proteins (HMPs) play a crucial role in diverse cellular processes, yet it still remains extremely difficult to determine their structures by experimental techniques. Given this situation, it is highly desirable to develop sequence-based computational methods for predicting structural characteristics of HMPs.     

Membrane topology of transmembrane proteins: determinants and experimental tools

Membrane topology refers to the two-dimensional structural information of a membrane protein that indicates the number of transmembrane (TM) segments and the orientation of soluble domains relative to the plane of the membrane. Since membrane proteins are co-translationally translocated across and inserted into the membrane, the TM segments orient themselves properly in an early stage of membrane protein biogenesis. Each membrane protein must contain some topogenic signals, but the translocation components and the membrane environment also influence the membrane topology of proteins. We discuss the factors that affect membrane protein orientation and have listed available experimental tools that can be used in determining membrane protein topology.     

Locating the stabilizing residues in (alpha/beta)8 barrel proteins based on hydrophobicity, long-range interactions, and sequence conservation

In nature, 1 out of every 10 proteins has an (alpha/beta)(8) (TIM)-barrel fold, and in most cases, pairwise comparisons show no sequence similarity between them. Hence, delineating the key residues that induce very different sequences to share a common fold is important for understanding the folding and stability of TIM-barrel domains. In this work, we propose a new consensus approach for locating these stabilizing residues based on long-range interactions, hydrophobicity, and conservation of amino acid residues. We have identified 957 stabilizing residues in 63 proteins from a nonredundant set of 71 TIM-barrel domains. Most of these residues are located in the 8-stranded beta-sheet, with nearly one half of them oriented toward the interior of the barrel and the other half oriented toward the surrounding alpha-helices. Several stabilizing residues are found in the N- and C-terminal loops, whereas very few appear in the alpha-helices that surround the internal beta-sheet. Further, these 957 residues are placed in 434 stabilizing segments of various sizes, and each domain contains 1-10 of these segments. We found that 8 segments per domain is the most abundant one, and two thirds of the proteins have 7-9 stabilizing segments. Finally, we verified the identified residues with experimental temperature factors and found that these residues are among the ones with less mobility in the considered proteins. We suggest that our new protocol serves as a powerful tool to identify the stabilizing residues in TIM-barrel domains, which can be used as potential candidates for studying protein folding and stability by means of protein engineering experiments.     

Proteome-wide functional classification and identification of prokaryotic transmembrane proteins by transmembrane topology similarity comparison

We propose a new method for classifying and identifying transmembrane (TM) protein functions in proteome-scale by applying a single-linkage clustering method based on TM topology similarity, which is calculated simply from comparing the lengths of loop regions. In this study, we focused on 87 prokaryotic TM proteomes consisting of 31 proteobacteria, 22 gram-positive bacteria, 19 other bacteria, and 15 archaea. Prior to performing the clustering, we first categorized individual TM protein sequences as "known," "putative" (similar to "known" sequences), or "unknown" by using the homology search and the sequence similarity comparison against SWISS-PROT to assess the current status of the functional annotation of the TM proteomes based on sequence similarity only. More than three-quarters, that is, 75.7% of the TM protein sequences are functionally "unknown," with only 3.8% and 20.5% of them being classified as "known" and "putative," respectively. Using our clustering approach based on TM topology similarity, we succeeded in increasing the rate of TM protein sequences functionally classified and identified from 24.3% to 60.9%. Obtained clusters correspond well to functional superfamilies or families, and the functional classification and identification are successfully achieved by this approach. For example, in an obtained cluster of TM proteins with six TM segments, 109 sequences out of 119 sequences annotated as "ATP-binding cassette transporter" are properly included and 122 "unknown" sequences are also contained.     

Determining the membrane topology of proteins: insertion pathway of a transmembrane helix of annexin 12

We describe a sensitive method for determining the bilayer topology of single-site cysteine-linked NBD fluorescent labels on membrane proteins. Based upon a method developed for peptides [W. C. Wimley and S. H. White (2000) Biochemistry 39, 161-170], it utilizes a novel fluorescence quencher, lysoUB, comprised of a single acyl chain attached to a UniBlue chromophore. The enhanced sensitivity of the method arises from the brightness of the NBD fluorescence and the quenching efficiency of lysoUB, which is not fluorescent. In the course of validating the method, we examined the insertion topology of the D-E helical region of repeat 2 of annexin 12, known to adopt a transbilayer orientation at mildly acidic pH [Langen et al. (1998) Proc. Natl. Acad. Sci. USA 95, 14060-14065]. In the final membrane-inserted state, an NBD label attached to the single-cysteine mutant D134C was found to be in the outer (cis) leaflet, while the one attached to D162C was found in the trans leaflet. But kinetic measurements of NBD fluorescence suggested the existence of a transient intermediate insertion state whose lifetime could be increased by increasing the fraction of anionic lipids in the vesicles. Indeed, the lifetime could be increased for times sufficient for the completion of lysoUB-NBD topology measurements. Such measurements revealed that the D-E region adopts an interfacial topology in the intermediate state with both ends on the cis side of the membrane, consistent with the general concept of interface-directed membrane insertion of proteins [White et al. (2001) J. Biol. Chem. 276, 32395-32398].     

IgTM: An algorithm to predict transmembrane domains and topology in proteins

Background  

Due to their role of receptors or transporters, membrane proteins play a key role in many important biological functions. In our work we used Grammatical Inference (GI) to localize transmembrane segments. Our GI process is based specifically on the inference of Even Linear Languages.     

A novel method for predicting transmembrane segments in proteins based on a statistical analysis of the SwissProt database: the PRED-TMR algorithm.

We present a novel method that predicts transmembrane domains in proteins using solely information contained in the sequence itself. The PRED-TMR algorithm described, refines a standard hydrophobicity analysis with a detection of potential termini ('edges', starts and ends) of transmembrane regions. This allows one both to discard highly hydrophobic regions not delimited by clear start and end configurations and to confirm putative transmembrane segments not distinguishable by their hydrophobic composition. The accuracy obtained on a test set of 101 non-homologous transmembrane proteins with reliable topologies compares well with that of other popular existing methods. Only a slight decrease in prediction accuracy was observed when the algorithm was applied to all transmembrane proteins of the SwissProt database (release 35). A WWW server running the PRED-TMR algorithm is available at http://o2.db.uoa. gr/PRED-TMR/     

Novel transmembrane topology of the hepatitis B virus envelope proteins.

The small (S), middle (M) and large (L) envelope proteins of the hepatitis B virus (HBV) are initially synthesized as multispanning membrane proteins of the endoplasmic reticulum membrane. We now demonstrate that all envelope proteins synthesized in transfected cells or in a cell-free system adopt more than one transmembrane orientation. The L protein disposes its N-terminal preS domain both to the cytoplasmic and the luminal side of the membrane. This unusual topology does not depend on interaction with the viral nucleocapsid, but is preserved in secreted empty envelope particles. Pulse-chase analysis suggests a novel process of post-translational translocation leading to the non-uniform topology. Analysis of L deletion mutants indicates that the block to co-translational translocation can be attributed to a specific sequence within preS, suggesting that translocation of L may be regulated. Additional topological heterogeneity is displayed in the S region of the envelope proteins and in the S protein itself, as assayed in a cell-free system. S proteins integrated into microsomal membranes exhibit both a luminal and a cytoplasmic orientation of the internal hydrophilic region carrying the major antigenic determinants. This may explain the unusual partial glycosylation of the HBV envelope proteins.     

Predicting the topology of transmembrane helical proteins using mean burial propensity and a hidden-Markov-model-based method

Helices in membrane spanning regions are more tightly packed than the helices in soluble proteins. Thus, we introduce a method that uses a simple scale of burial propensity and a new algorithm to predict transmembrane helical (TMH) segments and a positive-inside rule to predict amino-terminal orientation. The method (the topology predictor of transmembrane helical proteins using mean burial propensity [THUMBUP]) correctly predicted the topology of 55 of 73 proteins (or 75%) with known three-dimensional structures (the 3D helix database). This level of accuracy can be reached by MEMSAT 1.8 (a 200-parameter model-recognition method) and a new HMM-based method (a 111-parameter hidden Markov model, UMDHMM(TMHP)) if they were retrained with the 73-protein database. Thus, a method based on a physiochemical property can provide topology prediction as accurate as those methods based on more complicated statistical models and learning algorithms for the proteins with accurately known structures. Commonly used HMM-based methods and MEMSAT 1.8 were trained with a combination of the partial 3D helix database and a 1D helix database of TMH proteins in which topology information were obtained by gene fusion and other experimental techniques. These methods provide a significantly poorer prediction for the topology of TMH proteins in the 3D helix database. This suggests that the 1D helix database, because of its inaccuracy, should be avoided as either a training or testing database. A Web server of THUMBUP and UMDHMM(TMHP) is established for academic users at http://www.smbs.buffalo.edu/phys_bio/service.htm. The 3D helix database is also available from the same Web site.     

Proline residues in transmembrane helices of channel and transport proteins: a molecular modelling study.

Proline residues are commonly found in putative transbilayer helices of many integral membrane proteins which act as transporters, channels and receptors. Intramembranous prolines are often conserved between homologous proteins. It has been suggested that such intrahelical prolines provide liganding sites for cations via exposure of the backbone carbonyl oxygen atoms of residues i-3 and i-4 (relative to the proline). Molecular modelling studies have been carried out to evaluate this proposal. Bundles of parallel proline-kinked helices are considered as simplified models of ion channels. The energetics of K+ ion-helix bundle interactions are explored. It is shown that carbonyl oxygens exposed by the proline-induced kink and at the C-terminus of the helices may provide cation-liganding sites. 'Hybrid' bundles of antiparallel helices, only some of which contain proline residues, are considered as models of transport proteins. Again, proline-exposed carbonyl oxygens are shown to be capable of liganding cations. The roles of alpha-helix dipoles and of the geometry of helix packing are considered in relation to cation-bundle interactions. Implications with respect to modelling of ion channel and transport proteins are discussed.     

ZPRED: predicting the distance to the membrane center for residues in alpha-helical membrane proteins

MOTIVATION: Prediction methods are of great importance for membrane proteins as experimental information is harder to obtain than for globular proteins. As more membrane protein structures are solved it is clear that topology information only provides a simplified picture of a membrane protein. Here, we describe a novel challenge for the prediction of alpha-helical membrane proteins: to predict the distance between a residue and the center of the membrane, a measure we define as the Z-coordinate. Even though the traditional way of depicting membrane protein topology is useful, it is advantageous to have a measure that is based on a more "physical" property such as the Z-coordinate, since it implicitly contains information about re-entrant helices, interfacial helices, the tilt of a transmembrane helix and loop lengths. RESULTS: We show that the Z-coordinate can be predicted using either artificial neural networks, hidden Markov models or combinations of both. The best method, ZPRED, uses the output from a hidden Markov model together with a neural network. The average error of ZPRED is 2.55A and 68.6% of the residues are predicted within 3A of the target Z-coordinate in the 5-25A region. ZPRED is also able to predict the maximum protrusion of a loop to within 3A for 78% of the loops in the dataset. AVAILABILITY: Supplementary information and training data is available at http://www.sbc.su.se/~erikgr/.     

Structural principles of alpha/beta barrel proteins: the packing of the interior of the sheet

Alpha/beta barrel structures very similar to that first observed in triose phosphate isomerase are now known to occur in 14 enzymes. To understand the origin of this fold, we analyzed in three of these proteins the geometry of the eight-stranded beta-sheets and the packing of the residues at the center of the barrel. The packing in this region is seen in its simplest form in glycolate oxidase. It consists of 12 residues arranged in three layers. Each layer contains four side chains. The packing of RubisCO and TIM can be understood in terms of distortions of this simple pattern, caused by residues with small side chains at some of the positions inside the barrel. Two classes of packing are found. In one class, to which RubisCO and TIM belong, the central layer is formed by a residue from the first, third, fifth, and seventh strands; the upper and lower layers are formed by residues from the second, fourth, sixth, and eighth strands. In the second class, to which GAO belongs, this is reversed: it is side chains from the even-numbered strands that form the central layer, and side chains from the odd-numbered strands that form the outer layers. Our results suggest that not all proteins with this fold are related by evolution, but that they represent a common favorable solution to the structural problems involved in the creation of a closed beta barrel.     

Clusters in alpha/beta barrel proteins: implications for protein structure, function, and folding: a graph theoretical approach

The alpha/beta barrel fold is adopted by most enzymes performing a variety of catalytic reactions, but with very low sequence similarity. In order to understand the stabilizing interactions important in maintaining the alpha/beta barrel fold, we have identified residue clusters in a dataset of 36 alpha/beta barrel proteins that have less than 10% sequence identity within themselves. A graph theoretical algorithm is used to identify backbone clusters. This approach uses the global information of the nonbonded interaction in the alpha/beta barrel fold for the clustering procedure. The nonbonded interactions are represented mathematically in the form of an adjacency matrix. On diagonalizing the adjacency matrix, clusters and cluster centers are obtained from the highest eigenvalue and its corresponding vector components. Residue clusters are identified in the strand regions forming the beta barrel and are topologically conserved in all 36 proteins studied. The residues forming the cluster in each of the alpha/beta protein are also conserved among the sequences belonging to the same family. The cluster centers are found to occur in the middle of the strands or in the C-terminal of the strands. In most cases, the residues forming the clusters are part of the active site or are located close to the active site. The folding nucleus of the alpha/beta fold is predicted based on hydrophobicity index evaluation of residues and identification of cluster centers. The predicted nucleation sites are found to occur mostly in the middle of the strands. Proteins 2001;43:103-112.     

Analysis of dihedral angle preferences for alanine and glycine residues in alpha and beta transmembrane regions

For the past 50?years, the Ramachandran map has been used effectively to study the protein structure and folding. However, though extensive analysis has been done on dihedral angle preferences of residues in globular proteins, related studies and reports of membrane proteins are limited. It is of interest to explore the conformational preferences of residues in transmembrane regions of membrane proteins which are involved in several important and diverse biological processes. Hence, in the present work, a systematic comparative computational analysis has been made on dihedral angle preferences of alanine and glycine in alpha and beta transmembrane regions (the two major classes of transmembrane proteins) with the aid of the Ramachandran map. Further, the conformational preferences of residues in transmembrane regions were compared with the non-transmembrane regions. We have extracted cation-pi interacting residues present in transmembrane regions and explored the dihedral angle preferences. From our observations, we reveal the higher percentage of occurrences of glycine in alpha and beta transmembrane regions than other hydrophobic residues. Further, we noted a clear shift in ψ-angle preferences of glycine residues from negative bins in alpha transmembrane regions to positive bins in beta transmembrane regions. Also, cation-pi interacting residues in beta transmembrane regions avoid preferring ψ-angles in the range of ?59° to ?30°. In this article, we insist that the studies on preferences of dihedral angles in transmembrane regions, thorough understanding of structure and folding of transmembrane proteins, can lead to modeling of novel transmembrane regions towards designing membrane proteins.