Both helical propensity and side-chain hydrophobicity at a partially exposed site in alpha-helix contribute to the thermodynamic stability of ubiquitin

Pfam family DUF1023 consists entirely of uncharacterized proteins generated by sequencing the genomes of Actinobacteria (Bateman A., et al., Nucleic Acids Res. 2004;32 Database issue:D138-141.) Utilizing sequence similarity detection methods, we infer homology between DUF1023 and alpha/beta hydrolases. DUF1023 proteins conserve the core secondary structures in alpha/beta hydrolase fold, and share similar catalytic machinery as that of alpha/beta hydrolases. We predict DUF1023 spatial structure and deduce that they function as hydrolases utilizing catalytic Ser-His-Asp triad with the serine as a nucleophile.     

A combined transmembrane topology and signal peptide prediction method

An inherent problem in transmembrane protein topology prediction and signal peptide prediction is the high similarity between the hydrophobic regions of a transmembrane helix and that of a signal peptide, leading to cross-reaction between the two types of predictions. To improve predictions further, it is therefore important to make a predictor that aims to discriminate between the two classes. In addition, topology information can be gained when successfully predicting a signal peptide leading a transmembrane protein since it dictates that the N terminus of the mature protein must be on the non-cytoplasmic side of the membrane. Here, we present Phobius, a combined transmembrane protein topology and signal peptide predictor. The predictor is based on a hidden Markov model (HMM) that models the different sequence regions of a signal peptide and the different regions of a transmembrane protein in a series of interconnected states. Training was done on a newly assembled and curated dataset. Compared to TMHMM and SignalP, errors coming from cross-prediction between transmembrane segments and signal peptides were reduced substantially by Phobius. False classifications of signal peptides were reduced from 26.1% to 3.9% and false classifications of transmembrane helices were reduced from 19.0% to 7.7%. Phobius was applied to the proteomes of Homo sapiens and Escherichia coli. Here we also noted a drastic reduction of false classifications compared to TMHMM/SignalP, suggesting that Phobius is well suited for whole-genome annotation of signal peptides and transmembrane regions. The method is available at as well as at     

Rapid exploration of the folding topology of helical membrane proteins using paramagnetic perturbation

An understanding of the folding states of α-helical membrane proteins in detergent systems is important for functional and structural studies of these proteins. Here, we present a rapid and simple method for identification of the folding topology and assembly of transmembrane helices using paramagnetic perturbation in nuclear magnetic resonance spectroscopy. By monitoring the perturbation of signals from glycine residues located at specific sites, the folding topology and the assembly of transmembrane helices of membrane proteins were easily identified without time-consuming backbone assignment. This method is validated with Mistic (membrane-integrating sequence for translation of integral membrane protein constructs) of known structure as a reference protein. The folding topologies of two bacterial histidine kinase membrane proteins (SCO3062 and YbdK) were investigated by this method in dodecyl phosphocholine (DPC) micelles. Combing with analytical ultracentrifugation, we identified that the transmembrane domain of YbdK is present as a parallel dimer in DPC micelle. In contrast, the interaction of transmembrane domain of SCO3062 is not maintained in DPC micelle due to disruption of native structure of the periplasmic domain by DPC micelle.     

Multiscale simulation of transmembrane proteins

Multiscale simulation is employed to examine changes in atomistic-level protein structure due to long wavelength membrane undulations and plane stress fields. An ensemble of atomistic-level simulations of a model of a transmembrane influenza A virus M2 proton channel in a dimyristoylphosphatidylcholine (DMPC) bilayer is coupled to a corresponding mesoscopic model of a DMPC bilayer in an explicit mesoscopic solvent. Structural variations in the key proton gating His37 residues of the M2 channel are examined. Small, but distinct variations in the structure of the His37 residues are observed in both the open and closed states of the channel as a result of the coupling to mesoscopic-level membrane motions.     

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.     

Proteome-wide functional classification and identification of prokaryotic transmembrane proteins by transmembrane topology similarity comparison

We propose a new method for classifying and identifying transmembrane (TM) protein functions in proteome-scale by applying a single-linkage clustering method based on TM topology similarity, which is calculated simply from comparing the lengths of loop regions. In this study, we focused on 87 prokaryotic TM proteomes consisting of 31 proteobacteria, 22 gram-positive bacteria, 19 other bacteria, and 15 archaea. Prior to performing the clustering, we first categorized individual TM protein sequences as "known," "putative" (similar to "known" sequences), or "unknown" by using the homology search and the sequence similarity comparison against SWISS-PROT to assess the current status of the functional annotation of the TM proteomes based on sequence similarity only. More than three-quarters, that is, 75.7% of the TM protein sequences are functionally "unknown," with only 3.8% and 20.5% of them being classified as "known" and "putative," respectively. Using our clustering approach based on TM topology similarity, we succeeded in increasing the rate of TM protein sequences functionally classified and identified from 24.3% to 60.9%. Obtained clusters correspond well to functional superfamilies or families, and the functional classification and identification are successfully achieved by this approach. For example, in an obtained cluster of TM proteins with six TM segments, 109 sequences out of 119 sequences annotated as "ATP-binding cassette transporter" are properly included and 122 "unknown" sequences are also contained.     

Information and redundancy in the burial folding code of globular proteins within a wide range of shapes and sizes

Recent ab initio folding simulations for a limited number of small proteins have corroborated a previous suggestion that atomic burial information obtainable from sequence could be sufficient for tertiary structure determination when combined to sequence‐independent geometrical constraints. Here, we use simulations parameterized by native burials to investigate the required amount of information in a diverse set of globular proteins comprising different structural classes and a wide size range. Burial information is provided by a potential term pushing each atom towards one among a small number L of equiprobable concentric layers. An upper bound for the required information is provided by the minimal number of layers L^min still compatible with correct folding behavior. We obtain L^min between 3 and 5 for seven small to medium proteins with 50 ≤ N_r ≤ 110 residues while for a larger protein with N_r = 141 we find that L ≥ 6 is required to maintain native stability. We additionally estimate the usable redundancy for a given L ≥ L^min from the burial entropy associated to the largest folding‐compatible fraction of “superfluous” atoms, for which the burial term can be turned off or target layers can be chosen randomly. The estimated redundancy for small proteins with L = 4 is close to 0.8. Our results are consistent with the above‐average quality of burial predictions used in previous simulations and indicate that the fraction of approachable proteins could increase significantly with even a mild, plausible, improvement on sequence‐dependent burial prediction or on sequence‐independent constraints that augment the detectable redundancy during simulations. Proteins 2016; 84:515–531. © 2016 Wiley Periodicals, Inc.     

HMMpTM: Improving transmembrane protein topology prediction using phosphorylation and glycosylation site prediction

During the last two decades a large number of computational methods have been developed for predicting transmembrane protein topology. Current predictors rely on topogenic signals in the protein sequence, such as the distribution of positively charged residues in extra-membrane loops and the existence of N-terminal signals. However, phosphorylation and glycosylation are post-translational modifications (PTMs) that occur in a compartment-specific manner and therefore the presence of a phosphorylation or glycosylation site in a transmembrane protein provides topological information. We examine the combination of phosphorylation and glycosylation site prediction with transmembrane protein topology prediction. We report the development of a Hidden Markov Model based method, capable of predicting the topology of transmembrane proteins and the existence of kinase specific phosphorylation and N/O-linked glycosylation sites along the protein sequence. Our method integrates a novel feature in transmembrane protein topology prediction, which results in improved performance for topology prediction and reliable prediction of phosphorylation and glycosylation sites. The method is freely available at http://bioinformatics.biol.uoa.gr/HMMpTM.     

Membrane topology of human ABC proteins

In this review, we summarize the currently available information on the membrane topology of some key members of the human ABC protein subfamilies, and present the predicted domain arrangements. In the lack of high-resolution structures for eukaryotic ABC transporters this topology is based only on prediction algorithms and biochemical data for the location of various segments of the polypeptide chain, relative to the membrane. We suggest that topology models generated by the available prediction methods should only be used as guidelines to provide a basis of experimental strategies for the elucidation of the membrane topology.     

HTP: a neural network-based method for predicting the topology of helical transmembrane domains in proteins

In this paper we describe a microcomputer program (HTP) forpredicting the location and orientation of -helical transmemhranesegments in integral membrane proteins. HTP is a neural network-basedtool which gives as output the protein membrane topology basedon the statistical propensity of residues to be located in externaland internal loops. This method, which uses single protein sequencesas input to the network system, correctly predicts the topologyof 71 out of 92 membrane proteins of putative membrane orientation,independently of the protein source.     

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.     

Prediction of the burial status of transmembrane residues of helical membrane proteins

Background  

Helical membrane proteins (HMPs) play a crucial role in diverse cellular processes, yet it still remains extremely difficult to determine their structures by experimental techniques. Given this situation, it is highly desirable to develop sequence-based computational methods for predicting structural characteristics of HMPs.     

Best alpha-helical transmembrane protein topology predictions are achieved using hidden Markov models and evolutionary information

Methods that predict the topology of helical membrane proteins are standard tools when analyzing any proteome. Therefore, it is important to improve the performance of such methods. Here we introduce a novel method, PRODIV-TMHMM, which is a profile-based hidden Markov model (HMM) that also incorporates the best features of earlier HMM methods. In our tests, PRODIV-TMHMM outperforms earlier methods both when evaluated on "low-resolution" topology data and on high-resolution 3D structures. The results presented here indicate that the topology could be correctly predicted for approximately two-thirds of all membrane proteins using PRODIV-TMHMM. The importance of evolutionary information for topology prediction is emphasized by the fact that compared with using single sequences, the performance of PRODIV-TMHMM (as well as two other methods) is increased by approximately 10 percentage units by the use of homologous sequences. On a more general level, we also show that HMM-based (or similar) methods perform superiorly to methods that focus mainly on identification of the membrane regions.     

Hydrophobicity of transmembrane proteins: spatially profiling the distribution

A hallmark of soluble globular protein tertiary structure is a hydrophobic core and a protein exterior populated predominantly by hydrophilic residues. Recent hydrophobic moment profiling of the spatial distribution of 30 globular proteins of diverse size and structure had revealed features of this distribution that were comparable. Analogous profiling of the hydrophobicity distribution of the alpha-helical buried bundles of several transmembrane proteins, as the lipid/protein interface is approached from within the bilayer, reveals spatial hydrophobicity profiles that contrast with those obtained for the soluble proteins. The calculations, which enable relative changes of hydrophobicity to be simply identified over the entire spatial extent of the multimer within the lipid bilayer, show the accumulated zero-order moments of the bundles to be mainly inverted with respect to that found for the soluble proteins. This indicates a statistical increase in the average residue hydrophobic content as the lipid bilayer is approached. This result differs from that of a relatively recent calculation and qualitatively agrees with earlier calculations involving lipid exposed and buried residues of the alpha-helices of transmembrane proteins. Spatial profiling, over the entire spatial extent of the multimer with scaled values of residue hydrophobicity, provides information that is not available from calculations using lipid exposure alone.     

Hidden markov model for the prediction of transmembrane proteins using MATLAB

Since membranous proteins play a key role in drug targeting therefore transmembrane proteins prediction is active and challenging area of biological sciences. Location based prediction of transmembrane proteins are significant for functional annotation of protein sequences. Hidden markov model based method was widely applied for transmembrane topology prediction. Here we have presented a revised and a better understanding model than an existing one for transmembrane protein prediction. Scripting on MATLAB was built and compiled for parameter estimation of model and applied this model on amino acid sequence to know the transmembrane and its adjacent locations. Estimated model of transmembrane topology was based on TMHMM model architecture. Only 7 super states are defined in the given dataset, which were converted to 96 states on the basis of their length in sequence. Accuracy of the prediction of model was observed about 74 %, is a good enough in the area of transmembrane topology prediction. Therefore we have concluded the hidden markov model plays crucial role in transmembrane helices prediction on MATLAB platform and it could also be useful for drug discovery strategy. AVAILABILITY: The database is available for free at bioinfonavneet@gmail.comvinaysingh@bhu.ac.in.     

Observations concerning topology and locations of helix ends of membrane proteins of known structure

Summary Hydropathy plots of amino acid sequences reveal the approximate locations of the transbilayer helices of membrane proteins of known structure and are thus used to predict the helices of proteins of unknown structure. Because the threedimensional structures of membrane proteins are difficult to obtain, it is important to be able to extract as much information as possible from hydropathy plots. We describe an augmented hydropathy plot analysis of the three membrane proteins of known structure, which should be useful for the systematic examination and comparison of membrane proteins of unknown structure. The sliding-window analysis utilizes the floating interfacial hydrophobicity scale [IFH(h)] of Jacobs and White (Jacobs, R.E., White, S.H., 1989.Biochemistry 28:3421–3437) and the reverse-turn (RT) frequencies of Levitt (Levitt, M., 1977,Biochemistry 17:4277–4285). The IFH(h) scale allows one to examine the consequences of different assumptions about the average hydrogen bond status (h=0 to 1) of polar side chains. Hydrophobicity plots of the three proteins show that (i) the intracellular helix-connecting links and chain ends can be distinguished from the extracellular ones and (ii) the main peaks of hydrophobicity are bounded by minor ones which bracket the helix ends. RT frequency plots show that (iii) the centers of helices are usually very close to wide-window minima of average RT frequency and (iv) helices are always bounded by narrowwindow maxima of average RT frequency. The analysis suggests that side-chain hydrogen bonding with membrane components during folding may play a key role in insertion.     

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/.     

Experimentally based topology models for E. coli inner membrane proteins

Membrane protein topology predictions can be markedly improved by the inclusion of even very limited experimental information. We have recently introduced an approach for the production of reliable topology models based on a combination of experimental determination of the location (cytoplasmic or periplasmic) of a protein's C terminus and topology prediction. Here, we show that determination of the location of a protein's C terminus, rather than some internal loop, is the best strategy for large-scale topology mapping studies. We further report experimentally based topology models for 31 Escherichia coli inner membrane proteins, using methodology suitable for genome-scale studies.     

MPEx: A tool for exploring membrane proteins

Hydropathy plot methods form a cornerstone of membrane protein research, especially in the early stages of biochemical and structural characterization. Membrane Protein Explorer (MPEx), described in this article, is a refined and versatile hydropathy‐plot software tool for analyzing membrane protein sequences. MPEx is highly interactive and facilitates the characterization and identification of favorable protein transmembrane regions using experiment‐based physical and biological hydrophobicity scales. Besides allowing the consequences of sequence mutations to be examined, it provides tools for aiding the design of membrane‐active peptides. MPEx is freely available as a Java Web Start application from our web site at http://blanco.biomol.uci.edu/mpex .