Recognition of alpha-helix transmembrane domains with an amphipathy scale generated by molecular dynamics using only the primary sequence of proteins

We recently developed an amphipathy scale, elaborated from molecular dynamics data that can be used for the identification of hydrophobic or hydrophilic regions in proteins. This amphipathy scale reflects side chain/water molecule interaction energies. We have now used this amphipathy scale to find candidates for transmembrane segments, by examining a large sample of membrane proteins with alpha-helix segments. The candidates were selected based on an amphipathy coefficient value range and the minimum number of residues in a segment. We compared our results with the transmembrane segments previously identified in the PDB_TM database by the TMDET algorithm. We expected that the hydrophobic segments would be identified using only the primary structures of the proteins and the amphipathy scale. However, some of these hydrophobic segments may pertain to hydrophobic pockets not included in transmembrane regions. We found that our amphipathy scale could identify alpha-helix transmembrane regions with a probability of success of 76% when all segments were included and 90% when all membrane proteins were included.     

An hierarchical artificial neural network system for the classification of transmembrane proteins.

This work presents a simple artificial neural network which classifies proteins into two classes from their sequences alone: the membrane protein class and the non-membrane protein class. This may be important in the functional assignment and analysis of open reading frames (ORF's) identified in complete genomes and, especially, those ORF's that correspond to proteins with unknown function. The network described here has a simple hierarchical feed-forward topology and a limited number of neurons which makes it very fast. By using only information contained in 11 protein sequences, the method was able to identify, with 100% accuracy, all membrane proteins with reliable topologies collected from several papers in the literature. Applied to a test set of 995 globular, water-soluble proteins, the neural network classified falsely 23 of them in the membrane protein class (97.7% of correct assignment). The method was also applied to the complete SWISS-PROT database with considerable success and on ORF's of several complete genomes. The neural network developed was associated with the PRED-TMR algorithm (Pasquier,C., Promponas,V.J., Palaios,G.A., Hamodrakas,J.S. and Hamodrakas,S.J., 1999) in a new application package called PRED-TMR2. A WWW server running the PRED-TMR2 software is available at http://o2.db.uoa.gr/PRED-TMR2     

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.     

The Escherichia coli aspartate receptor: sequence specificity of a transmembrane helix studied by hydrophobic-biased random mutagenesis.

The Escherichia coli aspartate receptor is a dimer with two transmembrane sequences per monomer that connect a periplasmic ligand binding domain to a cytoplasmic signaling domain. The method of 'hydrophobic-biased' random mutagenesis, that we describe here, was used to construct mutant aspartate receptors in which either the entire transmembrane sequence or seven residues near the center of the transmembrane sequence were replaced with hydrophobic and polar random residues. Some of these receptors responded to aspartate in an in vivo chemotaxis assay, while others did not. The acceptable substitutions included hydrophobic to polar residues, small to larger residues, and large to smaller residues. However, one mutant receptor that had only a few hydrophobic substitutions did not respond to aspartate. These results add to our understanding of sequence specificity in the transmembrane regions of proteins with more than one transmembrane sequence. This work also demonstrates a method of constructing families of mutant proteins containing random residues with chosen characteristics.     

The effect of nucleotide bias upon the composition and prediction of transmembrane helices

Transmembrane helices are the most readily predictable secondary structure components of proteins. They can be predicted to a high degree of accuracy in a variety of ways. Many of these methods compare new sequence data with the sequence characteristics of known transmembrane domains. However, the known transmembrane sequences are not necessarily representative of a particular organism. We attempt to demonstrate that parameters optimized for the known transmembrane domains are far from optimal when predicting transmembrane regions in a given genome. In particular, we have tested the effect of nucleotide bias upon the composition and hence the prediction characteristics of transmembrane helices. Our analysis shows that nucleotide bias of a genome has a strong and predictable influence upon the occurrences of several of the most important hydrophobic amino acids found within transmembrane helices. Thus, we show that nucleotide bias should be taken into account when determining putative transmembrane domains from sequence data.     

Computational differentiation of N-terminal signal peptides and transmembrane helices

Signal peptides and transmembrane helices both contain a stretch of hydrophobic amino acids. This common feature makes it difficult for signal peptide and transmembrane helix predictors to correctly assign identity to stretches of hydrophobic residues near the N-terminal methionine of a protein sequence. The inability to reliably distinguish between N-terminal transmembrane helix and signal peptide is an error with serious consequences for the prediction of protein secretory status or transmembrane topology. In this study, we report a new method for differentiating protein N-terminal signal peptides and transmembrane helices. Based on the sequence features extracted from hydrophobic regions (amino acid frequency, hydrophobicity, and the start position), we set up discriminant functions and examined them on non-redundant datasets with jackknife tests. This method can incorporate other signal peptide prediction methods and achieve higher prediction accuracy. For Gram-negative bacterial proteins, 95.7% of N-terminal signal peptides and transmembrane helices can be correctly predicted (coefficient 0.90). Given a sensitivity of 90%, transmembrane helices can be identified from signal peptides with a precision of 99% (coefficient 0.92). For eukaryotic proteins, 94.2% of N-terminal signal peptides and transmembrane helices can be correctly predicted with coefficient 0.83. Given a sensitivity of 90%, transmembrane helices can be identified from signal peptides with a precision of 87% (coefficient 0.85). The method can be used to complement current transmembrane protein prediction and signal peptide prediction methods to improve their prediction accuracies.     

Prediction of transmembrane regions of beta-barrel proteins using ANN- and SVM-based methods

This article describes a method developed for predicting transmembrane beta-barrel regions in membrane proteins using machine learning techniques: artificial neural network (ANN) and support vector machine (SVM). The ANN used in this study is a feed-forward neural network with a standard back-propagation training algorithm. The accuracy of the ANN-based method improved significantly, from 70.4% to 80.5%, when evolutionary information was added to a single sequence as a multiple sequence alignment obtained from PSI-BLAST. We have also developed an SVM-based method using a primary sequence as input and achieved an accuracy of 77.4%. The SVM model was modified by adding 36 physicochemical parameters to the amino acid sequence information. Finally, ANN- and SVM-based methods were combined to utilize the full potential of both techniques. The accuracy and Matthews correlation coefficient (MCC) value of SVM, ANN, and combined method are 78.5%, 80.5%, and 81.8%, and 0.55, 0.63, and 0.64, respectively. These methods were trained and tested on a nonredundant data set of 16 proteins, and performance was evaluated using "leave one out cross-validation" (LOOCV). Based on this study, we have developed a Web server, TBBPred, for predicting transmembrane beta-barrel regions in proteins (available at http://www.imtech.res.in/raghava/tbbpred).     

A neural network model for the prediction of membrane-spanning amino acid sequences

The architecture and weights of an artificial neural network model that predicts putative transmembrane sequences have been developed and optimized by the algorithm of structure evolution. The resulting filter is able to classify membrane/nonmembrane transition regions in sequences of integral human membrane proteins with high accuracy. Similar results have been obtained for both training and test set data, indicating that the network has focused on general features of transmembrane sequences rather than specializing on the training data. Seven physicochemical amino acid properties have been used for sequence encoding. The predictions are compared to hydrophobicity plots.     

A new amphipathy scale I. Determination of the scale from molecular dynamics data

Two new amphipathy scales elaborated from molecular dynamics data are presented. Their applications contribute for the identification of the hydrophobic or hydrophilic regions in proteins solely from the primary structure. The new amphipathy coefficients (AC) reflect the side chain/solvent molecules configurational energies. A polar (water) and an apolar solvent, CCl4, were used resulting in the two ACwater and ACCCl4 scales. These solvents were chosen to simulate the aqueous phases and the transmembrane ambients of cellular membranes where the membrane proteins act. The new amphipathy scales were compared with some previous scales determined by different methods, which were also compared between them, indicating more than 90% of the correlation coefficients are less than 0.9: the scales are strictly dependent on the methodologies used in their determination. The ACCCl4 scale is related with the size of side chain amino acids while ACwater is related with the hydrophobicity of side chain amino acids. The quality of the scales was confirmed by an example of application where ACwater was able to identify correctly the transmembrane, hydrophobic regions of a membrane protein. These results also indicate that water is an important factor responsible for the tertiary structure of membrane proteins.     

Computational methods for predicting structure of membrane proteins using amino acid sequences

The structure of membrane proteins specifies their functional properties, which are important for medicine and pharmacology and, therefore, is of significant interest. The repetition of transmembrane regions that consist of hydrophobic amino acids is a characteristic and organic feature of polytopic membrane proteins. The ordered repetition (periodicity) can be detected by the Fourier method applied to a digital image of the symbolic amino acid sequence of a protein. In the present work, this investigation was carried out for 24 transmembrane proteins (successfully for 14 of them). If the repetition of transmembrane regions is aperiodic, it can be revealed by another method, that is, the method of the reiterated (four to five times) averaging of the protein hydrophobicity function in a window within the limits of 9–11 amino acids that moves along the sequence. This novel method was applied to the 24 transmembrane proteins (successfully for 19 of them) and demonstrated higher suitability than the Fourier method for predicting the secondary structure of these proteins and the corresponding functional properties.     

A consensus algorithm to screen genomes for novel families of transmembrane beta barrel proteins

The ability to search sequence datasets for membrane spanning proteins is an important requirement for genome annotation. However, the development of algorithms to identify novel types of transmembrane beta-barrel (TMB) protein has proven substantially harder than for transmembrane helical proteins, owing to a shorter TM domain in which only alternate residues are hydrophobic. Although recent reports have described important improvements in the development of such algorithms, there is still concern over their ability to confidently screen genomes. Here we describe a new algorithm combining composition and hidden Markov model topology based classifiers (called TMB-Hunt2), which achieves a crossvalidation accuracy of >95%, with 96.7% precision and 94.2% recall. An overview is given of the algorithm design, with a thorough assessment of performance and application to a number of genomes. Of particular note is that TMB/extracellular protein discrimination is significantly more difficult than TMB/cytoplasmic protein discrimination, with the predictor correctly rejecting just 74% of extracellular proteins, in comparison to 98% of cytoplasmic proteins. Focus is given to directions for further improvements in TMB/non-TMB protein discrimination, with a call for the development of standardized tests and assessments of such algorithms. Tools and datasets are made available through a website called TMB-Web (http://www.bioinformatics.leeds.ac.uk/TMB-Web/TMB-Hunt2).     

Recognition of the carboxyl-terminal signal for GPI modification requires translocation of its hydrophobic domain across the ER membrane

A carboxyl-terminal hydrophobic domain is an essential component of the processed signal for attachment of the glycosyl-phosphatidylinositol (GPI) membrane anchor to proteins and it is linked to the site (omega) of GPI modification by a spacer domain. This study was designed to test the hypothesis that the hydrophobic domain interacts with the lipid bilayer of the endoplasmic reticulum (ER) membrane to optimally position the omega site for GPI modification. The hydrophobic domain of the GPI signal in the human folate receptor (FR) type alpha was substituted with the carboxyl-terminal segment of the low-density lipoprotein receptor (LDLR), including its membrane spanning region, without altering either the spacer or the omega site. The FR-alpha/LDLR chimera was not GPI modified but was attached to the plasma membrane by a polypeptide anchor. When the carboxyl-terminal half of the hydrophobic transmembrane polypeptide in the FR-alpha/LDLR chimera was altered by introduction of negatively charged (Asp) residues, or when the cytosolic domain in the chimera was deleted, the mutated proteins became GPI-anchored. On the other hand, attachment of a carboxyl-terminal segment of LDLR including the entire cytosolic domain to FR-alpha converted it into a transmembrane protein. The results indicate that in the FR-alpha/LDLR chimera the inability of the cellular machinery for GPI modification to recognize the hydrophobic domain is not due to the intrinsic nature of the peptide, but is rather due to the retention of the peptide within the lipid bilayer. It follows that the hydrophobic domain in the signal for GPI modification must traverse the ER membrane prior to recognition of the omega site by the GPI-protein transamidase. The results thus establish a critical topographical requirement for recognition of the GPI signal in the ER.     

A new amphipathy scale: I. Determination of the scale from molecular dynamics data

Two new amphipathy scales elaborated from molecular dynamics data are presented. Their applications contribute for the identification of the hydrophobic or hydrophilic regions in proteins solely from the primary structure. The new amphipathy coefficients (AC) reflect the side chain/solvent molecules configurational energies. A polar (water) and an apolar solvent, CCl₄, were used resulting in the two AC_water and AC_CCl4 scales. These solvents were chosen to simulate the aqueous phases and the transmembrane ambients of cellular membranes where the membrane proteins act. The new amphipathy scales were compared with some previous scales determined by different methods, which were also compared between them, indicating more than 90% of the correlation coefficients are less than 0.9: the scales are strictly dependent on the methodologies used in their determination. The AC_CCl4 scale is related with the size of side chain amino acids while AC_water is related with the hydrophobicity of side chain amino acids. The quality of the scales was confirmed by an example of application where AC_water was able to identify correctly the transmembrane, hydrophobic regions of a membrane protein. These results also indicate that water is an important factor responsible for the tertiary structure of membrane proteins.     

Membrane localization of cyclic nucleotide phosphodiesterase 3 (PDE3). Two N-terminal domains are required for the efficient targeting to, and association of, PDE3 with endoplasmic reticulum

Subcellular localization of cyclic nucleotide phosphodiesterases (PDEs) may be important in compartmentalization of cAMP/cGMP signaling responses. In 3T3-L1 adipocytes, mouse (M) PDE3B was associated with the endoplasmic reticulum (ER) as indicated by its immunofluorescent colocalization with the ER protein BiP and subcellular fractionation studies. In transfected NIH 3006 or COS-7 cells, recombinant wild-type PDE3A and PDE3B isoforms were both found almost exclusively in the ER. The N-terminal portion of PDE3 can be arbitrarily divided into region 1 (aa 1-300), which contains a large hydrophobic domain with six predicted transmembrane helices, followed by region 2 (aa 301-500) containing a smaller hydrophobic domain (of approximately 50 aa). To investigate the role of regions 1 and 2 in membrane association, we examined the subcellular localization of a series of catalytically active, Flag-tagged N-terminal-truncated human (H) PDE3A and MPDE3B recombinants, as well as a series of fragments from regions 1 and 2 of MPDE3B synthesized as enhanced green fluorescent (EGFP) fusion proteins in COS-7 cells. In COS-7 cells, the localization of a mutant HPDE3A, lacking the first 189 amino acids (aa) and therefore four of the six predicted transmembrane helices (H3A-Delta189), was virtually identical to that of the wild type. M3B-Delta302 (lacking region 1) and H3A-Delta397 (lacking region 1 as well as part of region 2) retained, to different degrees, the ability to associate with membranes, albeit less efficiently than H3A-Delta189. Proteins that lacked both regions 1 and 2, H3A-Delta510 and M3B-Delta604, did not associate with membranes. Consistent with these findings, region 1 EGFP-MPDE3B fusion proteins colocalized with the ER, whereas region 2 EGFP fusion proteins were diffusely distributed. Thus, some portion of the N-terminal hydrophobic domain in region 1 plus a second domain in region 2 are important for efficient membrane association/targeting of PDE3.     

Transmembrane helix predictions revisited

Methods that predict membrane helices have become increasingly useful in the context of analyzing entire proteomes, as well as in everyday sequence analysis. Here, we analyzed 27 advanced and simple methods in detail. To resolve contradictions in previous works and to reevaluate transmembrane helix prediction algorithms, we introduced an analysis that distinguished between performance on redundancy-reduced high- and low-resolution data sets, established thresholds for significant differences in performance, and implemented both per-segment and per-residue analysis of membrane helix predictions. Although some of the advanced methods performed better than others, we showed in a thorough bootstrapping experiment based on various measures of accuracy that no method performed consistently best. In contrast, most simple hydrophobicity scale-based methods were significantly less accurate than any advanced method as they overpredicted membrane helices and confused membrane helices with hydrophobic regions outside of membranes. In contrast, the advanced methods usually distinguished correctly between membrane-helical and other proteins. Nonetheless, few methods reliably distinguished between signal peptides and membrane helices. We could not verify a significant difference in performance between eukaryotic and prokaryotic proteins. Surprisingly, we found that proteins with more than five helices were predicted at a significantly lower accuracy than proteins with five or fewer. The important implication is that structurally unsolved multispanning membrane proteins, which are often important drug targets, will remain problematic for transmembrane helix prediction algorithms. Overall, by establishing a standardized methodology for transmembrane helix prediction evaluation, we have resolved differences among previous works and presented novel trends that may impact the analysis of entire proteomes.     

Life at the border: Adaptation of proteins to anisotropic membrane environment

This review discusses main features of transmembrane (TM) proteins which distinguish them from water‐soluble proteins and allow their adaptation to the anisotropic membrane environment. We overview the structural limitations on membrane protein architecture, spatial arrangement of proteins in membranes and their intrinsic hydrophobic thickness, co‐translational and post‐translational folding and insertion into lipid bilayers, topogenesis, high propensity to form oligomers, and large‐scale conformational transitions during membrane insertion and transport function. Special attention is paid to the polarity of TM protein surfaces described by profiles of dipolarity/polarizability and hydrogen‐bonding capacity parameters that match polarity of the lipid environment. Analysis of distributions of Trp resides on surfaces of TM proteins from different biological membranes indicates that interfacial membrane regions with preferential accumulation of Trp indole rings correspond to the outer part of the lipid acyl chain region—between double bonds and carbonyl groups of lipids. These “midpolar” regions are not always symmetric in proteins from natural membranes. We also examined the hydrophobic effect that drives insertion of proteins into lipid bilayer and different free energy contributions to TM protein stability, including attractive van der Waals forces and hydrogen bonds, side‐chain conformational entropy, the hydrophobic mismatch, membrane deformations, and specific protein–lipid binding.     

Analysis of dihedral angle preferences for alanine and glycine residues in alpha and beta transmembrane regions

For the past 50?years, the Ramachandran map has been used effectively to study the protein structure and folding. However, though extensive analysis has been done on dihedral angle preferences of residues in globular proteins, related studies and reports of membrane proteins are limited. It is of interest to explore the conformational preferences of residues in transmembrane regions of membrane proteins which are involved in several important and diverse biological processes. Hence, in the present work, a systematic comparative computational analysis has been made on dihedral angle preferences of alanine and glycine in alpha and beta transmembrane regions (the two major classes of transmembrane proteins) with the aid of the Ramachandran map. Further, the conformational preferences of residues in transmembrane regions were compared with the non-transmembrane regions. We have extracted cation-pi interacting residues present in transmembrane regions and explored the dihedral angle preferences. From our observations, we reveal the higher percentage of occurrences of glycine in alpha and beta transmembrane regions than other hydrophobic residues. Further, we noted a clear shift in ψ-angle preferences of glycine residues from negative bins in alpha transmembrane regions to positive bins in beta transmembrane regions. Also, cation-pi interacting residues in beta transmembrane regions avoid preferring ψ-angles in the range of ?59° to ?30°. In this article, we insist that the studies on preferences of dihedral angles in transmembrane regions, thorough understanding of structure and folding of transmembrane proteins, can lead to modeling of novel transmembrane regions towards designing membrane proteins.     

Use of pyrene as a luminescence indicator of the viscosity of model and biological membranes

The pyrene movement in a lipid bilayer has been shown to occur not only in the lateral but also transmembrane direction. Within the excited state lifetime the pyrene monomer elevates from the depth to the polar regions of the membrane and emits a luminescence photon. The excimer does not exhibit any marked transmembrane movement while luminescing from the hydrophobic regions. The luminescence quenching efficiency of monomers and excimers depends on the depth of quencher penetration into the membrane. In the lipid bilayer the pyrene luminescence is strongly quenched by molecular oxygen. The pyrene binding to membrane proteins protects it from quenching. A conclusion has been made that the carrying out estimations of membrane viscosity from pyrene luminescence require considerable correction.     

Modeling and docking of the three-dimensional structure of the human melanocortin 4 receptor

A three-dimensional structure of the human melanocortin 4 receptor (hMC4R) is constructed in this study using a computer-aided molecular modeling approach. Human melanocortin 4 receptor is a G Protein-Coupled Receptor (GPCR). We structurally aligned transmembrane helices with bovine rhodopsin transmembrane domains, simulated both intracellular and extracellular loop domains on homologous loop regions in other proteins of known 3D structure and modeled the C terminus on the corresponding part of bovine rhodopsin. Then tandem minimization and dynamics calculations were run to refine the crude structure. The simulative model was tested by docking with a triplet peptide (RFF) ligand. It was found that the ligand is located among transmembrane regions TM3, TM4, TM5, and TM6 of hMC4R. In consistence with mutational and biochemical data, binding site is mainly formed as a hydrophobic and negatively charged pocket. The model constructed here might provide a structural framework for making rational predictions in relevant fields.