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/.     

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.     

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.     

Hydrophobicity and prediction of the secondary structure of membrane proteins and peptides.

Reliability of the hydropathy method to predict the formation of membrane-spanning alpha-helices by integral membrane proteins and peptides whose structure is known from X-ray crystallography is analysed. It is shown that Kyte-Doolittle hydropathy plots do not predict accurately 22 transmembrane alpha-helices in the reaction centres (RC) of the photosynthetic bacteria Rhodopseudomonas viridis and Rhodobacter sphaeroides (R-26). The accuracy of prediction for these proteins was improved using an optimised Kyte-Doolittle hydrophobicity scale. However, this hydrophobicity scale did not improve the predictions for the alphabeta-peptides of the B800-850 (LH2) complexes of the photosynthetic bacteria Rhodopseudomonas acidophila and Rhodospirillum molischianum, which were excluded from the optimisation procedure. The best and worst predictions of membrane-spanning alpha-helices for the RC proteins and LH2 peptides, respectively, were obtained with a propensity scale (PRC) calculated from the amino acid sequences and X-ray data for the RC proteins. A propensity scale (PLH) obtained using the amino acid sequences and X-ray data for the alphabeta-peptides of the LH2 complexes did not give an acceptable prediction of the transmembrane segments in the LH2 peptides; moreover, it markedly contradicted the PRC scale. Amino acids have been concluded to have no significant preference to localisation in transmembrane segments. Therefore, the predictive ability of the hydropathy methodology appears to be limited: the number of transmembrane segments can be correctly calculated for the best case only, and the lengths and positions of membrane-spanning alpha-helices in a protein amino acid sequence can not be predicted exactly.     

Combining hydrophobicity and helicity: a novel approach to membrane protein structure prediction

In spite of the overwhelming numbers and critical biological functions of membrane proteins, only a few have been characterized by high-resolution structural techniques. From the structures that are known, it is seen that their transmembrane (TM) segments tend to fold most often into alpha-helices. To evaluate systematically the features of these TM segments, we have taken two approaches: (1) using the experimentally-measured residence behavior of specifically designed hydrophobic peptides in RP-HPLC, a scale was derived based directly on the properties of individual amino acids incorporated into membrane-interactive helices: and (2) the relative alpha-helical propensity of each of the 20 amino acids was measured in the organic non-polar environment of n-butanol. By combining the resulting hydrophobicity and helical propensity data, in conjunction with consideration of the 'threshold hydrophobicity' required for spontaneous membrane integration of protein segments, an approach was developed for prediction of TM segments wherein each must fulfill the dual requirements of hydrophobicity and helicity. Evaluated against the available high-resolution structural data on membrane proteins, the present combining method is shown to provide accurate predictions for the locations of TM helices. In contrast, no segment in soluble proteins was predicted as a 'TM helix'.     

Genetic algorithm-based optimization of hydrophobicity tables

SUMMARY: The genomic abundance and pharmacological importance of membrane proteins have fueled efforts to identify them based solely on sequence information. Previous methods based on the physicochemical principle of a sliding window of hydrophobicity (hydropathy analysis) have been replaced by approaches based on hidden Markov models or neural networks which prevail due to their probabilistic orientation. In the current study, an optimization of the hydrophobicity tables used in hydropathy analysis is performed using a genetic algorithm. As such, the approach can be viewed as a synthesis between the physicochemically and statistically based methods. The resulting hydrophobicity tables lead to significant improvement in the prediction accuracy of hydropathy analysis. Furthermore, since hydropathy analysis is less dependent on the basis set of membrane proteins is used to hone the statistically based methods, as well as being faster, it may be valuable in the analysis of new genomes. Finally, the values obtained for each of the amino acids in the new hydrophobicity tables are discussed.     

Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes

We describe and validate a new membrane protein topology prediction method, TMHMM, based on a hidden Markov model. We present a detailed analysis of TMHMM's performance, and show that it correctly predicts 97-98 % of the transmembrane helices. Additionally, TMHMM can discriminate between soluble and membrane proteins with both specificity and sensitivity better than 99 %, although the accuracy drops when signal peptides are present. This high degree of accuracy allowed us to predict reliably integral membrane proteins in a large collection of genomes. Based on these predictions, we estimate that 20-30 % of all genes in most genomes encode membrane proteins, which is in agreement with previous estimates. We further discovered that proteins with N(in)-C(in) topologies are strongly preferred in all examined organisms, except Caenorhabditis elegans, where the large number of 7TM receptors increases the counts for N(out)-C(in) topologies. We discuss the possible relevance of this finding for our understanding of membrane protein assembly mechanisms. A TMHMM prediction service is available at http://www.cbs.dtu.dk/services/TMHMM/.     

PRALINETM: a strategy for improved multiple alignment of transmembrane proteins

MOTIVATION: Membrane-bound proteins are a special class of proteins. The regions that insert into the cell-membrane have a profoundly different hydrophobicity pattern compared with soluble proteins. Multiple alignment techniques use scoring schemes tailored for sequences of soluble proteins and are therefore in principle not optimal to align membrane-bound proteins. RESULTS: Transmembrane (TM) regions in protein sequences can be reliably recognized using state-of-the-art sequence prediction techniques. Furthermore, membrane-specific scoring matrices are available. We have developed a new alignment method, called PRALINETM, which integrates these two features to enhance multiple sequence alignment. We tested our algorithm on the TM alignment benchmark set by Bahr et al. (2001), and showed that the quality of TM alignments can be significantly improved compared with the quality produced by a standard multiple alignment technique. The results clearly indicate that the incorporation of these new elements into current state-of-the-art alignment methods is crucial for optimizing the alignment of TM proteins. AVAILABILITY: A webserver is available at http://www.ibi.vu.nl/programs/pralinewww.     

Using hydropathy features for function prediction of membrane proteins

A novel alignment-free method for computing functional similarity of membrane proteins based on features of hydropathy distribution is presented. The features of hydropathy distribution are used to represent protein families as hydropathy profiles. The profiles statistically summarize the hydropathy distribution of member proteins. The summation is made by using hydropathy features that numerically represent structurally/functionally significant portions of protein sequences. The hydropathy profiles are numerical vectors that are points in a high dimensional ‘hydropathy’ space. Their similarities are identified by projection of the space onto principal axes. Here, the approach is applied to the secondary transporters. The analysis using the presented approach is validated by the standard classification of the secondary transporters. The presented analysis allows for prediction of function attributes for proteins of uncharacterized families of secondary transporters. The results obtained using the presented analysis may help to characterize unknown function attributes of secondary transporters. They also show that analysis of hydropathy distribution can be used for function prediction of membrane proteins.     

Using hydropathy features for function prediction of membrane proteins

A novel alignment-free method for computing functional similarity of membrane proteins based on features of hydropathy distribution is presented. The features of hydropathy distribution are used to represent protein families as hydropathy profiles. The profiles statistically summarize the hydropathy distribution of member proteins. The summation is made by using hydropathy features that numerically represent structurally/functionally significant portions of protein sequences. The hydropathy profiles are numerical vectors that are points in a high dimensional 'hydropathy' space. Their similarities are identified by projection of the space onto principal axes. Here, the approach is applied to the secondary transporters. The analysis using the presented approach is validated by the standard classification of the secondary transporters. The presented analysis allows for prediction of function attributes for proteins of uncharacterized families of secondary transporters. The results obtained using the presented analysis may help to characterize unknown function attributes of secondary transporters. They also show that analysis of hydropathy distribution can be used for function prediction of membrane proteins.     

kPROT: a knowledge-based scale for the propensity of residue orientation in transmembrane segments. Application to membrane protein structure prediction

Modeling of integral membrane proteins and the prediction of their functional sites requires the identification of transmembrane (TM) segments and the determination of their angular orientations. Hydrophobicity scales predict accurately the location of TM helices, but are less accurate in computing angular disposition. Estimating lipid-exposure propensities of the residues from statistics of solved membrane protein structures has the disadvantage of relying on relatively few proteins. As an alternative, we propose here a scale of knowledge-based Propensities for Residue Orientation in Transmembrane segments (kPROT), derived from the analysis of more than 5000 non-redundant protein sequences. We assume that residues that tend to be exposed to the membrane are more frequent in TM segments of single-span proteins, while residues that prefer to be buried in the transmembrane bundle interior are present mainly in multi-span TMs. The kPROT value for each residue is thus defined as the logarithm of the ratio of its proportions in single and multiple TM spans. The scale is refined further by defining it for three discrete sections of the TM segment; namely, extracellular, central, and intracellular. The capacity of the kPROT scale to predict angular helical orientation was compared to that of alternative methods in a benchmark test, using a diversity of multi-span alpha-helical transmembrane proteins with a solved 3D structure. kPROT yielded an average angular error of 41 degrees, significantly lower than that of alternative scales (62 degrees -68 degrees ). The new scale thus provides a useful general tool for modeling and prediction of functional residues in membrane proteins. A WWW server (http://bioinfo.weizmann.ac.il/kPROT) is available for automatic helix orientation prediction with kPROT.     

Mapping Membrane Protein Backbone Dynamics: A Comparison of Site-Directed Spin Labeling with NMR N-Relaxation Measurements

The ability to detect nanosecond backbone dynamics with site-directed spin labeling (SDSL) in soluble proteins has been well established. However, for membrane proteins, the nitroxide appears to have more interactions with the protein surface, potentially hindering the sensitivity to backbone motions. To determine whether membrane protein backbone dynamics could be mapped with SDSL, a nitroxide was introduced at 55 independent sites in a model polytopic membrane protein, TM0026. Electron paramagnetic resonance spectral parameters were compared with NMR ¹⁵N-relaxation data. Sequential scans revealed backbone dynamics with the same trends observed for the R₁ relaxation rate, suggesting that nitroxide dynamics remain coupled to the backbone on membrane proteins.     

Mapping Membrane Protein Backbone Dynamics: A Comparison of Site-Directed Spin Labeling with NMR 15N-Relaxation Measurements

The ability to detect nanosecond backbone dynamics with site-directed spin labeling (SDSL) in soluble proteins has been well established. However, for membrane proteins, the nitroxide appears to have more interactions with the protein surface, potentially hindering the sensitivity to backbone motions. To determine whether membrane protein backbone dynamics could be mapped with SDSL, a nitroxide was introduced at 55 independent sites in a model polytopic membrane protein, TM0026. Electron paramagnetic resonance spectral parameters were compared with NMR ¹⁵N-relaxation data. Sequential scans revealed backbone dynamics with the same trends observed for the R₁ relaxation rate, suggesting that nitroxide dynamics remain coupled to the backbone on membrane proteins.     

Secreted protein prediction system combining CJ-SPHMM,TMHMM, and PSORT

To increase the coverage of secreted protein prediction, we describe a combination strategy. Instead of using a single method, we combine Hidden Markov Model (HMM)-based methods CJ-SPHMM and TMHMM with PSORT in secreted protein prediction. CJ-SPHMM is an HMM-based signal peptide prediction method, while TMHMM is an HMM-based transmembrane (TM) protein prediction algorithm. With CJ-SPHMM and TMHMM, proteins with predicted signal peptide and without predicted TM regions are taken as putative secreted proteins. This HMM-based approach predicts secreted protein with Ac (Accuracy) at 0.82 and Cc (Correlation coefficient) at 0.75, which are similar to PSORT with Ac at 0.82 and Cc at 0.76. When we further complement the HMM-based method, i.e., CJ-SPHMM + TMHMM with PSORT in secreted protein prediction, the Ac value is increased to 0.86 and the Cc value is increased to 0.81. Taking this combination strategy to search putative secreted proteins from the International Protein Index (IPI) maintained at the European Bioinformatics Institute (EBI), we constructed a putative human secretome with 5235 proteins. The prediction system described here can also be applied to predicting secreted proteins from other vertebrate proteomes. Availability: The CJ-SPHMM and predicted secreted proteins are available at: ftp://ftp.cbi.pku.edu.cn/pub/secreted-protein/     

The role of leucine and isoleucine in tuning the hydropathy of class A GPCRs

Leucine and Isoleucine are two amino acids that differ only by the positioning of one methyl group. This small difference can have important consequences in α-helices, as the β-branching of Ile results in helix destabilization. We set out to investigate whether there are general trends for the occurrences of Leu and Ile residues in the structures and sequences of class A GPCRs (G protein-coupled receptors). GPCRs are integral membrane proteins in which α-helices span the plasma membrane seven times and which play a crucial role in signal transmission. We found that Leu side chains are generally more exposed at the protein surface than Ile side chains. We explored whether this difference might be attributed to different functions of the two amino acids and tested if Leu tunes the hydrophobicity of the transmembrane domain based on the Wimley-White whole-residue hydrophobicity scales. Leu content decreases the variation in hydropathy between receptors and correlates with the non-Leu receptor hydropathy. Both measures indicate that hydropathy is tuned by Leu. To test this idea further, we generated protein sequences with random amino acid compositions using a simple numerical model, in which hydropathy was tuned by adjusting the number of Leu residues. The model was able to replicate the observations made with class A GPCR sequences. We speculate that the hydropathy of transmembrane domains of class A GPCRs is tuned by Leu (and to some lesser degree by Lys and Val) to facilitate correct insertion into membranes and/or to stably anchor the receptors within membranes.     

On filtering false positive transmembrane protein predictions

While helical transmembrane (TM) region prediction tools achieve high (>90%) success rates for real integral membrane proteins, they produce a considerable number of false positive hits in sequences of known nontransmembrane queries. We propose a modification of the dense alignment surface (DAS) method that achieves a substantial decrease in the false positive error rate. Essentially, a sequence that includes possible transmembrane regions is compared in a second step with TM segments in a sequence library of documented transmembrane proteins. If the performance of the query sequence against the library of documented TM segment-containing sequences in this test is lower than an empirical threshold, it is classified as a non-transmembrane protein. The probability of false positive prediction for trusted TM region hits is expressed in terms of E-values. The modified DAS method, the DAS-TMfilter algorithm, has an unchanged high sensitivity for TM segments ( approximately 95% detected in a learning set of 128 documented transmembrane proteins). At the same time, the selectivity measured over a non-redundant set of 526 soluble proteins with known 3D structure is approximately 99%, mainly because a large number of falsely predicted single membrane-pass proteins are eliminated by the DAS-TMfilter algorithm.     

Tight Turns of Outer Membrane Proteins: An Analysis of Sequence,Structure, and Hydrogen Bonding

As a structural class, tight turns can control molecular recognition, enzymatic activity, and nucleation of folding. They have been extensively characterized in soluble proteins but have not been characterized in outer membrane proteins (OMPs), where they also support critical functions. We clustered the 4 to 6 residue tight turns of 110 OMPs to characterize the phi/psi angles, sequence, and hydrogen bonding of these structures. We find significant differences between reports of soluble protein tight turns and OMP tight turns. Since OMP strands are less twisted than soluble strands, they favor different turn structures types. Moreover, the membrane localization of OMPs yields different sequence hallmarks for their tight turns relative to soluble protein turns. We also characterize the differences in phi/psi angles, sequence, and hydrogen bonding between OMP extracellular loops and OMP periplasmic turns. As previously noted, the extracellular loops tend to be much longer than the periplasmic turns. We find that this difference in length is due to the broader distribution of lengths of the extracellular loops not a large difference in the median length. Extracellular loops also tend to have more charged residues as predicted by the charge-out rule. Finally, in all OMP tight turns, hydrogen bonding between the side chain and backbone 2 to 4 residues away from that side chain plays an important role. These bonds preferentially use an Asp, Asn, Ser, or Thr residue in a beta or pro phi/psi conformation. We anticipate that this study will be applicable to future design and structure prediction of OMPs.     

Physicochemical factors for discriminating between soluble and membrane proteins: hydrophobicity of helical segments and protein length

The average hydrophobicity of a polypeptide segment is considered to be the most important factor in the formation of transmembrane helices, and the partitioning of the most hydrophobic (MH) segment into the alternative nonpolar environment, a membrane or hydrophobic core of a globular protein may determine the type of protein produced. In order to elucidate the importance of the MH segment in determining which of the two types of protein results from a given amino acid sequence, we statistically studied the characteristics of MH helices, longer than 19 residues in length, in 97 membrane proteins whose three-dimensional structure or topology is known, as well as 397 soluble proteins selected from the Protein Data Bank. The average hydrophobicity of MH helices in membrane proteins had a characteristic relationship with the length of the protein. All MH helices in membrane proteins that were longer than 500 residues had a hydrophobicity greater than 1.75 (Kyte and Doolittle scale), while the MH helices in membrane proteins smaller than 100 residues could be as hydrophilic as 0.1. The possibility of developing a method to discriminate membrane proteins from soluble ones, based on the effect of size on the type of protein produced, is discussed.     

Prediction of buried helices in multispan alpha helical membrane proteins

Analysis of a database of structures of membrane proteins shows that membrane proteins composed of 10 or more transmembrane (TM) helices often contain buried helices that are inaccessible to phospholipids. We introduce a method for identifying TM helices that are least phospholipid accessible and for prediction of fully buried TM helices in membrane proteins from sequence information alone. Our method is based on the calculation of residue lipophilicity and evolutionary conservation. Given that the number of buried helices in a membrane protein is known, our method achieves an accuracy of 78% and a Matthew's correlation coefficient of 0.68. A server for this tool (RANTS) is available online at http://gila.bioengr.uic.edu/lab/.     

Ser/Thr Motifs in Transmembrane Proteins: Conservation Patterns and Effects on Local Protein Structure and Dynamics

We combined systematic bioinformatics analyses and molecular dynamics simulations to assess the conservation patterns of Ser and Thr motifs in membrane proteins, and the effect of such motifs on the structure and dynamics of α-helical transmembrane (TM) segments. We find that Ser/Thr motifs are often present in β-barrel TM proteins. At least one Ser/Thr motif is present in almost half of the sequences of α-helical proteins analyzed here. The extensive bioinformatics analyses and inspection of protein structures led to the identification of molecular transporters with noticeable numbers of Ser/Thr motifs within the TM region. Given the energetic penalty for burying multiple Ser/Thr groups in the membrane hydrophobic core, the observation of transporters with multiple membrane-embedded Ser/Thr is intriguing and raises the question of how the presence of multiple Ser/Thr affects protein local structure and dynamics. Molecular dynamics simulations of four different Ser-containing model TM peptides indicate that backbone hydrogen bonding of membrane-buried Ser/Thr hydroxyl groups can significantly change the local structure and dynamics of the helix. Ser groups located close to the membrane interface can hydrogen bond to solvent water instead of protein backbone, leading to an enhanced local solvation of the peptide.