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.     

HMMpTM: Improving transmembrane protein topology prediction using phosphorylation and glycosylation site prediction

During the last two decades a large number of computational methods have been developed for predicting transmembrane protein topology. Current predictors rely on topogenic signals in the protein sequence, such as the distribution of positively charged residues in extra-membrane loops and the existence of N-terminal signals. However, phosphorylation and glycosylation are post-translational modifications (PTMs) that occur in a compartment-specific manner and therefore the presence of a phosphorylation or glycosylation site in a transmembrane protein provides topological information. We examine the combination of phosphorylation and glycosylation site prediction with transmembrane protein topology prediction. We report the development of a Hidden Markov Model based method, capable of predicting the topology of transmembrane proteins and the existence of kinase specific phosphorylation and N/O-linked glycosylation sites along the protein sequence. Our method integrates a novel feature in transmembrane protein topology prediction, which results in improved performance for topology prediction and reliable prediction of phosphorylation and glycosylation sites. The method is freely available at http://bioinformatics.biol.uoa.gr/HMMpTM.     

The HMMTOP transmembrane topology prediction server

The HMMTOP transmembrane topology prediction server predicts both the localization of helical transmembrane segments and the topology of transmembrane proteins. Recently, several improvements have been introduced to the original method. Now, the user is allowed to submit additional information about segment localization to enhance the prediction power. This option improves the prediction accuracy as well as helps the interpretation of experimental results, i.e. in epitope insertion experiments. Availability: HMMTOP 2.0 is freely available to non-commercial users at http://www.enzim.hu/hmmtop. Source code is also available upon request to academic users.     

Transmembrane protein topology prediction using support vector machines

Background  

Alpha-helical transmembrane (TM) proteins are involved in a wide range of important biological processes such as cell signaling, transport of membrane-impermeable molecules, cell-cell communication, cell recognition and cell adhesion. Many are also prime drug targets, and it has been estimated that more than half of all drugs currently on the market target membrane proteins. However, due to the experimental difficulties involved in obtaining high quality crystals, this class of protein is severely under-represented in structural databases. In the absence of structural data, sequence-based prediction methods allow TM protein topology to be investigated.     

Topology prediction of Brucella abortus Omp2b and Omp2a porins after critical assessment of transmembrane beta strands prediction by several secondary structure prediction methods

In order to propose a reliable model for Brucella porin topology, several structure prediction methods were evaluated in their ability to predict porin topology. Four porins of known structure were selected as test-cases and their secondary structure delineated. The specificity and sensitivity of 11 methods were separately evaluated. Our critical assessment shows that some secondary structure prediction methods (PHD, Dsc, Sopma) originally designed to predict globular protein structure are useful on porin topology prediction. The overall best prediction is obtained by combining these three "generalist" methods with a transmembrane beta strand prediction technique. This "consensus" method was applied to Brucella porins Omp2b and Omp2a, sharing no sequence homology with any other porin. The predicted topology is a 16-stranded antiparallel beta barrel with Omp2a showing a higher number of negatively charged residue in the exposed loops than Omp2b. Experiments are in progress to validate the proposed topology and the functional hypotheses. The ability of the proposed consensus method to predict topology of complex outer membrane protein is briefly discussed.     

Reliability measures for membrane protein topology prediction algorithms

We have developed reliability scores for five widely used membrane protein topology prediction methods, and have applied them both on a test set of 92 bacterial plasma membrane proteins with experimentally determined topologies and on all predicted helix bundle membrane proteins in three fully sequenced genomes: Escherichia coli, Saccharomyces cerevisiae and Caenorhabditis elegans. We show that the reliability scores work well for the TMHMM and MEMSAT methods, and that they allow the probability that the predicted topology is correct to be estimated for any protein. We further show that the available test set is biased towards high-scoring proteins when compared to the genome-wide data sets, and provide estimates for the expected prediction accuracy of TMHMM across the three genomes. Finally, we show that the performance of TMHMM is considerably better when limited experimental information (such as the in/out location of a protein's C terminus) is available, and estimate that at least ten percentage points in overall accuracy in whole-genome predictions can be gained in this way.     

Enhanced membrane protein topology prediction using a hierarchical classification method and a new scoring function

The prediction of transmembrane (TM) helix and topology provides important information about the structure and function of a membrane protein. Due to the experimental difficulties in obtaining a high-resolution model, computational methods are highly desirable. In this paper, we present a hierarchical classification method using support vector machines (SVMs) that integrates selected features by capturing the sequence-to-structure relationship and developing a new scoring function based on membrane protein folding. The proposed approach is evaluated on low- and high-resolution data sets with cross-validation, and the topology (sidedness) prediction accuracy reaches as high as 90%. Our method is also found to correctly predict both the location of TM helices and the topology for 69% of the low-resolution benchmark set. We also test our method for discrimination between soluble and membrane proteins and achieve very low overall false positive (0.5%) and false negative rates (0 to approximately 1.2%). Lastly, the analysis of the scoring function suggests that the topogeneses of single-spanning and multispanning TM proteins have different levels of complexity, and the consideration of interloop topogenic interactions for the latter is the key to achieving better predictions. This method can facilitate the annotation of membrane proteomes to extract useful structural and functional information. It is publicly available at http://bio-cluster.iis.sinica.edu.tw/~bioapp/SVMtop.     

Reorientation of aquaporin-1 topology during maturation in the endoplasmic reticulum

The topology of most eukaryotic polytopic membrane proteins is established cotranslationally in the endoplasmic reticulum (ER) through a series of coordinated translocation and membrane integration events. For the human aquaporin water channel AQP1, however, the initial four-segment-spanning topology at the ER membrane differs from the mature six-segment-spanning topology at the plasma membrane. Here we use epitope-tagged AQP1 constructs to follow the transmembrane (TM) orientation of key internal peptide loops in Xenopus oocyte and cell-free systems. This analysis revealed that AQP1 maturation in the ER involves a novel topological reorientation of three internal TM segments and two peptide loops. After the synthesis of TMs 4-6, TM3 underwent a 180-degree rotation in which TM3 C-terminal flanking residues were translocated from their initial cytosolic location into the ER lumen and N-terminal flanking residues underwent retrograde translocation from the ER lumen to the cytosol. These events convert TM3 from a type I to a type II topology and reposition TM2 and TM4 into transmembrane conformations consistent with the predicted six-segment-spanning AQP1 topology. AQP1 topological reorientation was also associated with maturation from a protease-sensitive conformation to a protease-resistant structure with water channel function. These studies demonstrate that initial protein topology established via cotranslational translocation events in the ER is dynamic and may be modified by subsequent steps of folding and/or maturation.     

Consensus predictions of membrane protein topology

We have explored the possibility that consensus predictions of membrane protein topology might provide a means to estimate the reliability of a predicted topology. Using five current topology prediction methods and a test set of 60 Escherichia coli inner membrane proteins with experimentally determined topologies, we find that prediction performance varies strongly with the number of methods that agree, and that the topology of nearly half of all E. coli inner membrane proteins can be predicted with high reliability (>90% correct predictions) by a simple majority-vote approach.     

An improved hidden Markov model for transmembrane protein detection and topology prediction and its applications to complete genomes

MOTIVATION: Knowledge of the transmembrane helical topology can help identify binding sites and infer functions for membrane proteins. However, because membrane proteins are hard to solubilize and purify, only a very small amount of membrane proteins have structure and topology experimentally determined. This has motivated various computational methods for predicting the topology of membrane proteins. RESULTS: We present an improved hidden Markov model, TMMOD, for the identification and topology prediction of transmembrane proteins. Our model uses TMHMM as a prototype, but differs from TMHMM by the architecture of the submodels for loops on both sides of the membrane and also by the model training procedure. In cross-validation experiments using a set of 83 transmembrane proteins with known topology, TMMOD outperformed TMHMM and other existing methods, with an accuracy of 89% for both topology and locations. In another experiment using a separate set of 160 transmembrane proteins, TMMOD had 84% for topology and 89% for locations. When utilized for identifying transmembrane proteins from non-transmembrane proteins, particularly signal peptides, TMMOD has consistently fewer false positives than TMHMM does. Application of TMMOD to a collection of complete genomes shows that the number of predicted membrane proteins accounts for approximately 20-30% of all genes in those genomes, and that the topology where both the N- and C-termini are in the cytoplasm is dominant in these organisms except for Caenorhabditis elegans. AVAILABILITY: http://liao.cis.udel.edu/website/servers/TMMOD/     

The use of functional domains to improve transmembrane protein topology prediction

Transmembrane proteins affect vital cellular functions and pathogenesis, and are a focus of drug design. It is difficult to obtain diffraction quality crystals to study transmembrane protein structure. Computational tools for transmembrane protein topology prediction fill in the gap between the abundance of transmembrane proteins and the scarcity of known membrane protein structures. Their prediction accuracy is still inadequate: TMHMM, the current state-of-the-art method, has less than 52% accuracy in topology prediction on one set of transmembrane proteins of known topology. Based on the observation that there are functional domains that occur preferentially internal or external to the membrane, we have extended the model of TMHMM to incorporate functional domains, using a probabilistic approach originally developed for computational gene finding. Our extension is better than TMHMM in predicting the topology of transmembrane proteins. As prediction of functional domain improves, our system's prediction accuracy will likely improve as well.     

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.     

A combined transmembrane topology and signal peptide prediction method

An inherent problem in transmembrane protein topology prediction and signal peptide prediction is the high similarity between the hydrophobic regions of a transmembrane helix and that of a signal peptide, leading to cross-reaction between the two types of predictions. To improve predictions further, it is therefore important to make a predictor that aims to discriminate between the two classes. In addition, topology information can be gained when successfully predicting a signal peptide leading a transmembrane protein since it dictates that the N terminus of the mature protein must be on the non-cytoplasmic side of the membrane. Here, we present Phobius, a combined transmembrane protein topology and signal peptide predictor. The predictor is based on a hidden Markov model (HMM) that models the different sequence regions of a signal peptide and the different regions of a transmembrane protein in a series of interconnected states. Training was done on a newly assembled and curated dataset. Compared to TMHMM and SignalP, errors coming from cross-prediction between transmembrane segments and signal peptides were reduced substantially by Phobius. False classifications of signal peptides were reduced from 26.1% to 3.9% and false classifications of transmembrane helices were reduced from 19.0% to 7.7%. Phobius was applied to the proteomes of Homo sapiens and Escherichia coli. Here we also noted a drastic reduction of false classifications compared to TMHMM/SignalP, suggesting that Phobius is well suited for whole-genome annotation of signal peptides and transmembrane regions. The method is available at as well as at     

Rapid membrane protein topology prediction

State-of-the-art methods for topology of α-helical membrane proteins are based on the use of time-consuming multiple sequence alignments obtained from PSI-BLAST or other sources. Here, we examine if it is possible to use the consensus of topology prediction methods that are based on single sequences to obtain a similar accuracy as the more accurate multiple sequence-based methods. Here, we show that TOPCONS-single performs better than any of the other topology prediction methods tested here, but ~6% worse than the best method that is utilizing multiple sequence alignments. AVAILABILITY AND IMPLEMENTATION: TOPCONS-single is available as a web server from http://single.topcons.net/ and is also included for local installation from the web site. In addition, consensus-based topology predictions for the entire international protein index (IPI) is available from the web server and will be updated at regular intervals.     

Two families of mechanosensitive channel proteins.

Mechanosensitive (MS) channels that provide protection against hypoosmotic shock are found in the membranes of organisms from the three domains of life: bacteria, archaea, and eucarya. Two families of ubiquitous MS channels are recognized, and these have been designated the MscL and MscS families. A high-resolution X-ray crystallographic structure is available for a member of the MscL family, and extensive molecular genetic, biophysical, and biochemical studies conducted in many laboratories have allowed postulation of a gating mechanism allowing the interconversion of a tightly closed state and an open state that controls transmembrane ion and metabolite fluxes. In contrast to the MscL channel proteins, which are of uniform topology, the much larger MscS family includes protein members with topologies that are predicted to vary from 3 to 11 alpha-helical transmembrane segments (TMSs) per polypeptide chain. Sequence analyses reveal that the three C-terminal TMSs of MscS channel proteins are conserved among family members and that the third of these three TMSs exhibits a 20-residue motif that is shared by the channel-forming TMS (TMS 1) of the MscL proteins. We propose that this C-terminal TMS in MscS family homologues serves as the channel-forming helix in a homooligomeric structure. The presence of a conserved residue pattern for the putative channel-forming TMSs in the MscL and MscS family proteins suggests a common structural organization, gating mechanism, and evolutionary origin.     

Transmembrane topology of the Acr3 family arsenite transporter from Bacillus subtilis

The transmembrane topology of the Acr3 family arsenite transporter Acr3 from Bacillus subtilis was analysed experimentally using translational fusions with alkaline phosphatase and green fluorescent protein and in silico by topology modelling. Initial topology prediction resulted in two models with 9 and 10 TM helices respectively. 32 fusion constructs were made between truncated forms of acr3 and the reporter genes at 17 different sites throughout the acr3 sequence to discriminate between these models. Nine strong reporter protein signals provided information about the majority of the locations of the cytoplasmic and extracellular loops of Acr3 and showed that both the N- and the C-termini are located in the cytoplasm. Two ambiguous data points indicated the possibility of an alternative 8 helix topology. This possibility was investigated using another 10 fusion variants, but no experimental support for the 8 TM topology was obtained. We therefore conclude that Acr3 has 10 transmembrane helices. Overall, the loops which connect the membrane spanning segments are short, with cytoplasmic loops being somewhat longer than the extracellular loops. The study provides the first ever experimentally derived structural information on a protein of the Acr3 family which constitutes one of the largest classes of arsenite transporters.     

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.     

Two Families of Mechanosensitive Channel Proteins

Mechanosensitive (MS) channels that provide protection against hypoosmotic shock are found in the membranes of organisms from the three domains of life: bacteria, archaea, and eucarya. Two families of ubiquitous MS channels are recognized, and these have been designated the MscL and MscS families. A high-resolution X-ray crystallographic structure is available for a member of the MscL family, and extensive molecular genetic, biophysical, and biochemical studies conducted in many laboratories have allowed postulation of a gating mechanism allowing the interconversion of a tightly closed state and an open state that controls transmembrane ion and metabolite fluxes. In contrast to the MscL channel proteins, which are of uniform topology, the much larger MscS family includes protein members with topologies that are predicted to vary from 3 to 11 α-helical transmembrane segments (TMSs) per polypeptide chain. Sequence analyses reveal that the three C-terminal TMSs of MscS channel proteins are conserved among family members and that the third of these three TMSs exhibits a 20-residue motif that is shared by the channel-forming TMS (TMS 1) of the MscL proteins. We propose that this C-terminal TMS in MscS family homologues serves as the channel-forming helix in a homooligomeric structure. The presence of a conserved residue pattern for the putative channel-forming TMSs in the MscL and MscS family proteins suggests a common structural organization, gating mechanism, and evolutionary origin.     

MetaTM - a consensus method for transmembrane protein topology prediction

Background  

Transmembrane (TM) proteins are proteins that span a biological membrane one or more times. As their 3-D structures are hard to determine, experiments focus on identifying their topology (i. e. which parts of the amino acid sequence are buried in the membrane and which are located on either side of the membrane), but only a few topologies are known. Consequently, various computational TM topology predictors have been developed, but their accuracies are far from perfect. The prediction quality can be improved by applying a consensus approach, which combines results of several predictors to yield a more reliable result.     

How accurately can we discriminate G-protein-coupled receptors as 7-tms TM protein sequences from other sequences?

The group of 2502 transmembrane (TM) protein sequences with seven TM segments (7-tms) registered in SWISS-PROT 46.0 contains 2200 G-protein-coupled receptors (GPCRs), indicating that GPCR candidates can be detected with a reliability of 87.9% in the eukaryotic genomes merely by correctly predicting the number of TM segments as 7-tms. The predictive accuracies of TM topology-prediction methods proposed so far are not as high as expected; even the best method, HMMTOP 2.0, can only achieve a capture rate of 7-tms sequences of 77.6%. It is necessary to improve this performance as much as possible, even if by only a few percentage points, in order to identify as many novel GPCR candidate genes as possible among the increasing number of newly sequenced genomes. In this study, we propose a simple but useful prediction method for detecting as many 7-tms TM protein sequences as GPCR candidates in eukaryotic genomes as possible. This is achieved by employing a two-step prediction procedure. The first step involves collecting 7-tms sequences by the best prediction method (HMMTOP 2.0), and the second involves picking up the remaining 7-tms sequences by the second-best method (TMHMM 2.0). By this procedure, the capture rate of 7-tms TM protein sequences in SWISS-PROT can be improved considerably from 77.6% to 84.5%, and the number of GPCR candidate sequences predicted as 7-tms in the human genome (Build 35) is increased from 790 (by HMMTOP 2.0) to 903. These 790 and 903 candidate sequences include, respectively, 587 and 636 of the known human GPCRs of the 717 registered in SWISS-PROT 46.0, demonstrating that the proposed combinatorial method is effective in detecting GPCR candidate genes in eukaryotic genomes.