WRF-TMH: predicting transmembrane helix by fusing composition index and physicochemical properties of amino acids

Membrane protein is the prime constituent of a cell, which performs a role of mediator between intra and extracellular processes. The prediction of transmembrane (TM) helix and its topology provides essential information regarding the function and structure of membrane proteins. However, prediction of TM helix and its topology is a challenging issue in bioinformatics and computational biology due to experimental complexities and lack of its established structures. Therefore, the location and orientation of TM helix segments are predicted from topogenic sequences. In this regard, we propose WRF-TMH model for effectively predicting TM helix segments. In this model, information is extracted from membrane protein sequences using compositional index and physicochemical properties. The redundant and irrelevant features are eliminated through singular value decomposition. The selected features provided by these feature extraction strategies are then fused to develop a hybrid model. Weighted random forest is adopted as a classification approach. We have used two benchmark datasets including low and high-resolution datasets. tenfold cross validation is employed to assess the performance of WRF-TMH model at different levels including per protein, per segment, and per residue. The success rates of WRF-TMH model are quite promising and are the best reported so far on the same datasets. It is observed that WRF-TMH model might play a substantial role, and will provide essential information for further structural and functional studies on membrane proteins. The accompanied web predictor is accessible at http://111.68.99.218/WRF-TMH/.     

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.     

A combination of compositional index and genetic algorithm for predicting transmembrane helical segments

Transmembrane helix (TMH) topology prediction is becoming a focal problem in bioinformatics because the structure of TM proteins is difficult to determine using experimental methods. Therefore, methods that can computationally predict the topology of helical membrane proteins are highly desirable. In this paper we introduce TMHindex, a method for detecting TMH segments using only the amino acid sequence information. Each amino acid in a protein sequence is represented by a Compositional Index, which is deduced from a combination of the difference in amino acid occurrences in TMH and non-TMH segments in training protein sequences and the amino acid composition information. Furthermore, a genetic algorithm was employed to find the optimal threshold value for the separation of TMH segments from non-TMH segments. The method successfully predicted 376 out of the 378 TMH segments in a dataset consisting of 70 test protein sequences. The sensitivity and specificity for classifying each amino acid in every protein sequence in the dataset was 0.901 and 0.865, respectively. To assess the generality of TMHindex, we also tested the approach on another standard 73-protein 3D helix dataset. TMHindex correctly predicted 91.8% of proteins based on TM segments. The level of the accuracy achieved using TMHindex in comparison to other recent approaches for predicting the topology of TM proteins is a strong argument in favor of our proposed method. Availability: The datasets, software together with supplementary materials are available at: http://faculty.uaeu.ac.ae/nzaki/TMHindex.htm.     

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.     

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.     

Combining hydrophobicity and helicity: a novel approach to membrane protein structure prediction

In spite of the overwhelming numbers and critical biological functions of membrane proteins, only a few have been characterized by high-resolution structural techniques. From the structures that are known, it is seen that their transmembrane (TM) segments tend to fold most often into alpha-helices. To evaluate systematically the features of these TM segments, we have taken two approaches: (1) using the experimentally-measured residence behavior of specifically designed hydrophobic peptides in RP-HPLC, a scale was derived based directly on the properties of individual amino acids incorporated into membrane-interactive helices: and (2) the relative alpha-helical propensity of each of the 20 amino acids was measured in the organic non-polar environment of n-butanol. By combining the resulting hydrophobicity and helical propensity data, in conjunction with consideration of the 'threshold hydrophobicity' required for spontaneous membrane integration of protein segments, an approach was developed for prediction of TM segments wherein each must fulfill the dual requirements of hydrophobicity and helicity. Evaluated against the available high-resolution structural data on membrane proteins, the present combining method is shown to provide accurate predictions for the locations of TM helices. In contrast, no segment in soluble proteins was predicted as a 'TM helix'.     

Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes

We describe and validate a new membrane protein topology prediction method, TMHMM, based on a hidden Markov model. We present a detailed analysis of TMHMM's performance, and show that it correctly predicts 97-98 % of the transmembrane helices. Additionally, TMHMM can discriminate between soluble and membrane proteins with both specificity and sensitivity better than 99 %, although the accuracy drops when signal peptides are present. This high degree of accuracy allowed us to predict reliably integral membrane proteins in a large collection of genomes. Based on these predictions, we estimate that 20-30 % of all genes in most genomes encode membrane proteins, which is in agreement with previous estimates. We further discovered that proteins with N(in)-C(in) topologies are strongly preferred in all examined organisms, except Caenorhabditis elegans, where the large number of 7TM receptors increases the counts for N(out)-C(in) topologies. We discuss the possible relevance of this finding for our understanding of membrane protein assembly mechanisms. A TMHMM prediction service is available at http://www.cbs.dtu.dk/services/TMHMM/.     

Prediction of buried helices in multispan alpha helical membrane proteins

Analysis of a database of structures of membrane proteins shows that membrane proteins composed of 10 or more transmembrane (TM) helices often contain buried helices that are inaccessible to phospholipids. We introduce a method for identifying TM helices that are least phospholipid accessible and for prediction of fully buried TM helices in membrane proteins from sequence information alone. Our method is based on the calculation of residue lipophilicity and evolutionary conservation. Given that the number of buried helices in a membrane protein is known, our method achieves an accuracy of 78% and a Matthew's correlation coefficient of 0.68. A server for this tool (RANTS) is available online at http://gila.bioengr.uic.edu/lab/.     

Using pseudo amino acid composition to predict transmembrane regions in protein: cellular automata and Lempel-Ziv complexity

Summary. Transmembrane (TM) proteins represent about 20–30% of the protein sequences in higher eukaryotes, playing important roles across a range of cellular functions. Moreover, knowledge about topology of these proteins often provides crucial hints toward their function. Due to the difficulties in experimental structure determinations of TM protein, theoretical prediction methods are highly preferred in identifying the topology of newly found ones according to their primary sequences, useful in both basic research and drug discovery. In this paper, based on the concept of pseudo amino acid composition (PseAA) that can incorporate sequence-order information of a protein sequence so as to remarkably enhance the power of discrete models (Chou, K. C., Proteins: Structure, Function, and Genetics, 2001, 43: 246–255), cellular automata and Lempel-Ziv complexity are introduced to predict the TM regions of integral membrane proteins including both α-helical and β-barrel membrane proteins, validated by jackknife test. The result thus obtained is quite promising, which indicates that the current approach might be a quite potential high throughput tool in the post-genomic era. The source code and dataset are available for academic users at liml@scu.edu.cn. Authors’ address: Menglong Li, College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, P.R. China     

The use of soluble protein structures in modeling helical proteins in a layered membrane

Major advances have been made in the prediction of soluble protein structures, led by the knowledge-based modeling methods that extract useful structural trends from known protein structures and incorporate them into scoring functions. The same cannot be reported for the class of transmembrane proteins, primarily due to the lack of high-resolution structural data for transmembrane proteins, which render many of the knowledge-based method unreliable or invalid. We have developed a method that harnesses the vast structural knowledge available in soluble protein data for use in the modeling of transmembrane proteins. At the core of the method, a set of transmembrane protein decoy sets that allow us to filter and train features recognized from soluble proteins for transmembrane protein modeling into a set of scoring functions. We have demonstrated that structures of soluble proteins can provide significant insight into transmembrane protein structures. A complementary novel two-stage modeling/selection process that mimics the two-stage helical membrane protein folding was developed. Combined with the scoring function, the method was successfully applied to model 5 transmembrane proteins. The root mean square deviations of the predicted models ranged from 5.0 to 8.8?Å to the native structures.     

How strongly do sequence conservation patterns and empirical scales correlate with exposure patterns of transmembrane helices of membrane proteins?

Given the difficulty in determining high-resolution structures of helical membrane proteins, sequence-based prediction methods can be useful in elucidating diverse physiological processes mediated by this important class of proteins. Predicting the angular orientations of transmembrane (TM) helices about the helix axes, based on the helix parameters from electron microscopy data, is a classical problem in this regard. This problem has triggered the development of a number of different empirical scales. Recently, sequence conservation patterns were also made use of for improved predictions. Empirical scales and sequence conservation patterns (collectively termed as "prediction scales") have also found frequent applications in other research areas of membrane proteins: for example, in structure modeling and in prediction of buried TM helices. This trend is expected to grow in the near future unless there are revolutionary developments in the experimental characterization of membrane proteins. Thus, it is timely and imperative to carry out a comprehensive benchmark test over the prediction scales proposed so far to determine their pros and cons. In the current analysis, we use exposure patterns of TM helices as a golden standard, because if one develops a prediction scale that correlates perfectly with exposure patterns of TM helices, it will enable one to predict buried residues (or buried faces) of TM helices with an accuracy of 100%. Our analysis reveals several important points. (1) It demonstrates that sequence conservation patterns are much more strongly correlated with exposure patterns of TM helices than empirical scales. (2) Scales that were specifically parameterized using structure data (structure-based scales) display stronger correlation than hydrophobicity-based scales, as expected. (3) A nonnegligible difference is observed among the structure-based scales in their correlational property, suggesting that not every learning algorithm is equally effective. (4) A straightforward framework of optimally combining sequence conservation patterns and empirical scales is proposed, which reveals that improvements gained from combining the two sources of information are not dramatic in almost all cases. In turn, this calls for the development of fundamentally different scales that capture the essentials of membrane protein folding for substantial improvements.     

Improving the accuracy of transmembrane protein topology prediction using evolutionary information

MOTIVATION: Many important biological processes such as cell signaling, transport of membrane-impermeable molecules, cell-cell communication, cell recognition and cell adhesion are mediated by membrane proteins. Unfortunately, as these proteins are not water soluble, it is extremely hard to experimentally determine their structure. Therefore, improved methods for predicting the structure of these proteins are vital in biological research. In order to improve transmembrane topology prediction, we evaluate the combined use of both integrated signal peptide prediction and evolutionary information in a single algorithm. RESULTS: A new method (MEMSAT3) for predicting transmembrane protein topology from sequence profiles is described and benchmarked with full cross-validation on a standard data set of 184 transmembrane proteins. The method is found to predict both the correct topology and the locations of transmembrane segments for 80% of the test set. This compares with accuracies of 62-72% for other popular methods on the same benchmark. By using a second neural network specifically to discriminate transmembrane from globular proteins, a very low overall false positive rate (0.5%) can also be achieved in detecting transmembrane proteins. AVAILABILITY: An implementation of the described method is available both as a web server (http://www.psipred.net) and as downloadable source code from http://bioinf.cs.ucl.ac.uk/memsat. Both the server and source code files are free to non-commercial users. Benchmark and training data are also available from http://bioinf.cs.ucl.ac.uk/memsat.     

An investigation into the ability to define transmembrane protein spans using the biophysical properties of amino acid residues

A key question associated with topology predictions for membrane proteins is whether there is sufficient variation in the biophysical properties of residues at the membrane interface to enable identification of TM spans in a robust and efficient manner using relatively simple methods of analysis. Here, a test for the homogeneity of multinomial populations is used to identify statistical differences between the residue compositions of windows within datasets of aligned non-homologous TM α-helices. Using this approach, the accuracy and robustness of the predicted boundaries for datasets of uncleaved signal (US) sequences and stop transfer sequences (ST) is tested. The validity of the 21 residue length, which is generally assumed for TM spans in membrane protein topology prediction is also investigated and it is suggested that ST sequences may be better represented by a length of 22 residues.     

Membrane protein prediction methods

We survey computational approaches that tackle membrane protein structure and function prediction. While describing the main ideas that have led to the development of the most relevant and novel methods, we also discuss pitfalls, provide practical hints and highlight the challenges that remain. The methods covered include: sequence alignment, motif search, functional residue identification, transmembrane segment and protein topology predictions, homology and ab initio modeling. In general, predictions of functional and structural features of membrane proteins are improving, although progress is hampered by the limited amount of high-resolution experimental information available. While predictions of transmembrane segments and protein topology rank among the most accurate methods in computational biology, more attention and effort will be required in the future to ameliorate database search, homology and ab initio modeling.     

Families of membranous proteins can be characterized by the amino acid composition of their transmembrane domains

SUMMARY: In eukaryotes, membranous proteins account for 20-30% of the proteome. Most of these proteins contain one or more transmembrane (TM) domains. These are short segments that transverse the bilayer lipid membrane. Various properties of the TM domains, such as their number, their topology and their arrangement within the membrane, are closely related to the protein's cellular functions. The properties of the TM domains also determine the cellular targeting and localization of these proteins. It is not known, however, whether the information encoded by TM domains suffices for the purpose of classifying proteins into their functional families. This is the question we address here. We introduce an algorithm that creates a profile of each functional family of membranous proteins based only on the amino acid composition of their TM domains. This is complemented by a classifier program for each such family (to determine whether a given protein belongs to it or not). We find that in most instances TM domains contain enough information to allow an accurate discrimination of approximately 80% sensitivity and approximately 90% specificity among unrelated polytopic functional families with the same number of TM domains. SUPPLEMENTARY INFORMATION: Available at www.protonet.cs.huji.ac.il/TM/     

Best alpha-helical transmembrane protein topology predictions are achieved using hidden Markov models and evolutionary information

Methods that predict the topology of helical membrane proteins are standard tools when analyzing any proteome. Therefore, it is important to improve the performance of such methods. Here we introduce a novel method, PRODIV-TMHMM, which is a profile-based hidden Markov model (HMM) that also incorporates the best features of earlier HMM methods. In our tests, PRODIV-TMHMM outperforms earlier methods both when evaluated on "low-resolution" topology data and on high-resolution 3D structures. The results presented here indicate that the topology could be correctly predicted for approximately two-thirds of all membrane proteins using PRODIV-TMHMM. The importance of evolutionary information for topology prediction is emphasized by the fact that compared with using single sequences, the performance of PRODIV-TMHMM (as well as two other methods) is increased by approximately 10 percentage units by the use of homologous sequences. On a more general level, we also show that HMM-based (or similar) methods perform superiorly to methods that focus mainly on identification of the membrane regions.     

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.     

TMPDB: a database of experimentally-characterized transmembrane topologies

TMPDB is a database of experimentally-characterized transmembrane (TM) topologies. TMPDB release 6.2 contains a total of 302 TM protein sequences, in which 276 are alpha-helical sequences, 17 beta-stranded, and 9 alpha-helical sequences with short pore-forming helices buried in the membrane. The TM topologies in TMPDB were determined experimentally by means of X-ray crystallography, NMR, gene fusion technique, substituted cysteine accessibility method, N-linked glycosylation experiment and other biochemical methods. TMPDB would be useful as a test and/or training dataset in improving the proposed TM topology prediction methods or developing novel methods with higher performance, and as a guide for both the bioinformaticians and biologists to better understand TM proteins. TMPDB and its subsets are freely available at the following web site: http://bioinfo.si.hirosaki-u.ac.jp/~TMPDB/.     

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.