Protein Topology Prediction Research Articles

New insights into the pathogenicity of TMEM165 variants using structural modeling based on AlphaFold 2 predictions

TMEM165 is a Golgi protein playing a crucial role in Mn2+ transport, and whose mutations in patients are known to cause Congenital Disorders of Glycosylation. Some of those mutations affect the highly-conserved consensus motifs E-φ-G-D-[KR]-[TS] characterizing the CaCA2/UPF0016 family, presumably important for the transport of Mn2+ which is essential for the function of many Golgi glycosylation enzymes. Others, like the G>R304 mutation, are far away from these motifs in the sequence. Until recently, the classical membrane protein topology prediction methods were unable to provide a clear picture of the organization of TMEM165 inside the cell membrane, or to explain in a convincing manner the impact of patient and experimentally-generated mutations on the transporter function of TMEM165. In this study, AlphaFold 2 was used to build a TMEM165 model that was then refined by molecular dynamics simulation with membrane lipids and water. This model provides a realistic picture of the 3D protein scaffold formed from a two-fold repeat of three transmembrane helices/domains where the consensus motifs face each other to form a putative acidic cation-binding site at the cytosolic side of the protein. It sheds new light on the impact of mutations on the transporter function of TMEM165, found in patients and studied experimentally in vitro, formerly and within this study. More particularly and very interestingly, this model explains the impact of the G>R304 mutation on TMEM165’s function. These findings provide great confidence in the predicted TMEM165 model whose structural features are discussed in the study and compared to other structural and functional TMEM165 homologs from the CaCA2/UPF0016 family and the LysE superfamily.

Hidden neural networks for transmembrane protein topology prediction

Hidden Markov Models (HMMs) are amongst the most successful methods for predicting protein features in biological sequence analysis. However, there are biological problems where the Markovian assumption is not sufficient since the sequence context can provide useful information for prediction purposes. Several extensions of HMMs have appeared in the literature in order to overcome their limitations. We apply here a hybrid method that combines HMMs and Neural Networks (NNs), termed Hidden Neural Networks (HNNs), for biological sequence analysis in a straightforward manner. In this framework, the traditional HMM probability parameters are replaced by NN outputs. As a case study, we focus on the topology prediction of for alpha-helical and beta-barrel membrane proteins. The HNNs show performance gains compared to standard HMMs and the respective predictors outperform the top-scoring methods in the field. The implementation of HNNs can be found in the package JUCHMME, downloadable from http://www.compgen.org/tools/juchmme, https://github.com/pbagos/juchmme. The updated PRED-TMBB2 and HMM-TM prediction servers can be accessed at www.compgen.org.

PURIFICATION, CHARACTERIZATION AND EXPRESSION, OF RECOMBINANT OUTER MEMBRANE PROTEIN A (OMP A) OF ACINETOBACTER BAUMANNII LI311

Acinetobacter baumannii is a known hospital aquired pathogenic bacterium that increasingly resists antibiotics treatment. In order to characterize and produce a soluble OmpA protein that can be used to develop Acinetobacter vaccine, polymerase chain reaction (PCR) was used to produce the ompA gene, of A. baumannii strain LI311, which was cloned into the histidin taged pET19b expression plasmid. Immobilized metal affinity chromatography (IMAC) was utilized to purify the recombinant protein, and amino acid sequences for OmpA protein homologs were attained from the National Center for Biotechnology Information (NCBI) protein resource then analyzed using the blast tool and Jalview program. Protein topology prediction was done using NCBI tools and PRED-TMBB2. Analysis of amino acid sequence of OmpA of A. baumannii strain LI311 showed that it has homologies to other clinical Acinetobacter spices , including: A. pittii , A.nosocomialis , A.seifertii, A. calcoaceticus, and A. ursingii with identity percentages of 100%, 100%, 96%, 92%, and 91%. Protein topology prediction revealed two conserved domains belonging to OmpA family protein ,which are beta-barrel domain outer membrane protein (OMP_b-brl) and OmpA-C-like domain, and it is a 10-βeta -stranded transmembrane Outer Membrane Protein with a signal peptide at residues 1–22A. A recombinant Histidine tagged- OmpA (39.31kDa )was successfully expressed and purified in this study. In conclusion, OmpA protein of A.baumannii strain LI31 is highly conserved across clinical species of Acinetobacter, and the soluble recombinant OmpA created in this study can be used to develop a putative vaccine that may prevent infections caused by the clinical species of Acinetobacter.

The membrane topology of immunity proteins for the two-peptide bacteriocins carnobacteriocin XY, lactococcin G, and lactococcin MN shows structural diversity.

The two‐peptide bacteriocins produced by Gram‐positive bacteria require two different peptides, present in equimolar amounts, to elicit optimal antimicrobial activity. Producer organisms are protected from their bacteriocin by a dedicated immunity protein. The immunity proteins for two‐peptide bacteriocins contain putative transmembrane domains (TMDs) and might therefore be associated with the membrane. The immunity protein CbnZ for the two‐peptide bacteriocin carnobacteriocin XY (CbnXY) was identified by heterologously expressing the cbnZ gene in sensitive host strains. Using protein topology prediction methods and the dual pho‐lac reporter system, we mapped out the membrane topology of CbnZ, along with those of the immunity proteins LagC and LciM for the two‐peptide bacteriocins lactococcin G and lactococcin MN, respectively. Our results reveal wide structural variety between these immunity proteins that can contain as little as one TMD or as many as four TMDs.

Protein Topology Prediction Algorithms Systematically Investigated in the Yeast Saccharomyces cerevisiae.

Membrane proteins perform a variety of functions, all crucially dependent on their orientation in the membrane. However, neither the exact number of transmembrane domains (TMDs) nor the topology of most proteins have been experimentally determined. Due to this, most scientists rely primarily on prediction algorithms to determine topology and TMD assignments. Since these can give contradictory results, single-algorithm-based predictions are unreliable. To map the extent of potential misanalysis, the predictions of nine algorithms on the yeast proteome are compared and it is found that they have little agreement when predicting TMD number and termini orientation. To view all predictions in parallel, a webpage called TopologYeast: http://www.weizmann.ac.il/molgen/TopologYeast was created. Each algorithm is compared with experimental data and a poor agreement is found. The analysis suggests that more systematic data on protein topology are required to increase the training sets for prediction algorithms and to have accurate knowledge of membrane protein topology.

Benzoate transport in Pseudomonas putida CSV86.

Pseudomonas putida strain CSV86 metabolizes variety of aromatic compounds as the sole carbon source. Genome analysis revealed the presence of genes encoding putative transporters for benzoate, p-hydroxybenzoate, phenylacetate, p-hydroxyphenylacetate and vanillate. Bioinformatic analysis revealed that benzoate transport and metabolism genes are clustered at the ben locus as benK-catA-benE-benF. Protein topology prediction suggests that BenK (aromatic acid-H+ symporter of major facilitator superfamily) has 12 transmembrane α-helices with the conserved motif LADRXGRKX in loop 2, while BenE (benzoate-H+ symporter protein) has 11 predicted transmembrane α-helices. benF and catA encode benzoate specific porin, OprD and catechol 1,2-dioxygenase, respectively. Biochemical studies suggest that benzoate was transported by an inducible and active process. Inhibition (90%-100%) in the presence of dinitrophenol suggests that the energy for the transport process is derived from the proton motive force. The maximum rate of benzoate transport was 484 pmole min-1 mg-1 cells with an affinity constant, Kmof 4.5 μM. Transcriptional analysis of the benzoate and glucose-grown cells showed inducible expression of benF, benK and benE, suggesting that besides outer membrane porin, both inner membrane transporters probably contribute for the benzoate transport in P. putida strain CSV86.

Determining the N-terminal orientations of recombinant transmembrane proteins in the Escherichia coli plasma membrane.

In silico algorithms have been the common approach for transmembrane (TM) protein topology prediction. However, computational tools may produce questionable results and experimental validation has proven difficult. Although biochemical strategies are available to determine the C-terminal orientation of TM proteins, experimental strategies to determine the N-terminal orientation are still limited but needed because the N-terminal end is essential for membrane targeting. Here, we describe a new and easy method to effectively determine the N-terminal orientation of the target TM proteins in Escherichia coli plasma membrane environment. D94N, the mutant of bacteriorhodopsin from Haloarcula marismortui, can be a fusion partner to increase the production of the target TM proteins if their N-termini are in cytoplasm (Nin orientation). To create a suitable linker for orientating the target TM proteins with the periplasmic N-termini (Nout orientation) correctly, we designed a three-TM-helix linker fused at the C-terminus of D94N fusion partner (termed D94N-3TM) and found that D94N-3TM can specifically improve the production of the Nout target TM proteins. In conclusion, D94N and D94N-3TM fusion partners can be applied to determine the N-terminal end of the target TM proteins oriented either Nin or Nout by evaluating the net expression of the fusion proteins.

The human transmembrane proteome.

BackgroundTransmembrane proteins have important roles in cells, as they are involved in energy production, signal transduction, cell-cell interaction, cell-cell communication and more. In human cells, they are frequently targets for pharmaceuticals; therefore, knowledge about their properties and structure is crucial. Topology of transmembrane proteins provide a low resolution structural information, which can be a starting point for either laboratory experiments or modelling their 3D structures.ResultsHere, we present a database of the human α-helical transmembrane proteome, including the predicted and/or experimentally established topology of each transmembrane protein, together with the reliability of the prediction. In order to distinguish transmembrane proteins in the proteome as well as for topology prediction, we used a newly developed consensus method (CCTOP) that incorporates recent state of the art methods, with tested accuracies on a novel human benchmark protein set. CCTOP utilizes all available structure and topology data as well as bioinformatical evidences for topology prediction in a probabilistic framework provided by the hidden Markov model. This method shows the highest accuracy (98.5 % for discrinimating between transmembrane and non-transmembrane proteins and 84 % for per protein topology prediction) among the dozen tested topology prediction methods. Analysis of the human proteome with the CCTOP indicates that it contains 4998 (26 %) transmembrane proteins. Besides predicting topology, reliability of the predictions is estimated as well, and it is demonstrated that the per protein prediction accuracies of more than 60 % of the predictions are over 98 % on the benchmark sets and most probably on the predicted human transmembrane proteome too.ConclusionsHere, we present the most accurate prediction of the human transmembrane proteome together with the experimental topology data. These data, as well as various statistics about the human transmembrane proteins and their topologies can be downloaded from and can be visualized at the website of the human transmembrane proteome (http://htp.enzim.hu).ReviewersThis article was reviewed by Dr. Sandor Pongor, Dr. Michael Galperin and Dr. Pascale Gaudet (nominated by Dr Michael Galperin).Electronic supplementary materialThe online version of this article (doi:10.1186/s13062-015-0061-x) contains supplementary material, which is available to authorized users.

CCTOP: a Consensus Constrained TOPology prediction web server.

The Consensus Constrained TOPology prediction (CCTOP; http://cctop.enzim.ttk.mta.hu) server is a web-based application providing transmembrane topology prediction. In addition to utilizing 10 different state-of-the-art topology prediction methods, the CCTOP server incorporates topology information from existing experimental and computational sources available in the PDBTM, TOPDB and TOPDOM databases using the probabilistic framework of hidden Markov model. The server provides the option to precede the topology prediction with signal peptide prediction and transmembrane-globular protein discrimination. The initial result can be recalculated by (de)selecting any of the prediction methods or mapped experiments or by adding user specified constraints. CCTOP showed superior performance to existing approaches. The reliability of each prediction is also calculated, which correlates with the accuracy of the per protein topology prediction. The prediction results and the collected experimental information are visualized on the CCTOP home page and can be downloaded in XML format. Programmable access of the CCTOP server is also available, and an example of client-side script is provided.

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.

A guideline to proteome‐wide α‐helical membrane protein topology predictions

For current state-of-the-art methods, the prediction of correct topology of membrane proteins has been reported to be above 80%. However, this performance has only been observed in small and possibly biased data sets obtained from protein structures or biochemical assays. Here, we test a number of topology predictors on an "unseen" set of proteins of known structure and also on four "genome-scale" data sets, including one recent large set of experimentally validated human membrane proteins with glycosylated sites. The set of glycosylated proteins is also used to examine the ability of prediction methods to separate membrane from nonmembrane proteins. The results show that methods utilizing multiple sequence alignments are overall superior to methods that do not. The best performance is obtained by TOPCONS, a consensus method that combines several of the other prediction methods. The best methods to distinguish membrane from nonmembrane proteins belong to the "Phobius" group of predictors. We further observe that the reported high accuracies in the smaller benchmark sets are not quite maintained in larger scale benchmarks. Instead, we estimate the performance of the best prediction methods for eukaryotic membrane proteins to be between 60% and 70%. The low agreement between predictions from different methods questions earlier estimates about the global properties of the membrane proteome. Finally, we suggest a pipeline to estimate these properties using a combination of the best predictors that could be applied in large-scale proteomics studies of membrane proteins.

Functional characterization and expression analysis of novel alternative splicing isoforms of Olr1 gene during mouse embryogenesis

LOX-1 (Lectin-like oxidized low-density lipoprotein receptor-1) is the primary endothelial receptor of oxidized LDL (oxLDL). Both in vitro and in vivo experiments have shown this protein to be important in the initiation of atherosclerosis and to be up-regulated by pro-atherogenic factors. Recently, it has been demonstrated that Olr1, the gene encoding Lox-1, is important for tumor growth and for maintaining the transformed state in different cancer cell lines, suggesting that it acts in a molecular pathway connecting cancer and atherosclerosis. Both diseases in humans are characterized by uncontrolled regulation of cellular growth and differentiation. We present evidence that Olr1 is expressed during mouse embryogenesis in developmental stages (from 7.5 to 9.5 dpc) in which cardiogenesis occurs. In addition, we identify two novel Olr1 isoform (hereafter referred to as D3D5Olr1 and D2D5Olr1) whose spatio-temporal expression pattern overlaps with Olr1 in vivo. In vitro, D3D5Olr1 localizes to the cell surface membrane as Olr1, in contrast with D2D5Olr1; these data suggest that D2D5Olr1 isoform translates a receptor that does not reach the plasma membrane. Accordingly, in silico transmembrane protein topology prediction analyses, show that D2D5Olr1 does not contain any transmembrane region. Finally, both isoforms can activate the same genetic pathways underlying Olr1 expression, such as, hypoxia and inflammation, even if with a different efficiency. All these data suggest a new functional involvement of Olr1, and probably of its spliceforms, in murine cardiogenesis and angiogenesis.

Rapid membrane protein topology prediction

Summary: 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.Contact: arne@bioinfo.seSupplementary information: Supplementary data are avaliable at Bioinformatics online.

Topology Prediction of Helical Transmembrane Proteins: How Far Have We Reached?

Transmembrane protein topology prediction methods play important roles in structural biology, because the structure determination of these types of proteins is extremely difficult by the common biophysical, biochemical and molecular biological methods. The need for accurate prediction methods is high, as the number of known membrane protein structures fall far behind the estimated number of these proteins in various genomes. The accuracy of these prediction methods appears to be higher than most prediction methods applied on globular proteins, however it decreases slightly with the increasing number of structures. Unfortunately, most prediction algorithms use common machine learning techniques, and they do not reveal why topologies are predicted with such a high success rate and which biophysical or biochemical properties are important to achieve this level of accuracy. Incorporating topology data determined so far into the prediction methods as constraints helps us to reach even higher prediction accuracy, therefore collection of such topology data is also an important issue.

Mycobacterium tuberculosis MycP1 Protease Plays a Dual Role in Regulation of ESX-1 Secretion and Virulence

Mycobacterium tuberculosis uses the ESX-1 secretion system to deliver virulence proteins during infection of host cells. Here we report a mechanism of posttranscriptional control of ESX-1 mediated by MycP1, a M. tuberculosis serine protease. We show that MycP1 is required for ESX-1 secretion but that, unexpectedly, genetic inactivation of MycP1 protease activity increases secretion of ESX-1 substrates. We demonstrate that EspB, an ESX-1 substrate required for secretion, is a target of MycP1 in vitro and in vivo. During macrophage infection, an inactive MycP1 protease mutant causes hyperactivation of ESX-1-stimulated innate signaling pathways. MycP1 is required for growth in mice during acute infection, while loss of its protease activity leads to attenuated virulence during chronic infection. As the key ESX-1 substrates ESAT-6 and CFP-10 are highly immunogenic, fine-tuning of their secretion by MycP1 may balance virulence and immune detection and be essential for successful maintenance of long-term M. tuberculosis infection.

Prediction of the human membrane proteome

Membrane proteins are key molecules in the cell, and are important targets for pharmaceutical drugs. Few three-dimensional structures of membrane proteins have been obtained, which makes computational prediction of membrane proteins crucial for studies of these key molecules. Here, seven membrane protein topology prediction methods based on different underlying algorithms, such as hidden Markov models, neural networks and support vector machines, have been used for analysis of the protein sequences from the 21,416 annotated genes in the human genome. The number of genes coding for a protein with predicted alpha-helical transmembrane region(s) ranged from 5508 to 7651, depending on the method used. Based on a majority decision method, we estimate 5539 human genes to code for membrane proteins, corresponding to approximately 26% of the human protein-coding genes. The largest fraction of these proteins has only one predicted transmembrane region, but there are also many proteins with seven predicted transmembrane regions, including the G-protein coupled receptors. A visualization tool displaying the topologies suggested by the eight prediction methods, for all predicted membrane proteins, is available on the public Human Protein Atlas portal (www.proteinatlas.org).

MetaTM - a consensus method for transmembrane protein topology prediction

BackgroundTransmembrane (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.ResultsA novel TM consensus method, named MetaTM, is proposed in this work. MetaTM is based on support vector machine models and combines the results of six TM topology predictors and two signal peptide predictors. On a large data set comprising 1460 sequences of TM proteins with known topologies and 2362 globular protein sequences it correctly predicts 86.7% of all topologies.ConclusionCombining several TM predictors in a consensus prediction framework improves overall accuracy compared to any of the individual methods. Our proposed SVM-based system also has higher accuracy than a previous consensus predictor. MetaTM is made available both as downloadable source code and as DAS server at

Transmembrane protein topology prediction using support vector machines

BackgroundAlpha-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.ResultsWe present a support vector machine-based (SVM) TM protein topology predictor that integrates both signal peptide and re-entrant helix prediction, benchmarked with full cross-validation on a novel data set of 131 sequences with known crystal structures. The method achieves topology prediction accuracy of 89%, while signal peptides and re-entrant helices are predicted with 93% and 44% accuracy respectively. An additional SVM trained to discriminate between globular and TM proteins detected zero false positives, with a low false negative rate of 0.4%. We present the results of applying these tools to a number of complete genomes. Source code, data sets and a web server are freely available from .ConclusionThe high accuracy of TM topology prediction which includes detection of both signal peptides and re-entrant helices, combined with the ability to effectively discriminate between TM and globular proteins, make this method ideally suited to whole genome annotation of alpha-helical transmembrane proteins.

Transmembrane Topology and Signal Peptide Prediction Using Dynamic Bayesian Networks

Hidden Markov models (HMMs) have been successfully applied to the tasks of transmembrane protein topology prediction and signal peptide prediction. In this paper we expand upon this work by making use of the more powerful class of dynamic Bayesian networks (DBNs). Our model, Philius, is inspired by a previously published HMM, Phobius, and combines a signal peptide submodel with a transmembrane submodel. We introduce a two-stage DBN decoder that combines the power of posterior decoding with the grammar constraints of Viterbi-style decoding. Philius also provides protein type, segment, and topology confidence metrics to aid in the interpretation of the predictions. We report a relative improvement of 13% over Phobius in full-topology prediction accuracy on transmembrane proteins, and a sensitivity and specificity of 0.96 in detecting signal peptides. We also show that our confidence metrics correlate well with the observed precision. In addition, we have made predictions on all 6.3 million proteins in the Yeast Resource Center (YRC) database. This large-scale study provides an overall picture of the relative numbers of proteins that include a signal-peptide and/or one or more transmembrane segments as well as a valuable resource for the scientific community. All DBNs are implemented using the Graphical Models Toolkit. Source code for the models described here is available at http://noble.gs.washington.edu/proj/philius. A Philius Web server is available at http://www.yeastrc.org/philius, and the predictions on the YRC database are available at http://www.yeastrc.org/pdr.

HMM_RA: an improved method for alpha-helical transmembrane protein topology prediction.

alpha-helical transmembrane (TM) proteins play important and diverse functional roles in cells. The ability to predict the topology of these proteins is important for identifying functional sites and inferring function of membrane proteins. This paper presents a Hidden Markov Model (referred to as HMM_RA) that can predict the topology of alpha-helical transmembrane proteins with improved performance. HMM_RA adopts the same structure as the HMMTOP method, which has five modules: inside loop, inside helix tail, membrane helix, outside helix tail and outside loop. Each module consists of one or multiple states. HMM_RA allows using reduced alphabets to encode protein sequences. Thus, each state of HMM_RA is associated with n emission probabilities, where n is the size of the reduced alphabet set. Direct comparisons using two standard data sets show that HMM_RA consistently outperforms HMMTOP and TMHMM in topology prediction. Specifically, on a high-quality data set of 83 proteins, HMM_RA outperforms HMMTOP by up to 7.6% in topology accuracy and 6.4% in alpha-helices location accuracy. On the same data set, HMM_RA outperforms TMHMM by up to 6.4% in topology accuracy and 2.9% in location accuracy. Comparison also shows that HMM_RA achieves comparable performance as Phobius, a recently published method.