A Simple Method for Predicting Transmembrane Proteins Based on Wavelet Transform

The increasing protein sequences from the genome project require theoretical methods to predict transmembrane helical segments (TMHs). So far, several prediction methods have been reported, but there are some deficiencies in prediction accuracy and adaptability in these methods. In this paper, a method based on discrete wavelet transform (DWT) has been developed to predict the number and location of TMHs in membrane proteins. PDB coded as 1KQG is chosen as an example to describe the prediction process by this method. 80 proteins with known 3D structure from Mptopo database are chosen at random as data sets (including 325 TMHs) and 80 sequences are divided into 13 groups according to their function and type. TMHs prediction is carried out for each group of membrane protein sequences and obtain satisfactory result. To verify the feasibility of this method, 80 membrane protein sequences are treated as test sets, 308 TMHs can be predicted and the prediction accuracy is 96.3%. Compared with the main prediction results of seven popular prediction methods, the obtained results indicate that the proposed method in this paper has higher prediction accuracy.     

High-Resolution Modeling of Transmembrane Helical Protein Structures from Distant Homologues

Eukaryotic transmembrane helical (TMH) proteins perform a wide diversity of critical cellular functions, but remain structurally largely uncharacterized and their high-resolution structure prediction is currently hindered by the lack of close structural homologues. To address this problem, we present a novel and generic method for accurately modeling large TMH protein structures from distant homologues exhibiting distinct loop and TMH conformations. Models of the adenosine A2AR and chemokine CXCR4 receptors were first ranked in GPCR-DOCK blind prediction contests in the receptor structure accuracy category. In a benchmark of 50 TMH protein homolog pairs of diverse topology (from 5 to 12 TMHs), size (from 183 to 420 residues) and sequence identity (from 15% to 70%), the method improves most starting templates, and achieves near-atomic accuracy prediction of membrane-embedded regions. Unlike starting templates, the models are of suitable quality for computer-based protein engineering: redesigned models and redesigned X-ray structures exhibit very similar native interactions. The method should prove useful for the atom-level modeling and design of a large fraction of structurally uncharacterized TMH proteins from a wide range of structural homologues.     

SOMPNN: an efficient non-parametric model for predicting transmembrane helices

Accurately predicting the transmembrane helices (TMH) in a helical membrane protein is an important but challenging task. Recent researches have demonstrated that statistics-based methods are promising routes to improve the TMH prediction accuracy. However, most of existing TMH predictors are parametric models and they have to make assumptions of several or even hundreds of adjustable parameters based on the underlying probability distribution, which is difficult when no a priori knowledge is available. Besides the performances of these parametric predictors significantly depend on the estimated parameters, some of them need to exploit the entire training dataset in the prediction stage, which will lead to low prediction efficiency and this problem will become even worse when dealing with large-scale dataset. In this paper, we propose a novel SOMPNN model for prediction of TMH that features by minimal parameter assumptions requirement and high computational efficiency. In the SOMPNN model, a self-organizing map (SOM) is used to adaptively learn the helices distribution knowledge hidden in the training data, and then a probabilistic neural network (PNN) is adopted to predict TMH segments based on the knowledge learned by SOM. Experimental results on two benchmark datasets show that the proposed SOMPNN outperforms most existing popular TMH predictors and is promising to be extended to deal with other complicated biological problems. The datasets and the source codes of SOMPNN are available at http://www.csbio.sjtu.edu.cn/bioinf/SOMPNN/.     

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.     

Computer-based comparison of structural features of envelope protein of Alkhurma hemorrhagic fever virus with the homologous proteins of two closest viruses

The aim of this study was prediction of epitopes and medically important structural properties of protein E of Alkhurma hemorrhagic fever virus (AHFV) and comparing these features with two closely relates viruses, i.e. Kyasanur Forest disease virus (KFDV) and Tick-borne encephalitis virus (TBEV) by bioinformatics tools. Prediction of evolutionary distance, localization, sequence of signal peptides, C, N O glycosylation sites, transmembrane helices (TMHs), cysteine bond positions and B cell and T cell epitopes of E proteins were performed. 2D-MH, Virus-PLoc, Signal-CF, EnsembleGly, MemBrain, DiANNA, BCPREDS and MHCPred servers were applied for the prediction. According to the results, the evolutionary distance of E protein of AHFV and two other viruses was almost equal. In all three proteins of study, residues 1-35 were predicted as signal sequences and one asparagine was predicted to be glycosylated. Results of prediction of transmembrane helices showed one TMH at position 444-467 and the other one at position 476-490. Twelve cysteines were potentially involved to form six disulfide bridges in the proteins. Four parts were predicted as B cell epitopes in E protein of AHFV. One epitope was conserved between three proteins of study. The only conserved major histocompatibility complex (MHC) binding epitope between three viruses was for DRB0401 allele. As there are not much experimental data available about AHFV, computer-aided study and comparison of E protein of this virus with two closely related flaviviruses can help in better understanding of medical properties of the virus.     

Twelve Transmembrane Helices Form the Functional Core of Mammalian MATE1 (Multidrug and Toxin Extruder 1) Protein

The x-ray structure of the prototypic MATE family member, NorM from Vibrio cholerae, reveals a protein fold composed of 12 transmembrane helices (TMHs), confirming hydropathy analyses of the majority of (prokaryotic and plant) MATE transporters. However, the mammalian MATEs are generally predicted to have a 13(th) TMH and an extracellular C terminus. Here we affirm this prediction, showing that the C termini of epitope-tagged, full-length human, rabbit, and mouse MATE1 were accessible to antibodies from the extracellular face of the membrane. Truncation of these proteins at or near the predicted junction between the 13(th) TMH and the long cytoplasmic loop that precedes it resulted in proteins that (i) trafficked to the membrane and (ii) interacted with antibodies only after permeabilization of the plasma membrane. CHO cells expressing rbMate1 truncated at residue Gly-545 supported levels of pH-sensitive transport similar to that of cells expressing the full-length protein. Although the high transport rate of the Gly-545 truncation mutant was associated with higher levels of membrane expression (than full-length MATE1), suggesting the 13(th) TMH may influence substrate translocation, the selectivity profile of the mutant indicated that TMH13 has little impact on ligand binding. We conclude that the functional core of MATE1 consists of 12 (not 13) TMHs. Therefore, we used the x-ray structure of NorM to develop a homology model of the first 12 TMHs of MATE1. The model proved to be stable in molecular dynamic simulations and agreed with topology evident from preliminary cysteine scanning of intracellular versus extracellular loops.     

Enhanced recognition of protein transmembrane domains with prediction-based structural profiles

MOTIVATION: Membrane domain prediction has recently been re-evaluated by several groups, suggesting that the accuracy of existing methods is still rather limited. In this work, we revisit this problem and propose novel methods for prediction of alpha-helical as well as beta-sheet transmembrane (TM) domains. The new approach is based on a compact representation of an amino acid residue and its environment, which consists of predicted solvent accessibility and secondary structure of each amino acid. A recently introduced method for solvent accessibility prediction trained on a set of soluble proteins is used here to indicate segments of residues that are predicted not to be accessible to water and, therefore, may be 'buried' in the membrane. While evolutionary profiles in the form of a multiple alignment are used to derive these simple 'structural profiles', they are not used explicitly for the membrane domain prediction and the overall number of parameters in the model is significantly reduced. This offers the possibility of a more reliable estimation of the free parameters in the model with a limited number of experimentally resolved membrane protein structures. RESULTS: Using cross-validated training on available sets of structurally resolved and non-redundant alpha and beta membrane proteins, we demonstrate that membrane domain prediction methods based on such a compact representation outperform approaches that utilize explicitly evolutionary profiles and multiple alignments. Moreover, using an external evaluation by the TMH Benchmark server we show that our final prediction protocol for the TM helix prediction is competitive with the state-of-the-art methods, achieving per-residue accuracy of approximately 89% and per-segment accuracy of approximately 80% on the set of high resolution structures used by the TMH Benchmark server. At the same time the observed rates of confusion with signal peptides and globular proteins are the lowest among the tested methods. The new method is available online at http://minnou.cchmc.org.     

Large Tilts in Transmembrane Helices Can Be Induced during Tertiary Structure Formation

While early structural models of helix-bundle integral membrane proteins posited that the transmembrane α-helices [transmembrane helices (TMHs)] were orientated more or less perpendicular to the membrane plane, there is now ample evidence from high-resolution structures that many TMHs have significant tilt angles relative to the membrane. Here, we address the question whether the tilt is an intrinsic property of the TMH in question or if it is imparted on the TMH during folding of the protein. Using a glycosylation mapping technique, we show that four highly tilted helices found in multi-spanning membrane proteins all have much shorter membrane-embedded segments when inserted by themselves into the membrane than seen in the high-resolution structures. This suggests that tilting can be induced by tertiary packing interactions within the protein, subsequent to the initial membrane-insertion step.     

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.     

Effects of 'hydrophobic mismatch' on the location of transmembrane helices in the ER membrane

We have studied the effects of 'hydrophobic mismatch' between a poly-Leu transmembrane helix (TMH) and the ER membrane using a glycosylation mapping approach. The simplest interpretation of our results is that the lumenal end of the TMH is located deeper in the membrane for both short (negative mismatch) and long (positive mismatch) TMHs than for poly-Leu segments of intermediate length. We further find that the position-specific effect of Lys residues on the location of short TMHs in the membrane varies with an apparent helical periodicity when the Lys residue is moved along the poly-Leu stretch. We discuss these findings in the context of models for peptide-lipid interactions during hydrophobic mismatch.     

Motifs in outer membrane protein sequences: applications for discrimination

Discriminating outer membrane proteins (OMPs) from other folding types of globular and membrane proteins is an important problem for predicting their secondary and tertiary structures and detecting outer membrane proteins from genomic sequences as well. In this work, we have systematically analyzed the distribution of amino acid residues in the sequences of globular and outer membrane proteins with several motifs, such as A*B, A**B, etc. We observed that the motifs E*L, A*K and L*E occur frequently in globular proteins while S*S, N*S and R*D predominantly occur in OMPs. We have devised a statistical method based on frequently occurring motifs in globular and OMPs and obtained an accuracy of 96% and 82% for correctly identifying OMPs and excluding globular proteins, respectively. Further, we noticed that the motifs of transmembrane helical (TMH) proteins are different from that of OMPs. While I*A, I*L and L*I prefer in TMH proteins S*S, N*S and N*N predominantly occur in OMPs. The information about the occurrence of A*B motifs in TMH and OMPs could discriminate them with an accuracy of 80% for excluding OMPs and 100% for identifying OMPs. The influence of protein size and structural class for discrimination is discussed.     

Interaction of transmembrane helices in ATP synthase subunit a in solution as revealed by spin label difference NMR

Subunit a in the membrane traversing F0 sector of Escherichia coli ATP synthase is known to fold with five transmembrane helices (TMHs) with residue 218 in TMH IV packing close to residue 248 in TMH V. In this study, we have introduced a spin label probe at Cys residues substituted at positions 222 or 223 and measured the effects on the Trp epsilon NH indole NMR signals of the seven Trp residues in the protein. The protein was purified and NMR experiments were carried out in a chloroform-methanol-H2O (4:4:1) solvent mixture. The spin label at positions 222 or 223 proved to broaden the signals of W231, W232, W235 and W241 located at the periplasmic ends of TMH IV and TMH V and the connecting loop between these helices. The broadening of W241 would require that the loop residues fold back on themselves in a hairpin-like structure much like it is predicted to fold in the native membrane. Placement of the spin label probe at several other positions also proved to have broadening effects on some of these Trp residues and provided additional constraints on folding of TMH IV and TMH V. The effects of the 223 probes on backbone amide resonances of subunit a were also measured by an HNCO experiment and the results are consistent with the two helices folding back on themselves in this solvent mixture. When Cys and Trp were substituted at residues 206 and 254 at the cytoplasmic ends of TMHs IV and V respectively, the W254 resonance was not broadened by the spin label at position 206. We conclude that the helices fold back on themselves in this solvent system and then pack at an angle such that the cytoplasmic ends of the polypeptide backbone are significantly displaced from each other.     

Improving transmembrane protein consensus topology prediction using inter-helical interaction

Alpha helix transmembrane proteins (αTMPs) represent roughly 30% of all open reading frames (ORFs) in a typical genome and are involved in many critical biological processes. Due to the special physicochemical properties, it is hard to crystallize and obtain high resolution structures experimentally, thus, sequence-based topology prediction is highly desirable for the study of transmembrane proteins (TMPs), both in structure prediction and function prediction. Various model-based topology prediction methods have been developed, but the accuracy of those individual predictors remain poor due to the limitation of the methods or the features they used. Thus, the consensus topology prediction method becomes practical for high accuracy applications by combining the advances of the individual predictors. Here, based on the observation that inter-helical interactions are commonly found within the transmembrane helixes (TMHs) and strongly indicate the existence of them, we present a novel consensus topology prediction method for αTMPs, CNTOP, which incorporates four top leading individual topology predictors, and further improves the prediction accuracy by using the predicted inter-helical interactions. The method achieved 87% prediction accuracy based on a benchmark dataset and 78% accuracy based on a non-redundant dataset which is composed of polytopic αTMPs. Our method derives the highest topology accuracy than any other individual predictors and consensus predictors, at the same time, the TMHs are more accurately predicted in their length and locations, where both the false positives (FPs) and the false negatives (FNs) decreased dramatically. The CNTOP is available at: http://ccst.jlu.edu.cn/JCSB/cntop/CNTOP.html.     

Interaction of transmembrane helices in ATP synthase subunit a in solution as revealed by spin label difference NMR

Subunit a in the membrane traversing F₀ sector of Escherichia coli ATP synthase is known to fold with five transmembrane helices (TMHs) with residue 218 in TMH IV packing close to residue 248 in TMH V. In this study, we have introduced a spin label probe at Cys residues substituted at positions 222 or 223 and measured the effects on the Trp ?NH indole NMR signals of the seven Trp residues in the protein. The protein was purified and NMR experiments were carried out in a chloroform-methanol-H₂O (4:4:1) solvent mixture. The spin label at positions 222 or 223 proved to broaden the signals of W231, W232, W235 and W241 located at the periplasmic ends of TMH IV and TMH V and the connecting loop between these helices. The broadening of W241 would require that the loop residues fold back on themselves in a hairpin-like structure much like it is predicted to fold in the native membrane. Placement of the spin label probe at several other positions also proved to have broadening effects on some of these Trp residues and provided additional constraints on folding of TMH IV and TMH V. The effects of the 223 probes on backbone amide resonances of subunit a were also measured by an HNCO experiment and the results are consistent with the two helices folding back on themselves in this solvent mixture. When Cys and Trp were substituted at residues 206 and 254 at the cytoplasmic ends of TMHs IV and V respectively, the W254 resonance was not broadened by the spin label at position 206. We conclude that the helices fold back on themselves in this solvent system and then pack at an angle such that the cytoplasmic ends of the polypeptide backbone are significantly displaced from each other.     

A Multilabel Model Based on Chou’s Pseudo–Amino Acid Composition for Identifying Membrane Proteins with Both Single and Multiple Functional Types

Predicting membrane protein type is a meaningful task because this kind of information is very useful to explain the function of membrane proteins. Due to the explosion of new protein sequences discovered, it is highly desired to develop efficient computation tools for quickly and accurately predicting the membrane type for a given protein sequence. Even though several membrane predictors have been developed, they can only deal with the membrane proteins which belong to the single membrane type. The fact is that there are membrane proteins belonging to two or more than two types. To solve this problem, a system for predicting membrane protein sequences with single or multiple types is proposed. Pseudo–amino acid composition, which has proven to be a very efficient tool in representing protein sequences, and a multilabel KNN algorithm are used to compose this prediction engine. The results of this initial study are encouraging.     

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.     

Predicting disulfide connectivity from protein sequence using multiple sequence feature vectors and secondary structure

MOTIVATION: Disulfide bonds are primary covalent crosslinks between two cysteine residues in proteins that play critical roles in stabilizing the protein structures and are commonly found in extracy-toplasmatic or secreted proteins. In protein folding prediction, the localization of disulfide bonds can greatly reduce the search in conformational space. Therefore, there is a great need to develop computational methods capable of accurately predicting disulfide connectivity patterns in proteins that could have potentially important applications. RESULTS: We have developed a novel method to predict disulfide connectivity patterns from protein primary sequence, using a support vector regression (SVR) approach based on multiple sequence feature vectors and predicted secondary structure by the PSIPRED program. The results indicate that our method could achieve a prediction accuracy of 74.4% and 77.9%, respectively, when averaged on proteins with two to five disulfide bridges using 4-fold cross-validation, measured on the protein and cysteine pair on a well-defined non-homologous dataset. We assessed the effects of different sequence encoding schemes on the prediction performance of disulfide connectivity. It has been shown that the sequence encoding scheme based on multiple sequence feature vectors coupled with predicted secondary structure can significantly improve the prediction accuracy, thus enabling our method to outperform most of other currently available predictors. Our work provides a complementary approach to the current algorithms that should be useful in computationally assigning disulfide connectivity patterns and helps in the annotation of protein sequences generated by large-scale whole-genome projects. AVAILABILITY: The prediction web server and Supplementary Material are accessible at http://foo.maths.uq.edu.au/~huber/disulfide     

Direct effects of ionizing radiation on integral membrane proteins. Noncovalent energy transfer requires specific interpeptide interactions

The 12 transmembrane alpha helices (TMHs) of human erythrocyte glucose transporter were individually cut by pepsin digestion as membrane-bound 2.5-3.5-kDa peptide fragments. Radiation-induced chemical degradation of these fragments showed an average target size of 34 kDa. This is 10-12 x larger than the average size of an individual TMH, demonstrating that a significant energy transfer occurs among these TMHs in the absence of covalent linkage. Heating this TMH preparation at 100 degrees C for 15 min reduced the target size to 5 kDa or less, suggesting that the noncovalent energy transfer requires specific helix-helix interactions. Purified phospholamban, a small (6-kDa) integral membrane protein containing a single TMH, formed a pentameric assembly in sodium dodecyl sulfate. The chemical degradation target size of this phospholamban pentamer was 5-6 kDa, illustrating that not all integral membrane protein assemblies permit intersubunit energy transfer. These findings together with other published observations suggest strongly that significant noncovalent energy transfer can occur within the tertiary and quaternary structure of membrane proteins and that as yet undefined proper molecular interactions are required for such covalent energy transfer. Our results with pepsin-digested glucose transporter also illustrate the importance of the interhelical interaction as a predominating force in maintaining the tertiary structure of a transmembrane protein.     

MetaDisorder: a meta-server for the prediction of intrinsic disorder in proteins

ABSTRACT: BACKGROUND: Intrinsically unstructured proteins (IUPs) lack a well-defined three-dimensional structure. Some of them may assume a locally stable structure under specific conditions, e.g. upon interaction with another molecule, while others function in a permanently unstructured state. The discovery of IUPs challenged the traditional protein structure paradigm, which stated that a specific well-defined structure defines the function of the protein. As of December 2011, approximately 60 methods for computational prediction of protein disorder from sequence have been made publicly available. They are based on different approaches, such as utilizing evolutionary information, energy functions, and various statistical and machine learning methods. RESULTS: Given the diversity of existing intrinsic disorder prediction methods, we decided to test whether it is possible to combine them into a more accurate meta-prediction method. We developed a method based on arbitrarily chosen 13 disorder predictors, in which the final consensus was weighted by the accuracy of the methods. We have also developed a disorder predictor GSmetaDisorder3D that used no third-party disorder predictors, but alignments to known protein structures, reported by the protein fold-recognition methods, to infer the potentially structured and unstructured regions. Following the success of our disorder predictors in the CASP8 benchmark, we combined them into a meta-meta predictor called GSmetaDisorderMD, which was the top scoring method in the subsequent CASP9 benchmark. CONCLUSIONS: A series of disorder predictors described in this article is available as a MetaDisorder web server at http://iimcb.genesilico.pl/metadisorder/. Results are presented both in an easily interpretable, interactive mode and in a simple text format suitable for machine processing.