基于小波分析的膜蛋白跨膜区段序列分析和预测

膜蛋白是一类结构独特的蛋白质,在各种细胞中普遍存在,发挥着重要的生理功能。目前仅有少数膜蛋白听结构被实验测出,因此用计算机预测膜蛋白的结构是蛋白质结构预测的主要研究内容之一。膜蛋白一般在膜上形成保守的跨膜螺旋结构,序列特征明显,比较适合用预测的方法确定跨膜螺旋区段的位置。国际上已有一些研究者用人工神经网络方法、多序列比对方法和统计方法进行了预测尝试,取得了一定的成功经验。我们对蛋白质序列数据库中的     

TM Finder: a prediction program for transmembrane protein segments using a combination of hydrophobicity and nonpolar phase helicity scales

Based on the principle of dual prediction by segment hydrophobicity and nonpolar phase helicity, in concert with imposed threshold values of these two parameters, we developed the automated prediction program TM Finder that can successfully locate most transmembrane (TM) segments in proteins. The program uses the results of experiments on a series of host-guest TM segment mimic peptides of prototypic sequence KK AAAXAAAAAXAAWAAXAAAKKKK-amide (where X = each of the 20 commonly occurring amino acids) through which an HPLC-derived hydropathy scale, a hydrophobicity threshold for spontaneous membrane insertion, and a nonpolar phase helical propensity scale were determined. Using these scales, the optimized prediction algorithm of TM Finder defines TM segments by first searching for competent core segments using the combination of hydrophobicity and helicity scales, and then performs a gap-joining operation, which minimizes prediction bias caused by local hydrophilic residues and/or the choice of window size. In addition, the hydrophobicity threshold requirement enables TM Finder to distinguish reliably between membrane proteins and globular proteins, thereby adding an important dimension to the program. A full web version of the TM Finder program can be accessed at http://www.bioinformatics-canada.org/TM/.     

A novel method for predicting transmembrane segments in proteins based on a statistical analysis of the SwissProt database: the PRED-TMR algorithm.

We present a novel method that predicts transmembrane domains in proteins using solely information contained in the sequence itself. The PRED-TMR algorithm described, refines a standard hydrophobicity analysis with a detection of potential termini ('edges', starts and ends) of transmembrane regions. This allows one both to discard highly hydrophobic regions not delimited by clear start and end configurations and to confirm putative transmembrane segments not distinguishable by their hydrophobic composition. The accuracy obtained on a test set of 101 non-homologous transmembrane proteins with reliable topologies compares well with that of other popular existing methods. Only a slight decrease in prediction accuracy was observed when the algorithm was applied to all transmembrane proteins of the SwissProt database (release 35). A WWW server running the PRED-TMR algorithm is available at http://o2.db.uoa. gr/PRED-TMR/     

Empirical lipid propensities of amino acid residues in multispan alpha helical membrane proteins

Characterizing the interactions between amino acid residues and lipid molecules is important for understanding the assembly of transmembrane helices and for studying membrane protein folding. In this study we develop TMLIP (TransMembrane helix-LIPid), an empirically derived propensity of individual residue types to face lipid membrane based on statistical analysis of high-resolution structures of membrane proteins. Lipid accessibilities of amino acid residues within the transmembrane (TM) region of 29 structures of helical membrane proteins are studied with a spherical probe of radius of 1.9 A. Our results show that there are characteristic preferences for residues to face the headgroup region and the hydrocarbon core region of lipid membrane. Amino acid residues Lys, Arg, Trp, Phe, and Leu are often found exposed at the headgroup regions of the membrane, where they have high propensity to face phospholipid headgroups and glycerol backbones. In the hydrocarbon core region, the strongest preference for interacting with lipids is observed for Ile, Leu, Phe and Val. Small and polar amino acid residues are usually buried inside helical bundles and are strongly lipophobic. There is a strong correlation between various hydrophobicity scales and the propensity of a given residue to face the lipids in the hydrocarbon region of the bilayer. Our data suggest a possibly significant contribution of the lipophobic effect to the folding of membrane proteins. This study shows that membrane proteins have exceedingly apolar exteriors rather than highly polar interiors. Prediction of lipid-facing surfaces of boundary helices using TMLIP1 results in a 54% accuracy, which is significantly better than random (25% accuracy). We also compare performance of TMLIP with another lipid propensity scale, kPROT, and with several hydrophobicity scales using hydrophobic moment analysis.     

A knowledge-based scale for amino acid membrane propensity

In this article, a membrane-propensity scale for amino acids is derived using only two ingredients: (i) a set of transmembrane helices segments from membrane protein crystal structures and (ii) the request that each component of the set has a free energy lower than that of a typical soluble protein sequence of the same length. Although the most widely used hydropathy scales satisfy this request, we use an optimization procedure that allows for extraction of an optimal scale, which correlates equally well with those scales. We show that, if the choice of the sequence database is accurate, significant knowledge-based scales, which are robust with respect to changes in the learning set, can be easily derived. The obtained scales can be used for transmembrane helices prediction. The predictive power of one of these scales is tested on membrane proteins, soluble proteins, and signal peptides databases, finding that its performances is comparable with those of the hydropathy scales.     

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.     

Prediction of rotational orientation of transmembrane helical segments of integral membrane proteins using new environment-based propensities for amino acids derived from structural analyses

Alpha-helical integral-membrane proteins (IMPs) play a key role in many biological processes, such as signal transduction, and are targets for >50% of current therapeutic drugs. In contrast to their significant abundance and biological importance, they comprise <1% of structurally solved proteins. In the absence of experimental evidence, molecular modeling of IMP structures is an alternative for providing structural information and aiding further experimental design. In the current work, we propose two new amino acid lipid-facing propensity scales derived from the structural analysis of a nonredundant set of water-soluble proteins. The new scales, pi and delta, perform as well or better than published scales (Carugo's hydrophobicity and kPROT scales) in predicting the lipid-facing side of helical segments of a set of structurally solved IMPs, thus indicating (a) that the folding properties of water-soluble proteins and IMPs are similar, and (b) that the new scales will prove useful in modeling the transmembrane segments of IMPs.     

Predicting the topology of transmembrane helical proteins using mean burial propensity and a hidden-Markov-model-based method

Helices in membrane spanning regions are more tightly packed than the helices in soluble proteins. Thus, we introduce a method that uses a simple scale of burial propensity and a new algorithm to predict transmembrane helical (TMH) segments and a positive-inside rule to predict amino-terminal orientation. The method (the topology predictor of transmembrane helical proteins using mean burial propensity [THUMBUP]) correctly predicted the topology of 55 of 73 proteins (or 75%) with known three-dimensional structures (the 3D helix database). This level of accuracy can be reached by MEMSAT 1.8 (a 200-parameter model-recognition method) and a new HMM-based method (a 111-parameter hidden Markov model, UMDHMM(TMHP)) if they were retrained with the 73-protein database. Thus, a method based on a physiochemical property can provide topology prediction as accurate as those methods based on more complicated statistical models and learning algorithms for the proteins with accurately known structures. Commonly used HMM-based methods and MEMSAT 1.8 were trained with a combination of the partial 3D helix database and a 1D helix database of TMH proteins in which topology information were obtained by gene fusion and other experimental techniques. These methods provide a significantly poorer prediction for the topology of TMH proteins in the 3D helix database. This suggests that the 1D helix database, because of its inaccuracy, should be avoided as either a training or testing database. A Web server of THUMBUP and UMDHMM(TMHP) is established for academic users at http://www.smbs.buffalo.edu/phys_bio/service.htm. The 3D helix database is also available from the same Web site.     

Prediction of helix-helix contacts and interacting helices in polytopic membrane proteins using neural networks

Despite rapidly increasing numbers of available 3D structures, membrane proteins still account for less than 1% of all structures in the Protein Data Bank. Recent high-resolution structures indicate a clearly broader structural diversity of membrane proteins than initially anticipated, motivating the development of reliable structure prediction methods specifically tailored for this class of molecules. One important prediction target capturing all major aspects of a protein's 3D structure is its contact map. Our analysis shows that computational methods trained to predict residue contacts in globular proteins perform poorly when applied to membrane proteins. We have recently published a method to identify interacting alpha-helices in membrane proteins based on the analysis of coevolving residues in predicted transmembrane regions. Here, we present a substantially improved algorithm for the same problem, which uses a newly developed neural network approach to predict helix-helix contacts. In addition to the input features commonly used for contact prediction of soluble proteins, such as windowed residue profiles and residue distance in the sequence, our network also incorporates features that apply to membrane proteins only, such as residue position within the transmembrane segment and its orientation toward the lipophilic environment. The obtained neural network can predict contacts between residues in transmembrane segments with nearly 26% accuracy. It is therefore the first published contact predictor developed specifically for membrane proteins performing with equal accuracy to state-of-the-art contact predictors available for soluble proteins. The predicted helix-helix contacts were employed in a second step to identify interacting helices. For our dataset consisting of 62 membrane proteins of solved structure, we gained an accuracy of 78.1%. Because the reliable prediction of helix interaction patterns is an important step in the classification and prediction of membrane protein folds, our method will be a helpful tool in compiling a structural census of membrane proteins.     

A critical evaluation of the hydropathy profile of membrane proteins

New membrane-preference scales are introduced for categories of membrane proteins with different functions. A statistical analysis is carried out with several scales to verify the relative accuracy in the prediction of the transmembrane segments of polytopic membrane proteins. The correlation between some of the scales most used and those calculated here provides criteria for selecting the most appropriate methods for a given type of protein. The parameters used in the evaluation of the hydropathy profiles have been carefully ascertained in order to develop a reliable methodology for hydropathy analysis. Finally, an integrated hydropathy analysis using different methods has been applied to several sequences of related proteins. The above analysis indicates that (a) microsomal cytochrome P450 contains only one hydrophobic region at the N-terminus that is consistently predicted to transverse the membrane: (b) only four of the six or seven putative transmembrane helices of cytochrome oxidase subunit III are predicted and correspond to helices I, III, V and VI of the previous nomenclature; (c) the product of the mitochondrial ATPase-6 gene (or the chloroplast ATPase-IV gene) of F0-F1-ATPase shows that helix IV is not consistently predicted to traverse the membrane, suggesting a four-helix model for this family of proteins.     

An amino acid "transmembrane tendency" scale that approaches the theoretical limit to accuracy for prediction of transmembrane helices: relationship to biological hydrophobicity

Hydrophobicity analyses applied to databases of soluble and transmembrane (TM) proteins of known structure were used to resolve total genomic hydrophobicity profiles into (helical) TM sequences and mainly "subhydrophobic" soluble components. This information was used to define a refined "hydrophobicity"-type TM sequence prediction scale that should approach the theoretical limit of accuracy. The refinement procedure involved adjusting scale values to eliminate differences between the average amino acid composition of populations TM and soluble sequences of equal hydrophobicity, a required property of a scale having maximum accuracy. Application of this procedure to different hydrophobicity scales caused them to collapse to essentially a single TM tendency scale. As expected, when different scales were compared, the TM tendency scale was the most accurate at predicting TM sequences. It was especially highly correlated (r = 0.95) to the biological hydrophobicity scale, derived experimentally from the percent TM conformation formed by artificial sequences passing though the translocon. It was also found that resolution of total genomic sequence data into TM and soluble components could be used to define the percent probability that a sequence with a specific hydrophobicity value forms a TM segment. Application of the TM tendency scale to whole genomic data revealed an overlap of TM and soluble sequences in the "semihydrophobic" range. This raises the possibility that a significant number of proteins have sequences that can switch between TM and non-TM states. Such proteins may exist in moonlighting forms having properties very different from those of the predominant conformation.     

PHAT: a transmembrane-specific substitution matrix. Predicted hydrophobic and transmembrane

MOTIVATION: Database searching algorithms for proteins use scoring matrices based on average protein properties, and thus are dominated by globular proteins. However, since transmembrane regions of a protein are in a distinctly different environment than globular proteins, one would expect generalized substitution matrices to be inappropriate for transmembrane regions. RESULTS: We present the PHAT (predicted hydrophobic and transmembrane) matrix, which significantly outperforms generalized matrices and a previously published transmembrane matrix in searches with transmembrane queries. We conclude that a better matrix can be constructed by using background frequencies characteristic of the twilight zone, where low-scoring true positives have scores indistinguishable from high-scoring false positives, rather than the amino acid frequencies of the database. The PHAT matrix may help improve the accuracy of sequence alignments and evolutionary trees of membrane proteins.     

Estimating intrinsic structural preferences of de novo emerging random-sequence proteins: Is aggregation the main bottleneck?

Present-day proteins are believed to have evolved features to reduce the risk of aggregation. However, proteins can emerge de novo by translation of non-coding DNA segments. In this study we assess the aggregation, disorder and transmembrane propensity of protein sequences generated by translating random nucleotide sequences of varying GC-content. Potential de novo random-sequence proteins translated from regions with GC content between 40% and 60% do not show stronger aggregation propensity than existing ones and exhibit similar tendency to be disordered. We suggest that de novo emerging proteins do not mean an unavoidable aggregation threat to evolving organisms.     

PredβTM: A Novel β-Transmembrane Region Prediction Algorithm

Predicting the transmembrane regions is an important aspect of understanding the structures and architecture of different β-barrel membrane proteins. Despite significant efforts, currently available β-transmembrane region predictors are still limited in terms of prediction accuracy, especially in precision. Here, we describe PredβTM, a transmembrane region prediction algorithm for β-barrel proteins. Using amino acid pair frequency information in known β-transmembrane protein sequences, we have trained a support vector machine classifier to predict β-transmembrane segments. Position-specific amino acid preference data is incorporated in the final prediction. The predictor does not incorporate evolutionary profile information explicitly, but is based on sequence patterns generated implicitly by encoding the protein segments using amino acid adjacency matrix. With a benchmark set of 35 β-transmembrane proteins, PredβTM shows a sensitivity and precision of 83.71% and 72.98%, respectively. The segment overlap score is 82.19%. In comparison with other state-of-art methods, PredβTM provides a higher precision and segment overlap without compromising with sensitivity. Further, we applied PredβTM to analyze the β-barrel membrane proteins without defined transmembrane regions and the uncharacterized protein sequences in eight bacterial genomes and predict possible β-transmembrane proteins. PredβTM can be freely accessed on the web at http://transpred.ki.si/.     

QT interval measurement using RMED curve; a novel approach based on wavelet techniques

Hypothesis/objective: Prolonged QT interval is an index of propensity for dangerous ventricular tachyarrhythmias. The aim of this article is to establish an automatic algorithm for QT interval measurement.

Method: The proposed method is based on the continuous wavelet transform. In this method, the concepts of the rescaled wavelet coefficients and dominant scales of the electrocardiogram (ECG) components are used to perform detection of ECG characteristic points. A new concept of rescaled maximum energy density is introduced so as to perform the estimation of the QT interval.

Results and conclusion: We have applied the algorithm to the PTB database of the Physiobank?Physionet in lead II. Then, the results were evaluated using pertinent reference QT. The criterion used for evaluation of the method's performance is the root mean square (RMS) error. The method approached the RMS error of 27.89 ms for 549 subjects. The proposed method is fast, simple and is applicable to a wide range of ECG cardio cycle morphologies.     

A turn propensity scale for transmembrane helices.

Using a model protein with a 40 residue hydrophobic transmembrane segment, we have measured the ability of all the 20 naturally occurring amino acids to form a tight turn when placed in the middle of the hydrophobic segment. Turn propensities in a transmembrane helix are found to be markedly different from those of globular proteins, and in most cases correlate closely with the hydrophobicity of the residue. The turn propensity scale may be used to improve current methods for membrane protein topology prediction.     

Knowledge-based potential for positioning membrane-associated structures and assessing residue-specific energetic contributions

The complex hydrophobic and hydrophilic milieus of membrane-associated proteins pose experimental and theoretical challenges to their understanding. Here, we produce a nonredundant database to compute knowledge-based asymmetric cross-membrane potentials from the per-residue distributions of C(β), C(γ) and functional group atoms. We predict transmembrane and peripherally associated regions from genomic sequence and position peptides and protein structures relative to the bilayer (available at http://www.degradolab.org/ez). The pseudo-energy topological landscapes underscore positional stability and functional mechanisms demonstrated here for antimicrobial peptides, transmembrane proteins, and viral fusion proteins. Moreover, experimental effects of point mutations on the relative ratio changes of dual-topology proteins are quantitatively reproduced. The functional group potential and the membrane-exposed residues display the largest energetic changes enabling to detect native-like structures from decoys. Hence, focusing on the uniqueness of membrane-associated proteins and peptides, we quantitatively parameterize their cross-membrane propensity, thus facilitating structural refinement, characterization, prediction, and design.     

A novel tool for the prediction of transmembrane protein topology based on a statistical analysis of the SwissProt database: the OrienTM algorithm

OrienTM is a computer software that utilizes an initial definition of transmembrane segments to predict the topology of transmembrane proteins from their sequence. It uses position-specific statistical information for amino acid residues which belong to putative non-transmembrane segments derived from statistical analysis of non-transmembrane regions of membrane proteins stored in the SwissProt database. Its accuracy compares well with that of other popular existing methods. A web-based version of OrienTM is publicly available at the address http://biophysics.biol.uoa.gr/OrienTM.     

Benchmarking B cell epitope prediction: underperformance of existing methods

Sequence profiling is used routinely to predict the location of B-cell epitopes. In the postgenomic era, the need for reliable epitope prediction is clear. We assessed 484 amino acid propensity scales in combination with ranges of plotting parameters to examine exhaustively the correlation of peaks and epitope location within 50 proteins mapped for polyclonal responses. After examining more than 10(6) combinations, we found that even the best set of scales and parameters performed only marginally better than random. Our results confirm the null hypothesis: Single-scale amino acid propensity profiles cannot be used to predict epitope location reliably. The implication for studies using such methods is obvious.