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.     

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.     

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.     

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.     

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.     

Francisella tularensis RipA protein topology and identification of functional domains

Francisella tularensis is a Gram-negative coccobacillus and is the etiological agent of the disease tularemia. Expression of the cytoplasmic membrane protein RipA is required for Francisella replication within macrophages and other cell types; however, the function of this protein remains unknown. RipA is conserved among all sequenced Francisella species, and RipA-like proteins are present in a number of individual strains of a wide variety of species scattered throughout the prokaryotic kingdom. Cross-linking studies revealed that RipA forms homoligomers. Using a panel of RipA-green fluorescent protein and RipA-PhoA fusion constructs, we determined that RipA has a unique topology within the cytoplasmic membrane, with the N and C termini in the cytoplasm and periplasm, respectively. RipA has two significant cytoplasmic domains, one composed roughly of amino acids 1 to 50 and the second flanked by the second and third transmembrane domains and comprising amino acids 104 to 152. RipA functional domains were identified by measuring the effects of deletion mutations, amino acid substitution mutations, and spontaneously arising intragenic suppressor mutations on intracellular replication, induction of interleukin-1β (IL-1β) secretion by infected macrophages, and oligomer formation. Results from these experiments demonstrated that each of the cytoplasmic domains and specific amino acids within these domains are required for RipA function.     

Evaluating transmembrane topology prediction methods for the effect of signal peptide in topology prediction

Reported performance of existing transmembrane (TM) topology prediction methods were often based on evaluations which neglected the risk of signal peptides (SP) being predicted as putative TM as well. Here, we evaluated 12 selected TM topology prediction methods (TMpred, TopPred II, DAS, TMAP, MEMSAT 2, SOSUI, PRED-TMR2, TMHMM 2.0, HMMTOP 2.0, SPLIT 3.5, TM Finder, and MPEx) for the effect of SP in prediction performance considering three SP treatments, namely: "remain" (untreated), "removed first", and "removed later". The results showed that the presence of SP significantly affected the prediction performance of the 12 selected TM topology prediction methods for all three predicted attributes (the number of transmembrane segments (TMSs), the number of TMSs plus position, and the N-tail location) and for the predicted topology (combined predictions of three attributes) by causing a reduction in prediction accuracy. In particular, lower prediction accuracies were obtained if SP is left untreated (remain) while significant increases were observed if SP is removed either first or later. However, between "removed first" and "removed later" SP treatments, the difference was statistically insignificant. In addition, we found that machine learning-based prediction methods were less affected by the presence of SP than hydropathy-based methods, but still the potential risk of degrading the prediction performance is there however to a lesser degree. Thus, when performing genome-wide analysis, the SP issue should be addressed during TM topology prediction.     

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 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     

The transmembrane topology of Batten disease protein CLN3 determined by consensus computational prediction constrained by experimental data

The CLN3 gene encodes an integral membrane protein of unknown function. Mutations in CLN3 can cause juvenile neuronal ceroid lipofuscinosis, or Batten disease, an inherited neurodegenerative lysosomal storage disease affecting children. Here, we report a topological study of the CLN3 protein using bioinformatic approaches constrained by experimental data. Our results suggest that CLN3 has a six transmembrane helix topology with cytoplasmic N and C-termini, three large lumenal loops, one of which may contain an amphipathic helix, and one large cytoplasmic loop. Surprisingly, varied topological predictions were made using different subsets of orthologous sequences, highlighting the challenges still remaining for bioinformatics.     

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 MEMPACK alpha-helical transmembrane protein structure prediction server

MOTIVATION: The experimental difficulties of alpha-helical transmembrane protein structure determination make this class of protein an important target for sequence-based structure prediction tools. The MEMPACK prediction server allows users to submit a transmembrane protein sequence and returns transmembrane topology, lipid exposure, residue contacts, helix-helix interactions and helical packing arrangement predictions in both plain text and graphical formats using a number of novel machine learning-based algorithms. AVAILABILITY: The server can be accessed as a new component of the PSIPRED portal by at http://bioinf.cs.ucl.ac.uk/psipred/.     

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.     

Characterization of the topology and functional domains of RKTG

RKTG (Raf kinase trapping to Golgi) is exclusively localized at the Golgi apparatus and functions as a spatial regulator of Raf-1 kinase by sequestrating Raf-1 to the Golgi. Based on the structural similarity with adiponectin receptors, RKTG was predicted to be a seven-transmembrane protein with a cytosolic N-terminus, distinct from classical GPCRs (G-protein-coupled receptors). We analysed in detail the topology and functional domains of RKTG in this study. We determined that the N-terminus of RKTG is localized on the cytosolic side. Two short stretches of amino acid sequences at the membrane proximal to the N- and C-termini (amino acids 61-71 and 299-303 respectively) were indispensable for Golgi localization of RKTG, but were not required for the interaction with Raf-1. The three loops facing the cytosol between the transmembrane domains had different roles in Golgi localization and Raf-1 interaction. While the first cytosolic loop was only important for Golgi localization, the third cytosolic loop was necessary for both Golgi localization and Raf-1 sequestration. Taken together, these findings suggest that RKTG is a type III membrane protein with its N-terminus facing the cytosol and multiple sequences are responsible for its localization at the Golgi apparatus and Raf-1 interaction. As RKTG is the first discovered Golgi protein with seven transmembrane domains, the knowledge derived from this study would not only provide structural information about the protein, but also pave the way for future characterization of the unique functions of RKTG in the regulation of cell signalling.     

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.     

Sequence-based prediction of protein domains

Guessing the boundaries of structural domains has been an important and challenging problem in experimental and computational structural biology. Predictions were based on intuition, biochemical properties, statistics, sequence homology and other aspects of predicted protein structure. Here, we introduced CHOPnet, a de novo method that predicts structural domains in the absence of homology to known domains. Our method was based on neural networks and relied exclusively on information available for all proteins. Evaluating sustained performance through rigorous cross-validation on proteins of known structure, we correctly predicted the number of domains in 69% of all proteins. For 50% of the two-domain proteins the centre of the predicted boundary was closer than 20 residues to the boundary assigned from three-dimensional (3D) structures; this was about eight percentage points better than predictions by ‘equal split’. Our results appeared to compare favourably with those from previously published methods. CHOPnet may be useful to restrict the experimental testing of different fragments for structure determination in the context of structural genomics.     

Identification of transmembrane protein functions by binary topology patterns

We propose a novel method for identifying and classifying the functions of transmembrane (TM) proteins based on their TM topology [the number of TM segments (tms), the loop length and the N-terminus location]. In this method, the TM topology is expressed as a string of '0' and '1', and this is designated the binary topology pattern (BTP). We focused on TM proteins with up to 12 tms, with the exception of 1 and 9 tms, and classified them into 37 functional groups by the number of tms and the functional annotation. These grouped TM protein sequences were used to determine BTPs which are specific to the individual functional groups. Since the evaluated accuracies (sensitivity, specificity and self-consistency) of these patterns in functional identification were quite high overall, i.e. 0.940, 0.934 and 0.935, respectively, as averaged over the 37 functional groups, we confirmed that TM protein function can be identified by the number of tms and the characteristics of loop lengths, i.e. BTPs.