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.     

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.     

Proteomics-based consensus prediction of protein retention in a bacterial membrane

The availability of complete bacterial genome sequences allows proteome-wide predictions of exported proteins that are potentially retained in the cytoplasmic membranes of the corresponding organisms. In practice, however, major problems are encountered with the computer-assisted distinction between (Sec-type) signal peptides that direct exported proteins into the growth medium and lipoprotein signal peptides or amino-terminal membrane anchors that cause protein retention in the membrane. In the present studies, which were aimed at improving methods to predict protein retention in the bacterial cytoplasmic membrane, we have compared sets of membrane-attached and extracellular proteins of Bacillus subtilis that were recently identified through proteomics approaches. The results showed that three classes of membrane-attached proteins can be distinguished. Two classes include 43 lipoproteins and 48 proteins with an amino-terminal transmembrane segment, respectively. Remarkably, a third class includes 31 proteins that remain membrane-retained despite the presence of typical Sec-type signal peptides with consensus signal peptidase recognition sites. This unprecedented finding indicates that unknown mechanisms are involved in membrane retention of this class of proteins. A further novelty is a consensus sequence indicative for release of certain lipoproteins from the membrane by proteolytic shaving. Finally, using non-overlapping sets of secreted and membrane-retained proteins, the accuracy of different signal peptide prediction algorithms was assessed. Accuracy for the prediction of protein retention in the membrane was increased to 82% using a majority-vote approach. Our findings provide important leads for future identification of surface proteins from pathogenic bacteria, which are attractive candidate infection markers and potential targets for drugs or vaccines.     

Integrated prediction of the helical membrane protein interactome in yeast

At least a quarter of all genes in most genomes contain putative transmembrane (TM) helices, and helical membrane protein interactions are a major component of the overall cellular interactome. However, current experimental techniques for large-scale detection of protein-protein interactions are biased against membrane proteins. Here, we define protein-protein interaction broadly as co-complexation, and develop a weighted-voting procedure to predict interactions among yeast helical membrane proteins by optimally combining evidence based on diverse genome-wide information such as sequence, function, localization, abundance, regulation, and phenotype. We use logistic regression to simultaneously optimize the weights of all evidence sources for best discrimination based on a set of known helical membrane protein interactions. The resulting integrated classifier not only significantly outperforms classifiers based on any single genomic feature, but also does better than a benchmark Na?ve Bayes classifier (using a simplifying assumption of conditional independence among features). Finally, we apply the optimized classifier genome-wide, and construct a comprehensive map of predicted helical membrane protein interactome in yeast. This can serve as a guide for prioritizing further experimental validation efforts.     

BPROMPT: A consensus server for membrane protein prediction

Protein structure prediction is a cornerstone of bioinformatics research. Membrane proteins require their own prediction methods due to their intrinsically different composition. A variety of tools exist for topology prediction of membrane proteins, many of them available on the Internet. The server described in this paper, BPROMPT (Bayesian PRediction Of Membrane Protein Topology), uses a Bayesian Belief Network to combine the results of other prediction methods, providing a more accurate consensus prediction. Topology predictions with accuracies of 70% for prokaryotes and 53% for eukaryotes were achieved. BPROMPT can be accessed at http://www.jenner.ac.uk/BPROMPT.     

The prediction of membrane protein structure and genome structural annotation

New methods, essentially based on hidden Markov models (HMM) and neural networks (NN), can predict the topography of both beta-barrel and all-alpha membrane proteins with high accuracy and a low rate of false positives and false negatives. These methods have been integrated in a suite of programs to filter proteomes of Gram-negative bacteria, searching for new membrane proteins.     

Mem-PHybrid: hybrid features-based prediction system for classifying membrane protein types

Membrane proteins are a major class of proteins and encoded by approximately 20% to 30% of genes in most organisms. In this work, a two-layer novel membrane protein prediction system, called Mem-PHybrid, is proposed. It is able to first identify the protein query as a membrane or nonmembrane protein. In the second level, it further identifies the type of membrane protein. The proposed Mem-PHybrid prediction system is based on hybrid features, whereby a fusion of both the physicochemical and split amino acid composition-based features is performed. This enables the proposed Mem-PHybrid to exploit the discrimination capabilities of both types of feature extraction strategy. In addition, minimum redundancy and maximum relevance has also been applied to reduce the dimensionality of a feature vector. We employ random forest, evidence-theoretic K-nearest neighbor, and support vector machine (SVM) as classifiers and analyze their performance on two datasets. SVM using hybrid features yields the highest accuracy of 89.6% and 97.3% on dataset1 and 91.5% and 95.5% on dataset2 for jackknife and independent dataset tests, respectively. The enhanced prediction performance of Mem-PHybrid is largely attributed to the exploitation of the discrimination power of the hybrid features and of the learning capability of SVM. Mem-PHybrid is accessible at http://www.111.68.99.218/Mem-PHybrid.     

Optimal mutation sites for PRE data collection and membrane protein structure prediction

Nuclear magnetic resonance paramagnetic relaxation enhancement (PRE) measures long-range distances to isotopically labeled residues, providing useful constraints for protein structure prediction. The method usually requires labor-intensive conjugation of nitroxide labels to multiple locations on the protein, one at a time. Here a computational procedure, based on protein sequence and simple secondary structure models, is presented to facilitate optimal placement of a minimum number of labels needed to determine the correct topology of?a helical transmembrane protein. Tests on DsbB (four helices) using just one label lead to correct topology predictions in four of five cases, with the predicted structures <6 ? to the native structure. Benchmark results using simulated PRE data show that we can generally predict the correct topology for five and six to seven helices using two and three labels, respectively, with an average success rate of 76% and structures of similar precision. The results show promise in facilitating experimentally constrained structure prediction of membrane proteins.     

State-of-the-art in phosphoproteomics

Presently, phosphorylation of proteins is the most studied and best understood PTM. However, the analysis of phosphoproteins and phosphopeptides is still one of the most challenging tasks in contemporary proteome research. Since not every phosphoprotein is accessible by a certain method and identification of the phosphorylated amino acid residue is required in the majority of cases, various strategies for the detection and localization of phosphorylations have been developed. Identification and localization of protein phosphorylations is mostly done by MS nowadays but phosphoproteins and -peptides are often suppressed in comparison to the unphosphorylated species if measured in complex mixtures. Thus, the isolation of pure phosphopeptide samples is a main task. This review gives an overview over the most frequently used methods in isolation and detection of phosphoproteins and -peptides such as specific enrichment or separation strategies as well as the localization of the phosphorylated residues by various mass spectrometric techniques.     

Structure prediction of membrane proteins

There is a large gap between the number of membrane protein (MP) sequences and that of their decoded 3D structures, especially high-resolution structures, due to difficulties in crystal preparation of MPs. However, detailed knowledge of the 3D structure is required for the fundamental understanding of the function of an MP and the interactions between the protein and its inhibitors or activators. In this paper, some computational approaches that have been used to predict MP structures are discussed and compared.     

Topology prediction of membrane proteins.

A new method is described for prediction of protein membrane topology (intra- and extracellular sidedness) from multiply aligned amino acid sequences after determination of the membrane-spanning segments. The prediction technique relies on residue compositional differences in the protein segments exposed at each side of the membrane. Intra/extracellular ratios are calculated for the residue types Asn, Asp, Gly, Phe, Pro, Trp, Tyr, and Val, preferably found on the extracellular side, and for Ala, Arg, Cys, and Lys, mostly occurring on the intracellular side. The consensus over these 12 residue distributions is used for sidedness prediction. The method was developed with a test set of 42 protein families, for which all but one were correctly predicted with the new algorithm. This represents an improvement over predictions based on the widely used "positive-inside rule" and other techniques, where at least six mispredictions were observed for the same data set. Further, application of this and other methods to 12 protein families not in the test set still showed the better performance of the present technique, which was subsequently applied to another set of membrane protein families where the topology has yet to be determined.     

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.     

The prediction of hydrophobicity gradients within membrane interactive protein alpha-helices using a novel graphical technique

Here, it is shown that amphiphilicity profiling based on the mean hydrophobic moment provides a simple visual guide for the identification of oblique orientated alpha-helices. The methodology has an efficiency of circa 70% and predicts that approximately 40% of transmembrane alpha-helices may possess these structures.     

Membrane protein prediction methods

We survey computational approaches that tackle membrane protein structure and function prediction. While describing the main ideas that have led to the development of the most relevant and novel methods, we also discuss pitfalls, provide practical hints and highlight the challenges that remain. The methods covered include: sequence alignment, motif search, functional residue identification, transmembrane segment and protein topology predictions, homology and ab initio modeling. In general, predictions of functional and structural features of membrane proteins are improving, although progress is hampered by the limited amount of high-resolution experimental information available. While predictions of transmembrane segments and protein topology rank among the most accurate methods in computational biology, more attention and effort will be required in the future to ameliorate database search, homology and ab initio modeling.     

Future directions in protein function prediction

New directions in computational methods for the prediction of protein function are discussed. THEMATICS, a method for the location and characterization of the active sites of enzymes, is featured. THEMATICS, for Theoretical Microscopic Titration Curves, is based on well-established finite-difference Poisson-Boltzmann methods for computing the electric field function of a protein. THEMATICS requires only the structure of the subject protein and thus may be applied to proteins that bear no similarity in structure or sequence to any previously characterized protein. The unique features of catalytic sites in proteins are discussed. Discussion of the chemical basis for the predictive powers of THEMATICS is featured in this paper. Some results are given for three illustrative examples: HIV-1 protease, human apurinic/apyrimidinic endonuclease, and human adenosine kinase.     

Uncertainty analysis in protein disorder prediction

A grand challenge in the proteomics and structural genomics era is the prediction of protein structure, including identification of those proteins that are partially or wholly unstructured. A number of predictors for identification of intrinsically disordered proteins (IDPs) have been developed over the last decade, but none can be taken as a fully reliable on its own. Using a single model for prediction is typically inadequate because prediction based on only the most accurate model ignores model uncertainty. In this paper, we present an empirical method to specify and measure uncertainty associated with disorder predictions. In particular, we analyze the uncertainty in the reference model itself and the uncertainty in data. This is achieved by training a set of models and developing several meta predictors on top of them. The best meta predictor achieved comparable or better results than any other single model, suggesting that incorporating different aspects of protein disorder prediction is important for the disorder prediction task. In addition, the best meta-predictor had more balanced sensitivity and specificity than any individual model. We also assessed the effects of changes in disorder prediction as a function of changes in the protein sequence. For collections of homologous sequences, we found that mutations caused many of the predicted disordered residues to be flipped to be predicted as ordered residues, while the reverse was observed much less frequently. These results suggest that disorder tendencies are more sensitive to allowed mutations than structure tendencies and the conservation of disorder is indeed less stable than conservation of structure. Availability: five meta-predictors and four single models developed for this study will be publicly freely accessible for non-commercial use.     

Lipids in membrane protein structures

This review describes the recent knowledge about tightly bound lipids in membrane protein structures and deduces general principles of the binding interactions. Bound lipids are grouped in annular, nonannular, and integral protein lipids. The importance of lipid binding for vertical positioning and tight integration of proteins in the membrane, for assembly and stabilization of oligomeric and multisubunit complexes, for supercomplexes, as well as their functional roles are pointed out. Lipid binding is stabilized by multiple noncovalent interactions from protein residues to lipid head groups and hydrophobic tails. Based on analysis of lipids with refined head groups in membrane protein structures, distinct motifs were identified for stabilizing interactions between the phosphodiester moieties and side chains of amino acid residues. Differences between binding at the electropositive and electronegative membrane side, as well as a preferential binding to the latter, are observed. A first attempt to identify lipid head group specific binding motifs is made. A newly identified cardiolipin binding site in the yeast cytochrome bc(1) complex is described. Assignment of unsaturated lipid chains and evolutionary aspects of lipid binding are discussed.     

蛋白质亚细胞定位预测中的序列编码技术

蛋白质序列的编码是亚细胞定位预测问题中的关键技术之一。该文较为详细地介绍了目前已有的蛋白质序列编码算法;并指出了序列编码中存在的一些问题及可能的发展方向。     

Progress and challenges in protein structure prediction

Depending on whether similar structures are found in the PDB library, the protein structure prediction can be categorized into template-based modeling and free modeling. Although threading is an efficient tool to detect the structural analogs, the advancements in methodology development have come to a steady state. Encouraging progress is observed in structure refinement which aims at drawing template structures closer to the native; this has been mainly driven by the use of multiple structure templates and the development of hybrid knowledge-based and physics-based force fields. For free modeling, exciting examples have been witnessed in folding small proteins to atomic resolutions. However, predicting structures for proteins larger than 150 residues still remains a challenge, with bottlenecks from both force field and conformational search.