Transmembrane helix predictions revisited

Unique Interactions of the Small Translocases of the Mitochondrial Inner Membrane (Tims) in Trypanosoma brucei

The infectious agent for African trypanosomiasis, Trypanosoma brucei, possesses a unique and essential translocase of the mitochondrial inner membrane, known as the TbTIM17 complex. TbTim17 associates with six small TbTims (TbTim9, TbTim10, TbTim11, TbTim12, TbTim13, and TbTim8/13). However, the interaction patterns of these smaller TbTims with each other and TbTim17 are not clear. Through yeast two-hybrid (Y2H) and co-immunoprecipitation analyses, we demonstrate that all six small TbTims interact with each other. Stronger interactions were found among TbTim8/13, TbTim9, and TbTim10. However, TbTim10 shows weaker associations with TbTim13, which has a stronger connection with TbTim17. Each of the small TbTims also interacts strongly with the C-terminal region of TbTim17. RNAi studies indicated that among all small TbTims, TbTim13 is most crucial for maintaining the steady-state levels of the TbTIM17 complex. Further analysis of the small TbTim complexes by size exclusion chromatography revealed that each small TbTim, except for TbTim13, is present in ~70 kDa complexes, possibly existing in heterohexameric forms. In contrast, TbTim13 is primarily present in the larger complex (>800 kDa) and co-fractionates with TbTim17. Altogether, our results demonstrate that, relative to other eukaryotes, the architecture and function of the small TbTim complexes are specific to T. brucei.

Performance of Novel Antimicrobial Protein Bg_9562 and In Silico Predictions on Its Properties with Reference to Its Antimicrobial Efficiency against Rhizoctonia solani

Bg_9562 is a potential broad-spectrum antifungal effector protein derived from the bacteria Burkholderia gladioli strain NGJ1 and is effective against Rhizoctonia solani, the causal agent of sheath blight in rice. In the present study, in vitro antifungal assays showed that Bg_9562 was efficient at 35 °C and 45 °C and ineffective either at high acidic pH (3.0) or alkaline pH (9.5) conditions. Compatibility studies between the native bioagents Trichoderma asperellum TAIK1 and Bacillus subtilis BIK3 indicated that Bg_9562 was compatible with the bioagents. A field study using foliar spray of the Bg_9562 protein indicated the need of formulating the protein before its application. In silico analysis predicted that Bg_9562 possess 111 amino acid residues (46 hydrophobic residues, 12 positive and 8 negative residues) with the high aliphatic index of 89.92, attributing to its thermostability with a half-life of 30 h. Bg_9562 (C491H813N137O166S5) possessed a protein binding potential of 1.27 kcal/mol with a better possibility of interacting and perturbing the membrane, the main target for antimicrobial proteins. The secondary structure revealed the predominance of random coils in its structure, and the best 3D model of Bg_9562 was predicted using an ab initio method with Robetta and AlphaFold 2. The predicted binding ligands were nucleic acids and zinc with confidence scores of 0.07 and 0.05, respectively. The N-terminal region (1-14 residues) and C-terminal region (101 to 111) of Bg_9562 residues were predicted to be disordered regions. Stability and binding properties of the protein from the above studies would help to encapsulate Bg_9562 using a suitable carrier to maintain efficiency and improve delivery against Rhizoctonia solani in the most challenging rice ecosphere.

Integrative Genomics Sheds Light on Evolutionary Forces Shaping the Acidithiobacillia Class Acidophilic Lifestyle

Extreme acidophiles thrive in environments rich in protons (pH values <3) and often high levels of dissolved heavy metals. They are distributed across the three domains of the Tree of Life including members of the Proteobacteria. The Acidithiobacillia class is formed by the neutrophilic genus Thermithiobacillus along with the extremely acidophilic genera Fervidacidithiobacillus, Igneacidithiobacillus, Ambacidithiobacillus, and Acidithiobacillus. Phylogenomic reconstruction revealed a division in the Acidithiobacillia class correlating with the different pH optima that suggested that the acidophilic genera evolved from an ancestral neutrophile within the Acidithiobacillia. Genes and mechanisms denominated as "first line of defense" were key to explaining the Acidithiobacillia acidophilic lifestyle including preventing proton influx that allows the cell to maintain a near-neutral cytoplasmic pH and differ from the neutrophilic Acidithiobacillia ancestors that lacked these systems. Additional differences between the neutrophilic and acidophilic Acidithiobacillia included the higher number of gene copies in the acidophilic genera coding for "second line of defense" systems that neutralize and/or expel protons from cell. Gain of genes such as hopanoid biosynthesis involved in membrane stabilization at low pH and the functional redundancy for generating an internal positive membrane potential revealed the transition from neutrophilic properties to a new acidophilic lifestyle by shaping the Acidithiobacillaceae genomic structure. The presence of a pool of accessory genes with functional redundancy provides the opportunity to "hedge bet" in rapidly changing acidic environments. Although a core of mechanisms for acid resistance was inherited vertically from an inferred neutrophilic ancestor, the majority of mechanisms, especially those potentially involved in resistance to extremely low pH, were obtained from other extreme acidophiles by horizontal gene transfer (HGT) events.

Elucidation of ligand binding and dimerization of NADPH:protochlorophyllide (Pchlide) oxidoreductase from pea (Pisum sativum L.) by structural analysis and simulations

NADPH:protochlorophyllide (Pchlide) oxidoreductase (POR) is a key enzyme of chlorophyll biosynthesis in angiosperms. It is one of few known photoenzymes, which catalyzes the light-activated trans-reduction of the C17-C18 double bond of Pchlide's porphyrin ring. Due to the light requirement, dark-grown angiosperms cannot synthesize chlorophyll. No crystal structure of POR is available, so to improve understanding of the protein's three-dimensional structure, its dimerization, and binding of ligands (both the cofactor NADPH and substrate Pchlide), we computationally investigated the sequence and structural relationships among homologous proteins identified through database searches. The results indicate that α4 and α7 helices of monomers form the interface of POR dimers. On the basis of conserved residues, we predicted 11 functionally important amino acids that play important roles in POR binding to NADPH. Structural comparison of available crystal structures revealed that they participate in formation of binding pockets that accommodate the Pchlide ligand, and that five atoms of the closed tetrapyrrole are involved in non-bonding interactions. However, we detected no clear pattern in the physico-chemical characteristics of the amino acids they interact with. Thus, we hypothesize that interactions of these atoms in the Pchlide porphyrin ring are important to hold the ligand within the POR binding site. Analysis of Pchlide binding in POR by molecular docking and PELE simulations revealed that the orientation of the nicotinamide group is important for Pchlide binding. These findings highlight the complexity of interactions of porphyrin-containing ligands with proteins, and we suggest that fit-inducing processes play important roles in POR-Pchlide interactions.

TMPSS: A Deep Learning-Based Predictor for Secondary Structure and Topology Structure Prediction of Alpha-Helical Transmembrane Proteins

Alpha transmembrane proteins (αTMPs) profoundly affect many critical biological processes and are major drug targets due to their pivotal protein functions. At present, even though the non-transmembrane secondary structures are highly relevant to the biological functions of αTMPs along with their transmembrane structures, they have not been unified to be studied yet. In this study, we present a novel computational method, TMPSS, to predict the secondary structures in non-transmembrane parts and the topology structures in transmembrane parts of αTMPs. TMPSS applied a Convolutional Neural Network (CNN), combined with an attention-enhanced Bidirectional Long Short-Term Memory (BiLSTM) network, to extract the local contexts and long-distance interdependencies from primary sequences. In addition, a multi-task learning strategy was used to predict the secondary structures and the transmembrane helixes. TMPSS was thoroughly trained and tested against a non-redundant independent dataset, where the Q3 secondary structure prediction accuracy achieved 78% in the non-transmembrane region, and the accuracy of the transmembrane region prediction achieved 90%. In sum, our method showcased a unified model for predicting the secondary structure and topology structure of αTMPs by only utilizing features generated from primary sequences and provided a steady and fast prediction, which promisingly improves the structural studies on αTMPs.

De novo emergence of adaptive membrane proteins from thymine-rich genomic sequences

Recent evidence demonstrates that novel protein-coding genes can arise de novo from non-genic loci. This evolutionary innovation is thought to be facilitated by the pervasive translation of non-genic transcripts, which exposes a reservoir of variable polypeptides to natural selection. Here, we systematically characterize how these de novo emerging coding sequences impact fitness in budding yeast. Disruption of emerging sequences is generally inconsequential for fitness in the laboratory and in natural populations. Overexpression of emerging sequences, however, is enriched in adaptive fitness effects compared to overexpression of established genes. We find that adaptive emerging sequences tend to encode putative transmembrane domains, and that thymine-rich intergenic regions harbor a widespread potential to produce transmembrane domains. These findings, together with in-depth examination of the de novo emerging YBR196C-A locus, suggest a novel evolutionary model whereby adaptive transmembrane polypeptides emerge de novo from thymine-rich non-genic regions and subsequently accumulate changes molded by natural selection.

There is increasing evidence that protein-coding genes can emerge de novo from noncoding genomic regions. Vakirlis et al. propose that sequences encoding transmembrane polypeptides can emerge de novo in thymine-rich genomic regions and provide organisms with fitness benefits.

Correcting mistakes in predicting distributions

Motivation

Many applications monitor predictions of a whole range of features for biological datasets, e.g. the fraction of secreted human proteins in the human proteome. Results and error estimates are typically derived from publications.

Results

Here, we present a simple, alternative approximation that uses performance estimates of methods to error-correct the predicted distributions. This approximation uses the confusion matrix (TP true positives, TN true negatives, FP false positives and FN false negatives) describing the performance of the prediction tool for correction. As proof-of-principle, the correction was applied to a two-class (membrane/not) and to a seven-class (localization) prediction.

Availability and implementation

Datasets and a simple JavaScript tool available freely for all users at http://www.rostlab.org/services/distributions.

Supplementary information

Supplementary data are available at Bioinformatics online.

Topology prediction of insect olfactory receptors

Olfactory receptors are important transmembrane proteins that enable organisms to perceive odours and react to them. Structural understanding of insect olfactory receptors is scarce. In this review, we discuss different transmembrane helix prediction methods, consensus methods, topology prediction methods which can enable topology prediction of these proteins. We discuss the current success rates by applying the algorithms on few G-protein coupled receptors of known structure and olfactory receptor sequences and outstanding challenges. Finally, we discuss the impact of topology prediction on biology and modeling of ORs.

Residues 529 to 549 participate in membrane penetration and pore-forming activity of the Bordetella adenylate cyclase toxin

The adenylate cyclase toxin-hemolysin (CyaA, ACT or AC-Hly) of pathogenic Bordetellae delivers its adenylyl cyclase (AC) enzyme domain into the cytosol of host cells and catalyzes uncontrolled conversion of cellular ATP to cAMP. In parallel, the toxin forms small cation-selective pores that permeabilize target cell membrane and account for the hemolytic activity of CyaA on erythrocytes. The pore-forming domain of CyaA is predicted to consist of five transmembrane α-helices, of which the helices I, III, IV and V have previously been characterized. We examined here the α-helix II that is predicted to form between residues 529 to 549. Substitution of the glycine 531 residue by a proline selectively reduced the hemolytic capacity but did not affect the AC translocating activity of the CyaA-G531P toxin. In contrast, CyaA toxins with alanine 538 or 546 replaced by diverse residues were selectively impaired in the capacity to translocate the AC domain across cell membrane but remained fully hemolytic. Such toxins, however, formed pores in planar asolectin bilayer membranes with a very low frequency and with at least two different conducting states. The helix-breaking substitution of alanine 538 by a proline residue abolished the voltage-activated increase of membrane activity of CyaA in asolectin bilayers. These results reveal that the predicted α-helix comprising the residues 529 to 549 plays a key role in CyaA penetration into the target plasma membrane and pore-forming activity of the toxin.

Discrimination of Native-like States of Membrane Proteins with Implicit Membrane-based Scoring Functions

A scoring protocol based on implicit membrane-based scoring functions and a new protocol for optimizing the positioning of proteins inside the membrane was evaluated for its capacity to discriminate native-like states from misfolded decoys. A decoy set previously established by the Baker lab (Proteins: Struct., Funct., Genet. 2006, 62, 1010-1025) was used along with a second set that was generated to cover higher resolution models. The Implicit Membrane Model 1 (IMM1), IMM1 model with CHARMM 36 parameters (IMM1-p36), generalized Born with simple switching (GBSW), and heterogeneous dielectric generalized Born versions 2 (HDGBv2) and 3 (HDGBv3) were tested along with the new HDGB van der Waals (HDGBvdW) model that adds implicit van der Waals contributions to the solvation free energy. For comparison, scores were also calculated with the distance-scaled finite ideal-gas reference (DFIRE) scoring function. Z-scores for native state discrimination, energy vs root-mean-square deviation (RMSD) correlations, and the ability to select the most native-like structures as top-scoring decoys were evaluated to assess the performance of the scoring functions. Ranking of the decoys in the Baker set that were relatively far from the native state was challenging and dominated largely by packing interactions that were captured best by DFIRE with less benefit of the implicit membrane-based models. Accounting for the membrane environment was much more important in the second decoy set where especially the HDGB-based scoring functions performed very well in ranking decoys and providing significant correlations between scores and RMSD, which shows promise for improving membrane protein structure prediction and refinement applications. The new membrane structure scoring protocol was implemented in the MEMScore web server ( http://feiglab.org/memscore ).

Comprehensive analysis of the numbers, lengths and amino acid compositions of transmembrane helices in prokaryotic, eukaryotic and viral integral membrane proteins of high-resolution structure

We report a comprehensive analysis of the numbers, lengths and amino acid compositions of transmembrane helices in 235 high-resolution structures of integral membrane proteins. The properties of 1551 transmembrane helices in the structures were compared with those obtained by analysis of the same amino acid sequences using topology prediction tools. Explanations for the 81 (5.2%) missing or additional transmembrane helices in the prediction results were identified. Main reasons for missing transmembrane helices were mis-identification of N-terminal signal peptides, breaks in α-helix conformation or charged residues in the middle of transmembrane helices and transmembrane helices with unusual amino acid composition. The main reason for additional transmembrane helices was mis-identification of amphipathic helices, extramembrane helices or hairpin re-entrant loops. Transmembrane helix length had an overall median of 24 residues and an average of 24.9 ± 7.0 residues and the most common length was 23 residues. The overall content of residues in transmembrane helices as a percentage of the full proteins had a median of 56.8% and an average of 55.7 ± 16.0%. Amino acid composition was analysed for the full proteins, transmembrane helices and extramembrane regions. Individual proteins or types of proteins with transmembrane helices containing extremes in contents of individual amino acids or combinations of amino acids with similar physicochemical properties were identified and linked to structure and/or function. In addition to overall median and average values, all results were analysed for proteins originating from different types of organism (prokaryotic, eukaryotic, viral) and for subgroups of receptors, channels, transporters and others.

Computational Approaches for Revealing the Structure of Membrane Transporters: Case Study on Bilitranslocase

The structural and functional details of transmembrane proteins are vastly underexplored, mostly due to experimental difficulties regarding their solubility and stability. Currently, the majority of transmembrane protein structures are still unknown and this present a huge experimental and computational challenge. Nowadays, thanks to X-ray crystallography or NMR spectroscopy over 3000 structures of membrane proteins have been solved, among them only a few hundred unique ones. Due to the vast biological and pharmaceutical interest in the elucidation of the structure and the functional mechanisms of transmembrane proteins, several computational methods have been developed to overcome the experimental gap. If combined with experimental data the computational information enables rapid, low cost and successful predictions of the molecular structure of unsolved proteins. The reliability of the predictions depends on the availability and accuracy of experimental data associated with structural information. In this review, the following methods are proposed for in silico structure elucidation: sequence-dependent predictions of transmembrane regions, predictions of transmembrane helix–helix interactions, helix arrangements in membrane models, and testing their stability with molecular dynamics simulations. We also demonstrate the usage of the computational methods listed above by proposing a model for the molecular structure of the transmembrane protein bilitranslocase. Bilitranslocase is bilirubin membrane transporter, which shares similar tissue distribution and functional properties with some of the members of the Organic Anion Transporter family and is the only member classified in the Bilirubin Transporter Family. Regarding its unique properties, bilitranslocase is a potentially interesting drug target.

Prediction of Structural and Functional Aspects of Protein

To predict the structure of protein from a primary amino acid sequence is computationally difficult. An investigation of the methods and algorithms used to predict protein structure and a thorough knowledge of the function and structure of proteins are critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To that end, this chapter sheds light on the methods used for protein structure prediction. This chapter covers the applications of modeled protein structures and unravels the relationship between pure sequence information and three-dimensional structure, which continues to be one of the greatest challenges in molecular biology. With this resource, it presents an all-encompassing examination of the problems, methods, tools, servers, databases, and applications of protein structure prediction, giving unique insight into the future applications of the modeled protein structures. In this chapter, current protein structure prediction methods are reviewed for a milieu on structure prediction, the prediction of structural fundamentals, tertiary structure prediction, and functional imminent. The basic ideas and advances of these directions are discussed in detail.

TMSEG: Novel prediction of transmembrane helices

Transmembrane proteins (TMPs) are important drug targets because they are essential for signaling, regulation, and transport. Despite important breakthroughs, experimental structure determination remains challenging for TMPs. Various methods have bridged the gap by predicting transmembrane helices (TMHs), but room for improvement remains. Here, we present TMSEG, a novel method identifying TMPs and accurately predicting their TMHs and their topology. The method combines machine learning with empirical filters. Testing it on a non-redundant dataset of 41 TMPs and 285 soluble proteins, and applying strict performance measures, TMSEG outperformed the state-of-the-art in our hands. TMSEG correctly distinguished helical TMPs from other proteins with a sensitivity of 98 ± 2% and a false positive rate as low as 3 ± 1%. Individual TMHs were predicted with a precision of 87 ± 3% and recall of 84 ± 3%. Furthermore, in 63 ± 6% of helical TMPs the placement of all TMHs and their inside/outside topology was correctly predicted. There are two main features that distinguish TMSEG from other methods. First, the errors in finding all helical TMPs in an organism are significantly reduced. For example, in human this leads to 200 and 1600 fewer misclassifications compared to the second and third best method available, and 4400 fewer mistakes than by a simple hydrophobicity-based method. Second, TMSEG provides an add-on improvement for any existing method to benefit from. Proteins 2016; 84:1706-1716. © 2016 Wiley Periodicals, Inc.

50 years of amino acid hydrophobicity scales: revisiting the capacity for peptide classification

BACKGROUND

Physicochemical properties are frequently analyzed to characterize protein-sequences of known and unknown function. Especially the hydrophobicity of amino acids is often used for structural prediction or for the detection of membrane associated or embedded β-sheets and α-helices. For this purpose many scales classifying amino acids according to their physicochemical properties have been defined over the past decades. In parallel, several hydrophobicity parameters have been defined for calculation of peptide properties. We analyzed the performance of separating sequence pools using 98 hydrophobicity scales and five different hydrophobicity parameters, namely the overall hydrophobicity, the hydrophobic moment for detection of the α-helical and β-sheet membrane segments, the alternating hydrophobicity and the exact ß-strand score.

RESULTS

Most of the scales are capable of discriminating between transmembrane α-helices and transmembrane β-sheets, but assignment of peptides to pools of soluble peptides of different secondary structures is not achieved at the same quality. The separation capacity as measure of the discrimination between different structural elements is best by using the five different hydrophobicity parameters, but addition of the alternating hydrophobicity does not provide a large benefit. An in silico evolutionary approach shows that scales have limitation in separation capacity with a maximal threshold of 0.6 in general. We observed that scales derived from the evolutionary approach performed best in separating the different peptide pools when values for arginine and tyrosine were largely distinct from the value of glutamate. Finally, the separation of secondary structure pools via hydrophobicity can be supported by specific detectable patterns of four amino acids.

CONCLUSION

It could be assumed that the quality of separation capacity of a certain scale depends on the spacing of the hydrophobicity value of certain amino acids. Irrespective of the wealth of hydrophobicity scales a scale separating all different kinds of secondary structures or between soluble and transmembrane peptides does not exist reflecting that properties other than hydrophobicity affect secondary structure formation as well. Nevertheless, application of hydrophobicity scales allows distinguishing between peptides with transmembrane α-helices and β-sheets. Furthermore, the overall separation capacity score of 0.6 using different hydrophobicity parameters could be assisted by pattern search on the protein sequence level for specific peptides with a length of four amino acids.

PSOFuzzySVM-TMH: identification of transmembrane helix segments using ensemble feature space by incorporated fuzzy support vector machine

Membrane protein is a central component of the cell that manages intra and extracellular processes. Membrane proteins execute a diversity of functions that are vital for the survival of organisms. The topology of transmembrane proteins describes the number of transmembrane (TM) helix segments and its orientation. However, owing to the lack of its recognized structures, the identification of TM helix and its topology through experimental methods is laborious with low throughput. In order to identify TM helix segments reliably, accurately, and effectively from topogenic sequences, we propose the PSOFuzzySVM-TMH model. In this model, evolutionary based information position specific scoring matrix and discrete based information 6-letter exchange group are used to formulate transmembrane protein sequences. The noisy and extraneous attributes are eradicated using an optimization selection technique, particle swarm optimization, from both feature spaces. Finally, the selected feature spaces are combined in order to form ensemble feature space. Fuzzy-support vector Machine is utilized as a classification algorithm. Two benchmark datasets, including low and high resolution datasets, are used. At various levels, the performance of the PSOFuzzySVM-TMH model is assessed through 10-fold cross validation test. The empirical results reveal that the proposed framework PSOFuzzySVM-TMH outperforms in terms of classification performance in the examined datasets. It is ascertained that the proposed model might be a useful and high throughput tool for academia and research community for further structure and functional studies on transmembrane proteins.

A census of α-helical membrane proteins in double-stranded DNA viruses infecting bacteria and archaea

Background

Viruses are the most abundant and genetically diverse biological entities on earth, yet the repertoire of viral proteins remains poorly explored. As the number of sequenced virus genomes grows into the thousands, and the number of viral proteins into the hundreds of thousands, we report a systematic computational analysis of the point of first-contact between viruses and their hosts, namely viral transmembrane (TM) proteins.

Results

The complement of α-helical TM proteins in double-stranded DNA viruses infecting bacteria and archaea reveals large-scale trends that differ from those of their hosts. Viruses typically encode a substantially lower fraction of TM proteins than archaea or bacteria, with the notable exception of viruses with virions containing a lipid component such as a lipid envelope, internal lipid core, or inner membrane vesicle. Compared to bacteriophages, archaeal viruses are substantially enriched in membrane proteins. However, this feature is not always stable throughout the evolution of a viral lineage; for example, TM proteins are not part of the common heritage shared between Lipothrixviridae and Rudiviridae. In contrast to bacteria and archaea, viruses almost completely lack proteins with complicated membrane topologies composed of more than 4 TM segments, with the few detected exceptions being obvious cases of relatively recent horizontal transfer from the host.

Conclusions

The dramatic differences between the membrane proteomes of cells and viruses stem from the fact that viruses do not depend on essential membranes for energy transformation, ion homeostasis, nutrient transport and signaling.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0817-4) contains supplementary material, which is available to authorized users.

Metazoans evolved by taking domains from soluble proteins to expand intercellular communication network

A central question in animal evolution is how multicellular animals evolved from unicellular ancestors. We hypothesize that membrane proteins must be key players in the development of multicellularity because they are well positioned to form the cell-cell contacts and to provide the intercellular communication required for the creation of complex organisms. Here we find that a major mechanism for the necessary increase in membrane protein complexity in the transition from non-metazoan to metazoan life was the new incorporation of domains from soluble proteins. The membrane proteins that have incorporated soluble domains in metazoans are enriched in many of the functions unique to multicellular organisms such as cell-cell adhesion, signaling, immune defense and developmental processes. They also show enhanced protein-protein interaction (PPI) network complexity and centrality, suggesting an important role in the cellular diversification found in complex organisms. Our results expose an evolutionary mechanism that contributed to the development of higher life forms.

In silico characterization of binding mode of CCR8 inhibitor: homology modeling, docking and membrane based MD simulation study

Human CC-chemokine receptor 8 (CCR8) is a crucial drug target in asthma that belongs to G-protein-coupled receptor superfamily, which is characterized by seven transmembrane helices. To date, there is no X-ray crystal structure available for CCR8; this hampers active research on the target. Molecular basis of interaction mechanism of antagonist with CCR8 remains unclear. In order to provide binding site information and stable binding mode, we performed modeling, docking and molecular dynamics (MD) simulation of CCR8. Docking study of biaryl-ether-piperidine derivative (13C) was performed inside predefined CCR8 binding site to get the representative conformation of 13C. Further, MD simulations of receptor and complex (13C-CCR8) inside dipalmitoylphosphatidylcholine lipid bilayers were performed to explore the effect of lipids. Results analyses showed that the Gln91, Tyr94, Cys106, Val109, Tyr113, Cys183, Tyr184, Ser185, Lys195, Thr198, Asn199, Met202, Phe254, and Glu286 were conserved in both docking and MD simulations. This indicated possible role of these residues in CCR8 antagonism. However, experimental mutational studies on these identified residues could be effective to confirm their importance in CCR8 antagonism. Furthermore, calculated Coulombic interactions represented the crucial roles of Glu286, Lys195, and Tyr113 in CCR8 antagonism. Important residues identified in this study overlap with the previous non-peptide agonist (LMD-009) binding site. Though, the non-peptide agonist and currently studied inhibitor (13C) share common substructure, but they differ in their effects on CCR8. So, to get more insight into their agonist and antagonist effects, further side-by-side experimental studies on both agonist (LMD-009) and antagonist (13C) are suggested.

Evaluation of transmembrane helix predictions in 2014

Experimental structure determination continues to be challenging for membrane proteins. Computational prediction methods are therefore needed and widely used to supplement experimental data. Here, we re-examined the state of the art in transmembrane helix prediction based on a nonredundant dataset with 190 high-resolution structures. Analyzing 12 widely-used and well-known methods using a stringent performance measure, we largely confirmed the expected high level of performance. On the other hand, all methods performed worse for proteins that could not have been used for development. A few results stood out: First, all methods predicted proteins in eukaryotes better than those in bacteria. Second, methods worked less well for proteins with many transmembrane helices. Third, most methods correctly discriminated between soluble and transmembrane proteins. However, several older methods often mistook signal peptides for transmembrane helices. Some newer methods have overcome this shortcoming. In our hands, PolyPhobius and MEMSAT-SVM outperformed other methods.

DOR - a Database of Olfactory Receptors - Integrated Repository for Sequence and Secondary Structural Information of Olfactory Receptors in Selected Eukaryotic Genomes

Olfaction is the response to odors and is mediated by a class of membrane-bound proteins called olfactory receptors (ORs). An understanding of these receptors serves as a good model for basic signal transduction mechanisms and also provides important clues for the strategies adopted by organisms for their ultimate survival using chemosensory perception in search of food or defense against predators. Prior research on cross-genome phylogenetic analyses from our group motivated the addressal of conserved evolutionary trends, clustering, and ortholog prediction of ORs. The database of olfactory receptors (DOR) is a repository that provides sequence and structural information on ORs of selected organisms (such as Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, and Homo sapiens). Users can download OR sequences, study predicted membrane topology, and obtain cross-genome sequence alignments and phylogeny, including three-dimensional (3D) structural models of 100 selected ORs and their predicted dimer interfaces. The database can be accessed from http://caps.ncbs.res.in/DOR. Such a database should be helpful in designing experiments on point mutations to probe into the possible dimerization modes of ORs and to even understand the evolutionary changes between different receptors.

Why is the biological hydrophobicity scale more accurate than earlier experimental hydrophobicity scales?

The recognition of transmembrane helices by the translocon is primarily guided by the average hydrophobicity of the potential transmembrane helix. However, the exact hydrophobicity of each amino acid can be identified in several different ways. The free energy of transfer for amino acid analogues between a hydrophobic media, for example, octanol and water can be measured or obtained from simulations, the hydrophobicity can also be estimated by statistical properties from known transmembrane segments and finally the contribution of each amino acid type for the probability of translocon recognition has recently been measured directly. Although these scales correlate quite well, there are clear differences between them and it is not well understood which scale represents neither the biology best nor what the differences are. Here, we try to provide some answers to this by studying the ability of different scales to recognize transmembrane helices and predict the topology of transmembrane proteins. From this analysis it is clear that the biological hydrophobicity scale as well scales created from statistical analysis of membrane helices perform better than earlier experimental scales that are mainly based on measurements of amino acid analogs and not directly on transmembrane helix recognition. Using these results we identified the properties of the scales that perform better than other scales. We find, for instance, that the better performing scales consider proline more hydrophilic. This shows that transmembrane recognition is not only governed by pure hydrophobicity but also by the helix preferences for amino acids, as proline is a strong helix breaker.

Green fluorescent protein (GFP)-based overexpression screening and characterization of AgrC, a Receptor protein of quorum sensing in Staphylococcus aureus

Staphylococcus aureus AgrC is an important component of the agr quorum-sensing system. AgrC is a membrane-embedded histidine kinase that is thought to act as a sensor for the recognition of environmental signals and the transduction of signals into the cytoplasm. However, the difficulty of expressing and purifying functional membrane proteins has drastically hindered in-depth understanding of the molecular structures and physiological functions of these proteins. Here, we describe the high-yield expression and purification of AgrC, and analyze its kinase activity. A C-terminal green fluorescent protein (GFP) fusion to AgrC served as a reporter for monitoring protein expression levels in real time. Protein expression levels were analyzed by the microscopic assessment of the whole-cell fluorescence. The expressed AgrC-GFP protein with a C-terminal His-tagged was purified using immobilized metal affinity chromatography (IMAC) and size exclusion chromatography (SEC) at yields of ≥10 mg/L, following optimization. We also assessed the effects of different detergents on membrane solubilization and AgrC kinase activity, and polyoxyethylene-(23)-lauryl-ether (Brij-35) was identified as the most suitable detergent. Furthermore, the secondary structural stability of purified AgrC was analyzed using circular dichroism (CD) spectroscopy. This study may serve as a general guide for improving the yields of other membrane protein preparations and selecting the appropriate detergent to stabilize membrane proteins for biophysical and biochemical analyses.

Transmembrane protein alignment and fold recognition based on predicted topology

BACKGROUND

Although Transmembrane Proteins (TMPs) are highly important in various biological processes and pharmaceutical developments, general prediction of TMP structures is still far from satisfactory. Because TMPs have significantly different physicochemical properties from soluble proteins, current protein structure prediction tools for soluble proteins may not work well for TMPs. With the increasing number of experimental TMP structures available, template-based methods have the potential to become broadly applicable for TMP structure prediction. However, the current fold recognition methods for TMPs are not as well developed as they are for soluble proteins.

METHODOLOGY

We developed a novel TMP Fold Recognition method, TMFR, to recognize TMP folds based on sequence-to-structure pairwise alignment. The method utilizes topology-based features in alignment together with sequence profile and solvent accessibility. It also incorporates a gap penalty that depends on predicted topology structure segments. Given the difference between α-helical transmembrane protein (αTMP) and β-strands transmembrane protein (βTMP), parameters of scoring functions are trained respectively for these two protein categories using 58 αTMPs and 17 βTMPs in a non-redundant training dataset.

RESULTS

We compared our method with HHalign, a leading alignment tool using a non-redundant testing dataset including 72 αTMPs and 30 βTMPs. Our method achieved 10% and 9% better accuracies than HHalign in αTMPs and βTMPs, respectively. The raw score generated by TMFR is negatively correlated with the structure similarity between the target and the template, which indicates its effectiveness for fold recognition. The result demonstrates TMFR provides an effective TMP-specific fold recognition and alignment method.

A benchmark server using high resolution protein structure data, and benchmark results for membrane helix predictions

Background

Helical membrane proteins are vital for the interaction of cells with their environment. Predicting the location of membrane helices in protein amino acid sequences provides substantial understanding of their structure and function and identifies membrane proteins in sequenced genomes. Currently there is no comprehensive benchmark tool for evaluating prediction methods, and there is no publication comparing all available prediction tools. Current benchmark literature is outdated, as recently determined membrane protein structures are not included. Current literature is also limited to global assessments, as specialised benchmarks for predicting specific classes of membrane proteins were not previously carried out.

Description

We present a benchmark server at http://sydney.edu.au/pharmacy/sbio/software/TMH_benchmark.shtml that uses recent high resolution protein structural data to provide a comprehensive assessment of the accuracy of existing membrane helix prediction methods. The server further allows a user to compare uploaded predictions generated by novel methods, permitting the comparison of these novel methods against all existing methods compared by the server. Benchmark metrics include sensitivity and specificity of predictions for membrane helix location and orientation, and many others. The server allows for customised evaluations such as assessing prediction method performances for specific helical membrane protein subtypes.

We report results for custom benchmarks which illustrate how the server may be used for specialised benchmarks. Which prediction method is the best performing method depends on which measure is being benchmarked. The OCTOPUS membrane helix prediction method is consistently one of the highest performing methods across all measures in the benchmarks that we performed.

Conclusions

The benchmark server allows general and specialised assessment of existing and novel membrane helix prediction methods. Users can employ this benchmark server to determine the most suitable method for the type of prediction the user needs to perform, be it general whole-genome annotation or the prediction of specific types of helical membrane protein. Creators of novel prediction methods can use this benchmark server to evaluate the performance of their new methods. The benchmark server will be a valuable tool for researchers seeking to extract more sophisticated information from the large and growing protein sequence databases.

WRF-TMH: predicting transmembrane helix by fusing composition index and physicochemical properties of amino acids

Membrane protein is the prime constituent of a cell, which performs a role of mediator between intra and extracellular processes. The prediction of transmembrane (TM) helix and its topology provides essential information regarding the function and structure of membrane proteins. However, prediction of TM helix and its topology is a challenging issue in bioinformatics and computational biology due to experimental complexities and lack of its established structures. Therefore, the location and orientation of TM helix segments are predicted from topogenic sequences. In this regard, we propose WRF-TMH model for effectively predicting TM helix segments. In this model, information is extracted from membrane protein sequences using compositional index and physicochemical properties. The redundant and irrelevant features are eliminated through singular value decomposition. The selected features provided by these feature extraction strategies are then fused to develop a hybrid model. Weighted random forest is adopted as a classification approach. We have used two benchmark datasets including low and high-resolution datasets. tenfold cross validation is employed to assess the performance of WRF-TMH model at different levels including per protein, per segment, and per residue. The success rates of WRF-TMH model are quite promising and are the best reported so far on the same datasets. It is observed that WRF-TMH model might play a substantial role, and will provide essential information for further structural and functional studies on membrane proteins. The accompanied web predictor is accessible at http://111.68.99.218/WRF-TMH/ .

Improving transmembrane protein consensus topology prediction using inter-helical interaction

Alpha helix transmembrane proteins (αTMPs) represent roughly 30% of all open reading frames (ORFs) in a typical genome and are involved in many critical biological processes. Due to the special physicochemical properties, it is hard to crystallize and obtain high resolution structures experimentally, thus, sequence-based topology prediction is highly desirable for the study of transmembrane proteins (TMPs), both in structure prediction and function prediction. Various model-based topology prediction methods have been developed, but the accuracy of those individual predictors remain poor due to the limitation of the methods or the features they used. Thus, the consensus topology prediction method becomes practical for high accuracy applications by combining the advances of the individual predictors. Here, based on the observation that inter-helical interactions are commonly found within the transmembrane helixes (TMHs) and strongly indicate the existence of them, we present a novel consensus topology prediction method for αTMPs, CNTOP, which incorporates four top leading individual topology predictors, and further improves the prediction accuracy by using the predicted inter-helical interactions. The method achieved 87% prediction accuracy based on a benchmark dataset and 78% accuracy based on a non-redundant dataset which is composed of polytopic αTMPs. Our method derives the highest topology accuracy than any other individual predictors and consensus predictors, at the same time, the TMHs are more accurately predicted in their length and locations, where both the false positives (FPs) and the false negatives (FNs) decreased dramatically. The CNTOP is available at: http://ccst.jlu.edu.cn/JCSB/cntop/CNTOP.html.

Topological and mutational analysis of Saccharomyces cerevisiae Fks1

Fks1, with orthologs in nearly all fungi as well as plants and many protists, plays a central role in fungal cell wall formation as the putative catalytic component of β-1,3-glucan synthase. It is also the target for an important new antifungal group, the echinocandins, as evidenced by the localization of resistance-conferring mutations to Fks1 hot spots 1, 2, and 3 (residues 635 to 649, 1354 to 1361, and 690 to 700, respectively). Since Fks1 is an integral membrane protein and echinocandins are cyclic peptides with lipid tails, Fks1 topology is key to understanding its function and interaction with echinocandins. We used hemagglutinin (HA)-Suc2-His4C fusions to C-terminally truncated Saccharomyces cerevisiae Fks1 to experimentally define its topology and site-directed mutagenesis to test function of selected residues. Of the 15 to 18 transmembrane helices predicted in silico for Fks1 from evolutionarily diverse fungi, 13 were experimentally confirmed. The N terminus (residues 1 to 445) is cytosolic and the C terminus (residues 1823 to 1876) external; both are essential to Fks1 function. The cytosolic central domain (residues 715 to 1294) includes newly recognized homology to glycosyltransferases, and residues potentially involved in substrate UDP-glucose binding and catalysis are essential. All three hot spots are external, with hot spot 1 adjacent to and hot spot 3 largely embedded within the outer leaflet of the membrane. This topology suggests a model in which echinocandins interact through their lipid tails with hot spot 3 and through their cyclic peptides with hot spots 1 and 2.

Combining modelling and mutagenesis studies of synaptic vesicle protein 2A to identify a series of residues involved in racetam binding

LEV (levetiracetam), an antiepileptic drug which possesses a unique profile in animal models of seizure and epilepsy, has as its unique binding site in brain, SV2A (synaptic vesicle protein 2A). Previous studies have used a chimaeric and site-specific mutagenesis approach to identify three residues in the putative tenth transmembrane helix of SV2A that, when mutated, alter binding of LEV and related racetam derivatives to SV2A. In the present paper, we report a combined modelling and mutagenesis study that successfully identifies another 11 residues in SV2A that appear to be involved in ligand binding. Sequence analysis and modelling of SV2A suggested residues equivalent to critical functional residues of other MFS (major facilitator superfamily) transporters. Alanine scanning of these and other SV2A residues resulted in the identification of residues affecting racetam binding, including Ile273 which differentiated between racetam analogues, when mutated to alanine. Integrating mutagenesis results with docking analysis led to the construction of a mutant in which six SV2A residues were replaced with corresponding SV2B residues. This mutant showed racetam ligand-binding affinity intermediate to the affinities observed for SV2A and SV2B.

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.

STRUCTURE PREDICTION OF HELICAL TRANSMEMBRANE PROTEINS AT TWO LENGTH SCALES

As the first step toward a multi-scale, hierarchical computational approach for membrane protein structure prediction, the packing of transmembrane helices was modeled at the residue and atom levels, respectively. For predictions at the residue level, the helix-helix and helix-membrane interactions were described by a set of knowledge-based energy functions. For predictions at the atom level, CHARMM19 force field was used. To facilitate the system to overcome energy barriers, the Wang–Landau method was employed, where a random walk is performed in the energy space with a uniform probability. Native-like structures were predicted at both levels for two model systems, each of which consists of two transmembrane helices. Interestingly, consistent results were obtained from simulations at the residue and atom levels for the same system, strongly suggesting the feasibility of a hierarchical approach for membrane protein structure predictions.

RECOGNITION OF TRANSMEMBRANE SEGMENTS IN PROTEINS: REVIEW AND CONSISTENCY-BASED BENCHMARKING OF INTERNET SERVERS

Membrane proteins perform a number of crucial functions as transporters, receptors, and components of enzyme complexes. Identification of membrane proteins and prediction of their topology is thus an important part of genome annotation. We present here an overview of transmembrane segments in protein sequences, summarize data from large-scale genome studies, and report results of benchmarking of several popular internet servers.

SOMPNN: an efficient non-parametric model for predicting transmembrane helices

Accurately predicting the transmembrane helices (TMH) in a helical membrane protein is an important but challenging task. Recent researches have demonstrated that statistics-based methods are promising routes to improve the TMH prediction accuracy. However, most of existing TMH predictors are parametric models and they have to make assumptions of several or even hundreds of adjustable parameters based on the underlying probability distribution, which is difficult when no a priori knowledge is available. Besides the performances of these parametric predictors significantly depend on the estimated parameters, some of them need to exploit the entire training dataset in the prediction stage, which will lead to low prediction efficiency and this problem will become even worse when dealing with large-scale dataset. In this paper, we propose a novel SOMPNN model for prediction of TMH that features by minimal parameter assumptions requirement and high computational efficiency. In the SOMPNN model, a self-organizing map (SOM) is used to adaptively learn the helices distribution knowledge hidden in the training data, and then a probabilistic neural network (PNN) is adopted to predict TMH segments based on the knowledge learned by SOM. Experimental results on two benchmark datasets show that the proposed SOMPNN outperforms most existing popular TMH predictors and is promising to be extended to deal with other complicated biological problems. The datasets and the source codes of SOMPNN are available at http://www.csbio.sjtu.edu.cn/bioinf/SOMPNN/.

Prediction of transmembrane topology and signal peptide given a protein's amino acid sequence

Here, we describe transmembrane topology and signal peptide predictors and highlight their advantages and shortcomings. We also discuss the relation between these two types of prediction.

Integrated prediction of one-dimensional structural features and their relationships with conformational flexibility in helical membrane proteins

Background

Many structural properties such as solvent accessibility, dihedral angles and helix-helix contacts can be assigned to each residue in a membrane protein. Independent studies exist on the analysis and sequence-based prediction of some of these so-called one-dimensional features. However, there is little explanation of why certain residues are predicted in a wrong structural class or with large errors in the absolute values of these features. On the other hand, membrane proteins undergo conformational changes to allow transport as well as ligand binding. These conformational changes often occur via residues that are inherently flexible and hence, predicting fluctuations in residue positions is of great significance.

Results

We performed a statistical analysis of common patterns among selected one-dimensional equilibrium structural features (ESFs) and developed a method for simultaneously predicting all of these features using an integrated system. Our results show that the prediction performance can be improved if multiple structural features are trained in an integrated model, compared to the current practice of developing individual models. In particular, the performance of the solvent accessibility and bend-angle prediction improved in this way. The well-performing bend-angle prediction can be used to predict helical positions with severe kinks at a modest success rate. Further, we showed that single-chain conformational dynamics, measured by B-factors derived from normal mode analysis, could be predicted from observed and predicted ESFs with good accuracy. A web server was developed (http://tardis.nibio.go.jp/netasa/htmone/) for predicting the one-dimensional ESFs from sequence information and analyzing the differences between the predicted and observed values of the ESFs.

Conclusions

The prediction performance of the integrated model is significantly better than that of the models performing the task separately for each feature for the solvent accessibility and bend-angle predictions. The predictability of the features also plays a role in determining flexible positions. Although the dynamics studied here concerns local atomic fluctuations, a similar analysis in terms of global structural features will be helpful in predicting large-scale conformational changes, for which work is in progress.

Prediction of the human membrane proteome

Membrane proteins are key molecules in the cell, and are important targets for pharmaceutical drugs. Few three-dimensional structures of membrane proteins have been obtained, which makes computational prediction of membrane proteins crucial for studies of these key molecules. Here, seven membrane protein topology prediction methods based on different underlying algorithms, such as hidden Markov models, neural networks and support vector machines, have been used for analysis of the protein sequences from the 21,416 annotated genes in the human genome. The number of genes coding for a protein with predicted alpha-helical transmembrane region(s) ranged from 5508 to 7651, depending on the method used. Based on a majority decision method, we estimate 5539 human genes to code for membrane proteins, corresponding to approximately 26% of the human protein-coding genes. The largest fraction of these proteins has only one predicted transmembrane region, but there are also many proteins with seven predicted transmembrane regions, including the G-protein coupled receptors. A visualization tool displaying the topologies suggested by the eight prediction methods, for all predicted membrane proteins, is available on the public Human Protein Atlas portal (www.proteinatlas.org).

The prediction of membrane protein structure and genome structural annotation

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

Active machine learning for transmembrane helix prediction

Background

About 30% of genes code for membrane proteins, which are involved in a wide variety of crucial biological functions. Despite their importance, experimentally determined structures correspond to only about 1.7% of protein structures deposited in the Protein Data Bank due to the difficulty in crystallizing membrane proteins. Algorithms that can identify proteins whose high-resolution structure can aid in predicting the structure of many previously unresolved proteins are therefore of potentially high value. Active machine learning is a supervised machine learning approach which is suitable for this domain where there are a large number of sequences but only very few have known corresponding structures. In essence, active learning seeks to identify proteins whose structure, if revealed experimentally, is maximally predictive of others.

Results

An active learning approach is presented for selection of a minimal set of proteins whose structures can aid in the determination of transmembrane helices for the remaining proteins. TMpro, an algorithm for high accuracy TM helix prediction we previously developed, is coupled with active learning. We show that with a well-designed selection procedure, high accuracy can be achieved with only few proteins. TMpro, trained with a single protein achieved an F-score of 94% on benchmark evaluation and 91% on MPtopo dataset, which correspond to the state-of-the-art accuracies on TM helix prediction that are achieved usually by training with over 100 training proteins.

Conclusion

Active learning is suitable for bioinformatics applications, where manually characterized data are not a comprehensive representation of all possible data, and in fact can be a very sparse subset thereof. It aids in selection of data instances which when characterized experimentally can improve the accuracy of computational characterization of remaining raw data. The results presented here also demonstrate that the feature extraction method of TMpro is well designed, achieving a very good separation between TM and non TM segments.

Bioinformatic approaches for the structure and function of membrane proteins

Membrane proteins play important roles in the biology of the cell, including intercellular communication and molecular transport. Their well-established importance notwithstanding, the high-resolution structures of membrane proteins remain elusive due to difficulties in protein expression, purification and crystallization. Thus, accurate prediction of membrane protein topology can increase the understanding of membrane protein function. Here, we provide a brief review of the diverse computational methods for predicting membrane protein structure and function, including recent progress and essential bioinformatics tools. Our hope is that this review will be instructive to users studying membrane protein biology in their choice of appropriate bioinformatics methods.

Rational mutational analysis of a multidrug MFS transporter CaMdr1p of Candida albicans by employing a membrane environment based computational approach

CaMdr1p is a multidrug MFS transporter of pathogenic Candida albicans. An over-expression of the gene encoding this protein is linked to clinically encountered azole resistance. In-depth knowledge of the structure and function of CaMdr1p is necessary for an effective design of modulators or inhibitors of this efflux transporter. Towards this goal, in this study, we have employed a membrane environment based computational approach to predict the functionally critical residues of CaMdr1p. For this, information theoretic scores which are variants of Relative Entropy (Modified Relative Entropy RE(M)) were calculated from Multiple Sequence Alignment (MSA) by separately considering distinct physico-chemical properties of transmembrane (TM) and inter-TM regions. The residues of CaMdr1p with high RE(M) which were predicted to be significantly important were subjected to site-directed mutational analysis. Interestingly, heterologous host Saccharomyces cerevisiae, over-expressing these mutant variants of CaMdr1p wherein these high RE(M) residues were replaced by either alanine or leucine, demonstrated increased susceptibility to tested drugs. The hypersensitivity to drugs was supported by abrogated substrate efflux mediated by mutant variant proteins and was not attributed to their poor expression or surface localization. Additionally, by employing a distance plot from a 3D deduced model of CaMdr1p, we could also predict the role of these functionally critical residues in maintaining apparent inter-helical interactions to provide the desired fold for the proper functioning of CaMdr1p. Residues predicted to be critical for function across the family were also found to be vital from other previously published studies, implying its wider application to other membrane protein families.

Structural genomics target selection for the New York consortium on membrane protein structure

The New York Consortium on Membrane Protein Structure (NYCOMPS), a part of the Protein Structure Initiative (PSI) in the USA, has as its mission to establish a high-throughput pipeline for determination of novel integral membrane protein structures. Here we describe our current target selection protocol, which applies structural genomics approaches informed by the collective experience of our team of investigators. We first extract all annotated proteins from our reagent genomes, i.e. the 96 fully sequenced prokaryotic genomes from which we clone DNA. We filter this initial pool of sequences and obtain a list of valid targets. NYCOMPS defines valid targets as those that, among other features, have at least two predicted transmembrane helices, no predicted long disordered regions and, except for community nominated targets, no significant sequence similarity in the predicted transmembrane region to any known protein structure. Proteins that feed our experimental pipeline are selected by defining a protein seed and searching the set of all valid targets for proteins that are likely to have a transmembrane region structurally similar to that of the seed. We require sequence similarity aligning at least half of the predicted transmembrane region of seed and target. Seeds are selected according to their feasibility and/or biological interest, and they include both centrally selected targets and community nominated targets. As of December 2008, over 6,000 targets have been selected and are currently being processed by the experimental pipeline. We discuss how our target list may impact structural coverage of the membrane protein space.

Data-driven model for the prediction of protein transmembrane regions

We present a novel approach combining mathematical methods and artificial neural networks to predict the transmembrane regions of transmembrane proteins, considering protein sequence information alone. We have focused on developing a data-driven model based on a non-linear modelling method, the counter-propagation artificial neural network, and on mathematical descriptors defining the sequence information of transmembrane proteins with known three-dimensional structures. The developed model has proven to be promising in predicting protein transmembrane regions, with an error below 10% for the external validation set. In combination with available experimental data the model can give us a better understanding of transmembrane proteins.

7TMRmine: a Web server for hierarchical mining of 7TMR proteins

BACKGROUND

Seven-transmembrane region-containing receptors (7TMRs) play central roles in eukaryotic signal transduction. Due to their biomedical importance, thorough mining of 7TMRs from diverse genomes has been an active target of bioinformatics and pharmacogenomics research. The need for new and accurate 7TMR/GPCR prediction tools is paramount with the accelerated rate of acquisition of diverse sequence information. Currently available and often used protein classification methods (e.g., profile hidden Markov Models) are highly accurate for identifying their membership information among already known 7TMR subfamilies. However, these alignment-based methods are less effective for identifying remote similarities, e.g., identifying proteins from highly divergent or possibly new 7TMR families. In this regard, more sensitive (e.g., alignment-free) methods are needed to complement the existing protein classification methods. A better strategy would be to combine different classifiers, from more specific to more sensitive methods, to identify a broader spectrum of 7TMR protein candidates.

DESCRIPTION

We developed a Web server, 7TMRmine, by integrating alignment-free and alignment-based classifiers specifically trained to identify candidate 7TMR proteins as well as transmembrane (TM) prediction methods. This new tool enables researchers to easily assess the distribution of GPCR functionality in diverse genomes or individual newly-discovered proteins. 7TMRmine is easily customized and facilitates exploratory analysis of diverse genomes. Users can integrate various alignment-based, alignment-free, and TM-prediction methods in any combination and in any hierarchical order. Sixteen classifiers (including two TM-prediction methods) are available on the 7TMRmine Web server. Not only can the 7TMRmine tool be used for 7TMR mining, but also for general TM-protein analysis. Users can submit protein sequences for analysis, or explore pre-analyzed results for multiple genomes. The server currently includes prediction results and the summary statistics for 68 genomes.

CONCLUSION

7TMRmine facilitates the discovery of 7TMR proteins. By combining prediction results from different classifiers in a multi-level filtering process, prioritized sets of 7TMR candidates can be obtained for further investigation. 7TMRmine can be also used as a general TM-protein classifier. Comparisons of TM and 7TMR protein distributions among 68 genomes revealed interesting differences in evolution of these protein families among major eukaryotic phyla.

Transmembrane protein topology prediction using support vector machines

Background

Alpha-helical transmembrane (TM) proteins are involved in a wide range of important biological processes such as cell signaling, transport of membrane-impermeable molecules, cell-cell communication, cell recognition and cell adhesion. Many are also prime drug targets, and it has been estimated that more than half of all drugs currently on the market target membrane proteins. However, due to the experimental difficulties involved in obtaining high quality crystals, this class of protein is severely under-represented in structural databases. In the absence of structural data, sequence-based prediction methods allow TM protein topology to be investigated.

Results

We present a support vector machine-based (SVM) TM protein topology predictor that integrates both signal peptide and re-entrant helix prediction, benchmarked with full cross-validation on a novel data set of 131 sequences with known crystal structures. The method achieves topology prediction accuracy of 89%, while signal peptides and re-entrant helices are predicted with 93% and 44% accuracy respectively. An additional SVM trained to discriminate between globular and TM proteins detected zero false positives, with a low false negative rate of 0.4%. We present the results of applying these tools to a number of complete genomes. Source code, data sets and a web server are freely available from .

Conclusion

The high accuracy of TM topology prediction which includes detection of both signal peptides and re-entrant helices, combined with the ability to effectively discriminate between TM and globular proteins, make this method ideally suited to whole genome annotation of alpha-helical transmembrane proteins.

Predictions suggesting a participation of beta-sheet configuration in the M2 domain of the P2X(7) receptor: a novel conformation?

Scanning experiments have shown that the putative TM2 domain of the P2X(7) receptor (P2X(7)R) lines the ionic pore. However, none has identified an alpha-helix structure, the paradigmatic secondary structure of ion channels in mammalian cells. In addition, some researchers have suggested a beta-sheet conformation in the TM2 domain of P2X(2). These data led us to investigate a new architecture within the P2X receptor family. P2X(7)R is considered an intriguing receptor because its activation induces nonselective large pore formation, in contrast to the majority of other ionic channel proteins in mammals. This receptor has two states: a low-conductance channel (approximately 10 pS) and a large pore (> 400 pS). To our knowledge, one fundamental question remains unanswered: Are the P2X(7)R channel and the pore itself the same entity or are they different structures? There are no structural data to help solve this question. Thus, we investigated the hydrophobic M2 domain with the aim of predicting the fitted position and the secondary structure of the TM2 segment from human P2X(7)R (hP2X(7)R). We provide evidence for a beta-sheet conformation, using bioinformatics algorithms and molecular-dynamics simulation in conjunction with circular dichroism in different environments and Fourier transform infrared spectroscopy. In summary, our study suggests the possibility that a segment composed of residues from part of the M2 domain and part of the putative TM2 segment of P2X(7)R is partially folded in a beta-sheet conformation, and may play an important role in channel/pore formation associated with P2X(7)R activation. It is important to note that most nonselective large pores have a transmembrane beta-sheet conformation. Thus, this study may lead to a paradigmatic change in the P2X(7)R field and/or raise new questions about this issue.

Large-scale analysis of thermostable, mammalian proteins provides insights into the intrinsically disordered proteome

Intrinsically disordered proteins are predicted to be highly abundant and play broad biological roles in eukaryotic cells. In particular, by virtue of their structural malleability and propensity to interact with multiple binding partners, disordered proteins are thought to be specialized for roles in signaling and regulation. However, these concepts are based on in silico analyses of translated whole genome sequences, not on large-scale analyses of proteins expressed in living cells. Therefore, whether these concepts broadly apply to expressed proteins is currently unknown. Previous studies have shown that heat-treatment of cell extracts lead to partial enrichment of soluble, disordered proteins. On the basis of this observation, we sought to address the current dearth of knowledge about expressed, disordered proteins by performing a large-scale proteomics study of thermostable proteins isolated from mouse fibroblast cells. With the use of novel multidimensional chromatography methods and mass spectrometry, we identified a total of 1320 thermostable proteins from these cells. Further, we used a variety of bioinformatics methods to analyze the structural and biological properties of these proteins. Interestingly, more than 900 of these expressed proteins were predicted to be substantially disordered. These were divided into two categories, with 514 predicted to be predominantly disordered and 395 predicted to exhibit both disordered and ordered/folded features. In addition, 411 of the thermostable proteins were predicted to be folded. Despite the use of heat treatment (60 min at 98 degrees C) to partially enrich for disordered proteins, which might have been expected to select for small proteins, the sequences of these proteins exhibited a wide range of lengths (622 +/- 555 residues (average length +/- standard deviation) for disordered proteins and 569 +/- 598 residues for folded proteins). Computational structural analyses revealed several unexpected features of the thermostable proteins: (1) disordered domains and coiled-coil domains occurred together in a large number of disordered proteins, suggesting functional interplay between these domains; and (2) more than 170 proteins contained lengthy domains (>300 residues) known to be folded. Reference to Gene Ontology Consortium functional annotations revealed that, while disordered proteins play diverse biological roles in mouse fibroblasts, they do exhibit heightened involvement in several functional categories, including, cytoskeletal structure and cell movement, metabolic and biosynthetic processes, organelle structure, cell division, gene transcription, and ribonucleoprotein complexes. We believe that these results reflect the general properties of the mouse intrinsically disordered proteome (IDP-ome) although they also reflect the specialized physiology of fibroblast cells. Large-scale identification of expressed, thermostable proteins from other cell types in the future, grown under varied physiological conditions, will dramatically expand our understanding of the structural and biological properties of disordered eukaryotic proteins.

Molecular cloning and characterization of a novel pollen predominantly membrane protein gene BcMF12 from Brassica campestris ssp. chinensis

A novel membrane protein gene, BcMF12, was isolated from Chinese cabbage (Brassica campestris L. ssp. chinensis Makino) using rapid amplification of the cDNA ends based on a pollen-specific cDNA fragment (DN237936). The cDNA was 1,155 bp in length with an open reading frame of 894 bp capable of encoding a putative polypeptide of 297 amino acids with an estimated molecular mass of 34.6 kDa and a predicted isoelectric point of 9.6. Comparative and bioinformatics analyses revealed that BcMF12 showed high similarities with some membrane protein sequences previously published in the public database and contained six highly conserved transmembrane domains corresponding to six highly hydrophobic regions. This indicates that BcMF12 may be a putative membrane protein. RNA gel blot analysis indicated that the transcripts of BcMF12 were abundant in the flower bud, flower and anther, but not detected in the root, stem, leaf and pistil. Moreover, the BcMF12 transcripts were detectable at the late stages of pollen development. Morphological investigations of pollen from the BcMF12 antisense transgenic plants showed that most of pollen grains of transgenic plants were abnormal. These results strongly suggest that BcMF12 is a novel pollen-preferentially membrane protein which play an important role during the pollen development in Chinese cabbage.