Transmembrane Protein Topology Research Articles

Unraveling the Membrane Topology of TMEM151A: A Step Towards Understanding its Cellular Role

Transmembrane protein 151A (TMEM151A) has been identified as a causative gene for paroxysmal kinesigenic dyskinesia, though its molecular function remains almost completely unknown. Understanding the membrane topology of transmembrane proteins is crucial for elucidating their functions and possible interacting partners. In this study, we utilized molecular dynamics simulations, immunocytochemistry, and electron microscopy to define the topology of TMEM151A. Our results validate a starting AlphaFold model of TMEM151A and reveal that it comprises a transmembrane domain with two membrane-spanning alpha helices connected by a short extracellular loop and an intramembrane helix-hinge-helix structure. Notably, most of the protein is oriented towards the intracellular side of the membranes with a large cytosolic domain featuring a combination of alpha-helix and beta-sheet structures, as well as the protein N- and C-termini. These insights into TMEM151A’s topology and orientation of its domains with respect of the cell membranes provide essential information for future functional studies and represent a first fundamental step for understanding its role in the pathogenesis of paroxysmal kinesigenic dyskinesia.

Spf1 and Ste24: quality controllers of transmembrane protein topology in the eukaryotic cell.

DNA replication, transcription, and translation in eukaryotic cells occur with decreasing but still high fidelity. In contrast, for the estimated 33% of the human proteome that is inserted as transmembrane (TM) proteins, insertion with a non-functional inverted topology is frequent. Correct topology is essential for function and trafficking to appropriate cellular compartments and is controlled principally by responses to charged residues within 15 residues of the inserted TM domain (TMD); the flank with the higher positive charge remains in the cytosol (inside), following the positive inside rule (PIR). Yeast (Saccharomyces cerevisiae) mutants that increase insertion contrary to the PIR were selected. Mutants with strong phenotypes were found only in SPF1 and STE24 (human cell orthologs are ATP13A1 and ZMPSte24) with, at the time, no known relevant functions. Spf1/Atp13A1 is now known to dislocate to the cytosol TM proteins inserted contrary to the PIR, allowing energy-conserving reinsertion. We hypothesize that Spf1 and Ste24 both recognize the short, positively charged ER luminal peptides of TM proteins inserted contrary to the PIR, accepting these peptides into their large membrane-spanning, water-filled cavities through interaction with their many interior surface negative charges. While entry was demonstrated for Spf1, no published evidence directly demonstrates substrate entry to the Ste24 cavity, internal access to its zinc metalloprotease (ZMP) site, or active withdrawal of fragments, which may be essential for function. Spf1 and Ste24 comprise a PIR quality control system that is conserved in all eukaryotes and presumably evolved in prokaryotic progenitors as they gained differentiated membrane functions. About 75% of the PIR is imposed by this quality control system, which joins the UPR, ERAD, and autophagy (ER-phagy) in coordinated, overlapping quality control of ER protein function.

Secondary and Topological Structural Merge Prediction of Alpha-Helical Transmembrane Proteins Using a Hybrid Model Based on Hidden Markov and Long Short-Term Memory Neural Networks.

Alpha-helical transmembrane proteins (αTMPs) play essential roles in drug targeting and disease treatments. Due to the challenges of using experimental methods to determine their structure, αTMPs have far fewer known structures than soluble proteins. The topology of transmembrane proteins (TMPs) can determine the spatial conformation relative to the membrane, while the secondary structure helps to identify their functional domain. They are highly correlated on αTMPs sequences, and achieving a merge prediction is instructive for further understanding the structure and function of αTMPs. In this study, we implemented a hybrid model combining Deep Learning Neural Networks (DNNs) with a Class Hidden Markov Model (CHMM), namely HDNNtopss. DNNs extract rich contextual features through stacked attention-enhanced Bidirectional Long Short-Term Memory (BiLSTM) networks and Convolutional Neural Networks (CNNs), and CHMM captures state-associative temporal features. The hybrid model not only reasonably considers the probability of the state path but also has a fitting and feature-extraction capability for deep learning, which enables flexible prediction and makes the resulting sequence more biologically meaningful. It outperforms current advanced merge-prediction methods with a Q4 of 0.779 and an MCC of 0.673 on the independent test dataset, which have practical, solid significance. In comparison to advanced prediction methods for topological and secondary structures, it achieves the highest topology prediction with a Q2 of 0.884, which has a strong comprehensive performance. At the same time, we implemented a joint training method, Co-HDNNtopss, and achieved a good performance to provide an important reference for similar hybrid-model training.

Genome Sequence Resource of Ustilago crameri, a Fungal Pathogen Causing Millet Smut Disease of Foxtail Millet.

Genome Sequence Resource of Ustilago crameri, a Fungal Pathogen Causing Millet Smut Disease of Foxtail Millet.

An Improved Genome Sequence Resource of Bipolaris maydis, Causal Agent of Southern Corn Leaf Blight.

An Improved Genome Sequence Resource of Bipolaris maydis, Causal Agent of Southern Corn Leaf Blight.

Genome Resource of Colletotrichum spaethianum, the Causal Agent of Leaf Anthracnose in Polygonatum falcatum

Genome Resource of <i>Colletotrichum spaethianum</i>, the Causal Agent of Leaf Anthracnose in <i>Polygonatum falcatum</i>

Phylogenetic assessment and in silico characterization of cytochrome b protein of three alpheid shrimps

Cytochrome b (cyt b) is one of the cytochrome proteins involved in electron transport in the respiratory chain of mitochondria. Cyt b is the only gene among the cytochrome complex coded by mitochondrial DNA. It is the most widely used gene for phylogenetic assessment and inter species variation studies. Here, the amino acid sequence of cyt b in three snapping shrimps such as, Alpheus lobidens, A. randali, A. bellulus was analysed and the results showed higher similarity in A. lobidens and A. randali as reflected in the phylogenetic tree. This study describes the applications of bioinformatics tools to predict the physico-chemical characters of cyt b protein. This protein was composed of least percentage of Cys (0.8%) and highest percentage of Leu (13.8%). The maximum molecular weight (MW) was predicted as 42.62 KDa in A. randali. The theoretical pI value was ranged from 8.35 to 8.36 and confirmed that cyt b was alkali in nature. The instability index value was in the range of 42.29 to 46.94 which showed the protein was more stable. The secondary structure of this protein was primarily composed of α-helixes and random coil, revealing the stable structure. The comparative modelling was performed by Swiss model where the 3-D crystal structure of bovine cyt bc1 (6haw1.c) was used as template. Ramachandran plot analysis showed that most of the amino acids (&gt;92%) falling on the favoured region. Seven conserved motifs were identified by MEME analysis. The modelled 3-D structure of this protein was validated by PROCHECK and QMEAN. The transmembrane protein topology and helix probability curve was predicted by TMHMM server. Protein-protein interactions was analysed by STRING tool and found the network of cyt b with related proteins. The results of this study may provide valuable insights into fundamental characteristics of cyt b in Alpheid shrimps.

Identification of TRAMs as sphingolipid-binding proteins using a photoactivatable and clickable short-chain ceramide analog

Ceramide is a lipid molecule that regulates diverse physiological and pathological reactions in part through inverting the topology of certain transmembrane proteins. This topological inversion is achieved through regulated alternative translocation (RAT), which reverses the direction by which membrane proteins are translocated across the endoplasmic reticulum during translation. However, owing to technical challenges in studying protein–ceramide interaction, it remains unclear how ceramide levels are sensed in cells to trigger RAT. Here, we report the synthesis of pac-C7-Cer, a photoactivatable and clickable short-chain ceramide analog that can be used as a probe to study protein–ceramide interactions. We demonstrate that translocating chain-associated membrane protein 2 (TRAM2), a protein known to control RAT of transmembrane 4 L6 subfamily member 20, and TRAM1, a homolog of TRAM2, interacted with molecules derived from pac-C7-Cer. This interaction was competed by naturally existing long-chain ceramide molecules. We showed that binding of ceramide and its analogs to TRAM2 correlated with their ability to induce RAT of transmembrane 4 L6 subfamily member 20. In addition to probing ceramide–TRAM interactions, we provide evidence that pac-C7-cer could be used for proteome-wide identification of ceramide-binding proteins. Our study provides mechanistic insights into RAT by identifying TRAMs as potential ceramide-binding proteins and establishes pac-C7-Cer as a valuable tool for future study of ceramide–protein interactions.

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.

Translating Co-Translational Translocation

Understanding the process of proteins targeting to specific locations within a cell is a fundamental part of cell biology. Synthesis of proteins by co-translational translocation, which also determines the resulting protein topology, is a complex process that is often difficult for students to understand. Our goal was to create an engaging lesson to stimulate critical and experimental thinking and help students more effectively learn about co-translational translocation. In the first part of the lesson, students complete a strip sequence activity to order the basic events of co-translational translocation. This activity stimulates peer and class discussion that helps students engage with the material and generate and answer questions. In the second part of the lesson, students complete a four-part guided worksheet that introduces two versions of the protease protection assay with a secretory protein and uses the protease protection assay to evaluate the topology of transmembrane proteins with different types of ER signal sequences. Student responses to in-class clicker questions and exam questions assess student learning. Student survey results indicate the activity was engaging, not confusing, and was helpful for most students.

Significance of Cholesterol-Binding Motifs in ABCA1, ABCG1, and SR-B1 Structure.

ABCA1, ABCG1 transporters, and SR-B1 receptor are the major proteins involved in cholesterol efflux from cells. We superposed in silico the location of putative cholesterol (Chol)-binding motifs CRAC/CARC and CCM in human ABCA1, ABCG1, and SR-B1 with (1) transmembrane protein topology, (2) a profile of structural order of protein, and (3) with an influence of single amino acid substitutions on protein structure and function. ABCA1, ABCG1, and SR-B1 molecules contain 50, 19, and 13 Chol-binding motifs, respectively, that are localized either in membrane helices, or at membrane-water interface, or in water-exposed protein regions. Arginine residues in motifs that coincide with molecular recognition features within intrinsically disordered regions of the transporters are suggested to be important in cholesterol binding; cholesterol-arginine interaction may result in the induction of local order in protein structure. Chol-binding motifs in membrane helices may immobilize cholesterol, while motifs at membrane-water interface may be involved into the efflux of "active" cholesterol. Cholesterol may interfere with ATP binding in both nucleotide-binding domains of ABCA1 structure. For ABCA1 and ABCG1, but not for SR-B1, the presence of mirror code as a CARC-CRAC vector couple in the C-terminal helices controlling protein-cholesterol interactions in the outer and inner membrane leaflets was evidenced. We propose the role of Chol-binding motifs with different immersion in membrane in transport of different cholesterol pools by ABCA1 and ABCG1.

Semi-supervised learning of Hidden Markov Models for biological sequence analysis.

Hidden Markov Models (HMMs) are probabilistic models widely used in applications in computational sequence analysis. HMMs are basically unsupervised models. However, in the most important applications, they are trained in a supervised manner. Training examples accompanied by labels corresponding to different classes are given as input and the set of parameters that maximize the joint probability of sequences and labels is estimated. A main problem with this approach is that, in the majority of the cases, labels are hard to find and thus the amount of training data is limited. On the other hand, there are plenty of unclassified (unlabeled) sequences deposited in the public databases that could potentially contribute to the training procedure. This approach is called semi-supervised learning and could be very helpful in many applications. We propose here, a method for semi-supervised learning of HMMs that can incorporate labeled, unlabeled and partially labeled data in a straightforward manner. The algorithm is based on a variant of the Expectation-Maximization (EM) algorithm, where the missing labels of the unlabeled or partially labeled data are considered as the missing data. We apply the algorithm to several biological problems, namely, for the prediction of transmembrane protein topology for alpha-helical and beta-barrel membrane proteins and for the prediction of archaeal signal peptides. The results are very promising, since the algorithms presented here can significantly improve the prediction performance of even the top-scoring classifiers. Supplementary data are available at Bioinformatics online.

Protein Palmitoylation Plays an Important Role in Trichomonas vaginalis Adherence

The flagellated protozoan parasite Trichomonas vaginalis is the etiologic agent of trichomoniasis, the most common non-viral sexually transmitted infection worldwide. As an obligate extracellular pathogen, adherence to epithelial cells is critical for parasite survival within the human host and a better understanding of this process is a prerequisite for the development of therapies to combat infection. In this sense, recent work has shown S-acylation as a key modification that regulates pathogenesis in different protozoan parasites. However, there are no reports indicating whether this post-translational modification is a mechanism operating in T. vaginalis In order to study the extent and function of S-acylation in T. vaginalis biology, we undertook a proteomic study to profile the full scope of S-acylated proteins in this parasite and reported the identification of 363 proteins involved in a variety of biological processes such as protein transport, pathogenesis related and signaling, among others. Importantly, treatment of parasites with the palmitoylation inhibitor 2-bromopalmitate causes a significant decrease in parasite: parasite aggregation as well as adherence to host cells suggesting that palmitoylation could be modifying proteins that are key regulators of Trichomonas vaginalis pathogenesis.

Structural Analysis and Epitope Prediction of MHC Class-1-Chain Related Protein-A for Cancer Vaccine Development

Major histocompatibility complex class 1 chain-related gene sequence A is a polymorphic gene found at about 46.6 kb centromeric to HLA-B. It encodes a transmembrane protein, which is a non-classical human leukocyte antigen whose expression is normally induced by stress conditions like cancer and viral infections. The expression of MIC-A leads to the activation of NKG2D receptors of natural killer and T cells, leading to the generation of innate immune response that can easily eliminate/cleanse tumour cells and other cells that express the protein. Several bioinformatics and immunoinformatics tools were used to analyse the sequence and structure of the MIC-A protein. These tools were used in building and evaluating modelled structure of MIC-A, and to predict several antigenic determinant sites on the protein. The MIC-A protein structure generated an average antigenic propensity of 1.0289. Additionally, the hydrophilic regions on the surface of the MIC-A protein where antibodies can be attached were revealed. A total of fourteen antigenic epitopes were predicted, with six found in the transmembrane protein topology, and are predicted to play a role in the development of vaccines that can reactivate the functionalities of the MIC-A protein on the surface of cancer cells in order to elicit a desired immune response.

Predicting Beta Barrel Transmembrane Proteins Using HMMs.

Transmembrane beta-barrels (TMBBs) constitute an important structural class of membrane proteins located in the outer membrane of gram-negative bacteria, and in the outer membrane of chloroplasts and mitochondria. They are involved in a wide variety of cellular functions and the prediction of their transmembrane topology, as well as their discrimination in newly sequenced genomes is of great importance as they are promising targets for antimicrobial drugs and vaccines. Several methods have been applied for the prediction of the transmembrane segments and the topology of beta barrel transmembrane proteins utilizing different algorithmic techniques. Hidden Markov Models (HMMs) have been efficiently used in the development of several computational methods used for this task. In this chapter we give a brief review of different available prediction methods for beta barrel transmembrane proteins pointing out sequence and structural features that should be incorporated in a prediction method. We then describe the procedure of the design and development of a Hidden Markov Model capable of predicting the transmembrane beta strands of TMBBs and discriminating them from globular proteins.

Deep Conditional Random Field Approach to Transmembrane Topology Prediction and Application to GPCR Three-Dimensional Structure Modeling.

Transmembrane proteins play important roles incellular energy production, signal transmission, and metabolism. Many shallow machine learning methods have been applied to transmembrane topology prediction, but the performance was limited by the large size of membrane proteins and the complex biological evolution information behind the sequence. In this paper, we proposed a novel deep approach based on conditional random fields named as dCRF-TM for predicting the topology of transmembrane proteins. Conditional random fields take into account more complicated interrelation between residue labels in full-length sequence than HMM and SVM-based methods. Three widely-used datasets were employed in the benchmark. DCRF-TM had the accuracy 95 percent over helix location prediction and the accuracy 78 percent over helix number prediction. DCRF-TM demonstrated a more robust performance on large size proteins (>350 residues) against 11 state-of-the-art predictors. Further dCRF-TM was applied to ab initio modeling three-dimensional structures of seven-transmembrane receptors, also known as G protein-coupled receptors. The predictions on 24 solved G protein-coupled receptors and unsolved vasopressin V2 receptor illustrated that dCRF-TM helped abGPCR-I-TASSER to improve TM-score 34.3 percent rather than using the random transmembrane definition. Two out of five predicted models caught the experimental verified disulfide bonds in vasopressin V2 receptor.

Characterization of Disease-Associated Mutations in Human Transmembrane Proteins.

Transmembrane protein coding genes are commonly associated with human diseases. We characterized disease causing mutations and natural polymorphisms in transmembrane proteins by mapping missense genetic variations from the UniProt database on the transmembrane protein topology listed in the Human Transmembrane Proteome database. We found characteristic differences in the spectrum of amino acid changes within transmembrane regions: in the case of disease associated mutations the non-polar to non-polar and non-polar to charged amino acid changes are equally frequent. In contrast, in the case of natural polymorphisms non-polar to charged amino acid changes are rare while non-polar to non-polar changes are common. The majority of disease associated mutations result in glycine to arginine and leucine to proline substitutions. Mutations to positively charged amino acids are more common in the center of the lipid bilayer, where they cause more severe structural and functional anomalies. Our analysis contributes to the better understanding of the effect of disease associated mutations in transmembrane proteins, which can help prioritize genetic variations in personal genomic investigations.

An improved method to construct basic probability assignment based on the confusion matrix for classification problem

The determination of basic probability assignment (BPA) is a crucial issue in the application of Dempster–Shafer evidence theory. Classification is a process of determining the class label that a sample belongs to. In classification problem, the construction of BPA based on the confusion matrix has been studied. However, the existing methods do not make full use of the available information provided by the confusion matrix. In this paper, an improved method to construct the BPA is proposed based on the confusion matrix. The proposed method takes into account both the precision rate and the recall rate of each class. An illustrative case regarding the prediction of transmembrane protein topology is given to demonstrate the effectiveness of the proposed method.

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.