Identification of beta-barrel membrane proteins based on amino acid composition properties and predicted secondary structure

Unlike all-helices membrane proteins, beta-barrel membrane proteins can not be successfully discriminated from other proteins, especially from all-beta soluble proteins. This paper performs an analysis on the amino acid composition in membrane parts of 12 beta-barrel membrane proteins versus beta-strands of 79 all-beta soluble proteins. The average and variance of the amino acid composition in these two classes are calculated. Amino acids such as Gly, Asn, Val that are most likely associated with classification are selected based on Fishers discriminant ratio. A linear classifier built with these selected amino acids composition in observed beta-strands achieves 100% classification accuracy for 12 membrane proteins and 79 soluble proteins in a four-fold cross-validation experiment. Since at present the accuracy of secondary structure prediction is quite high, a promising method to identify beta-barrel membrane proteins is presented based on the linear classifier coupled with predicted secondary structure. Applied to 241 beta-barrel membrane proteins and 3855 soluble proteins with various structures, the method achieves 85.48% (206/241) sensitivity and 92.53% specificity (3567/3855).     

Detergent-associated solution conformations of helical and beta-barrel membrane proteins

Membrane proteins present major challenges for structural biology. In particular, the production of suitable crystals for high-resolution structural determination continues to be a significant roadblock for developing an atomic-level understanding of these vital cellular systems. The use of detergents for extracting membrane proteins from the native membrane for either crystallization or reconstitution into model lipid membranes for further study is assumed to leave the protein with the proper fold with a belt of detergent encompassing the membrane-spanning segments of the structure. Small-angle X-ray scattering was used to probe the detergent-associated solution conformations of three membrane proteins, namely bacteriorhodopsin (BR), the Ste2p G-protein coupled receptor from Saccharomyces cerevisiae, and the Escherichia coli porin OmpF. The results demonstrate that, contrary to the traditional model of a detergent-associated membrane protein, the helical proteins BR and Ste2p are not in the expected, compact conformation and associated with detergent micelles, while the beta-barrel OmpF is indeed embedded in a disk-like micelle in a properly folded state. The comparison provided by the BR and Ste2p, both members of the 7TM family of helical membrane proteins, further suggests that the interhelical interactions between the transmembrane helices of the two proteins differ, such that BR, like other rhodopsins, can properly refold to crystallize, while Ste2p continues to prove resistant to crystallization from an initially detergent-associated state.     

TMBETADISC-RBF: Discrimination of beta-barrel membrane proteins using RBF networks and PSSM profiles

Discriminating outer membrane proteins (OMPs) from other folding types of globular and membrane proteins is an important task both for identifying OMPs from genomic sequences and for the successful prediction of their secondary and tertiary structures. We have developed a method based on radial basis function networks and position specific scoring matrix (PSSM) profiles generated by PSI-BLAST and non-redundant protein database. Our approach with PSSM profiles has correctly predicted the OMPs with a cross-validated accuracy of 96.4% in a set of 1251 proteins, which contain 206 OMPs, 667 globular proteins and 378 alpha-helical inner membrane proteins. Furthermore, we applied our method on a dataset containing 114 OMPs, 187 TMH proteins and 195 globular proteins obtained with less than 20% sequence identity and obtained the cross-validated accuracy of 95%. This accuracy of discriminating OMPs is higher than other methods in the literature and our method could be used as an effective tool for dissecting OMPs from genomic sequences. We have developed a prediction server, TMBETADISC-RBF, which is available at http://rbf.bioinfo.tw/~sachen/OMP.html.     

TMB finding pipeline: novel approach for detecting beta-barrel membrane proteins in genomic sequences

We have developed a novel approach for dissecting transmembrane beta-barrel proteins (TMBs) in genomic sequences. The features include (i) the identification of TMBs using the preference of residue pairs in globular, transmembrane helical (TMH) and TMBs, (ii) elimination of globular/TMH proteins that show sequence identity of more than 70% for the coverage of 80% residues with known structures, (iii) elimination of globular/TMH proteins that have sequence identity of more than 60% with known sequences in SWISS-PROT, and (iv) exclusion of TMH proteins using SOSUI, a prediction system for TMH proteins. Our approach picked up 7% TMBs in all the considered genomes. The comparison between the identified TMBs in E. coli genome and available experimental data demonstrated that the new approach could correctly identify all the 11 known TMBs, whose crystal structures are available. Further, it revealed the presence of 19 TMBs, homology with known structures, 60 TMBs similar to well annotated sequences, and 54 TMBs that have high sequence similarity with Escherichia coli beta-barrel proteins deposited in Transport Classification Database (TCDB). Interestingly, the present approach identified TMBs from all 15 families in TCDB. In human genome, the occurrence of TMBs varies from 0 to 3% in different chromosomes. We suggest that our approach could lead to a step forward in the advancement of structural and functional genomics.     

beta-Barrel membrane bacterial proteins: structure, function, assembly and interaction with lipids

Membrane proteins, although constituting about one-third of all proteins encoded by the genomes of living organisms, are still strongly underrepresented in the database of 3D protein structures, which reflects the big challenge presented by this class of proteins. Structural biologists, by employing electron and x-ray approaches, are continuously revealing new and fundamental insights into the structure, function, assembly and interaction with lipids of membrane proteins. To date, two structural motifs, alpha-helices and beta-sheets, have been found in membrane proteins and interestingly these two structural motives correlate with the location: while alpha-helical bundles are most often found in the receptors and ion channels of plasma and endoplasmic reticulum membranes, beta-barrels are restricted to the outer membrane of Gram-negative bacteria and in the mitochondrial membrane, and represent the structural motif used by several microbial toxins to form cytotoxic transmembrane channels. The beta-barrel, while being a rigid and stable motif is a versatile scaffold, having a wide variation in the size of the barrel, in the mechanism to open or close the gate and to impose selectivity on substrates. Even if the number of x-ray structures of integral membrane proteins has greatly increased in recent years, only a few of them provide information at a molecular level on how proteins interact with lipids that surround them in the membrane. The detailed mechanism of protein lipid interactions is of fundamental importance for understanding membrane protein folding, membrane adsorption, insertion and function in lipid bilayers. Both specific and unspecific interactions with lipids may participate in protein folding and assembly.     

Thermodynamics, the structure of integral membrane proteins, and transport

Membranes are structures whose lipid and protein components are at, or close to, equilibrium in the plane of the membrane, but are not at equilibrium across the membrane. The thermodynamic tendency of ionic and highly polar molecules to be in contact with water rather than with nonpolar media (hydrophilic interactions) is important in determining these equilibrium and nonequilibrium states. In this paper, we speculate about the structures and orientations of integral proteins in a membrane, and about how the equilibrium and nonequilibrium features of such structures and orientations might be influenced by the special mechanisms of biosynthesis, processing, and membrane insertion of these proteins. The relevance of these speculations to the mechanisms of the translocation event in membrane transport is discussed, and specific protein models of transport that have been proposed are analyzed.     

Integrating protein secondary structure prediction and multiple sequence alignment

Modern protein secondary structure prediction methods are based on exploiting evolutionary information contained in multiple sequence alignments. Critical steps in the secondary structure prediction process are (i) the selection of a set of sequences that are homologous to a given query sequence, (ii) the choice of the multiple sequence alignment method, and (iii) the choice of the secondary structure prediction method. Because of the close relationship between these three steps and their critical influence on the prediction results, secondary structure prediction has received increased attention from the bioinformatics community over the last few years. In this treatise, we discuss recent developments in computational methods for protein secondary structure prediction and multiple sequence alignment, focus on the integration of these methods, and provide some recommendations for state-of-the-art secondary structure prediction in practice.     

Analysis and prediction of RNA-binding residues using sequence, evolutionary conservation, and predicted secondary structure and solvent accessibility

Identification and prediction of RNA-binding residues (RBRs) provides valuable insights into the mechanisms of protein-RNA interactions. We analyzed the contributions of a wide range of factors including amino acid sequence, evolutionary conservation, secondary structure and solvent accessibility, to the prediction/characterization of RBRs. Five feature sets were designed and feature selection was performed to find and investigate relevant features. We demonstrate that (1) interactions with positively charged amino acids Arg and Lys are preferred by the egatively charged nucleotides; (2) Gly provides flexibility for the RNA binding sites; (3) Glu with negatively charged side chain and several hydrophobic residues such as Leu, Val, Ala and Phe are disfavored in the RNA-binding sites; (4) coil residues, especially in long segments, are more flexible (than other secondary structures) and more likely to interact with RNA; (5) helical residues are more rigid and consequently they are less likely to bind RNA; and (6) residues partially exposed to the solvent are more likely to form RNA-binding sites. We introduce a novel sequence-based predictor of RBRs, RBRpred, which utilizes the selected features. RBRpred is comprehensively tested on three datasets with varied atom distance cutoffs by performing both five-fold cross validation and jackknife tests and achieves Matthew's correlation coefficient (MCC) of 0.51, 0.48 and 0.42, respectively. The quality is comparable to or better than that for state-of-the-art predictors that apply the distancebased cutoff definition. We show that the most important factor for RBRs prediction is evolutionary conservation, followed by the amino acid sequence, predicted secondary structure and predicted solvent accessibility. We also investigate the impact of using native vs. predicted secondary structure and solvent accessibility. The predictions are sufficient for the RBR prediction and the knowledge of the actual solvent accessibility helps in predictions for lower distance cutoffs.     

Calciomics:prediction and analysis of EF-hand calcium binding proteins by protein engineering

Ca2+ plays a pivotal role in the physiology and biochemistry of prokaryotic and mammalian organisms.Viruses also utilize the universal Ca2+ signal to create a specific cellular environment to achieve coexistence with the host,and to propagate.In this paper we first describe our development of a grafting approach to understand site-specific Ca2+ binding properties of EF-hand proteins with a helix-loop-helix Ca2+ binding motif,then summarize our prediction and identification of EF-hand Ca2+ binding sites on a...     

Calciomics: prediction and analysis of EF-hand calcium binding proteins by protein engineering

Ca²⁺ plays a pivotal role in the physiology and biochemistry of prokaryotic and mammalian organisms. Viruses also utilize the universal Ca²⁺ signal to create a specific cellular environment to achieve coexistence with the host, and to propagate. In this paper we first describe our development of a grafting approach to understand site-specific Ca²⁺ binding properties of EF-hand proteins with a helix-loop-helix Ca²⁺ binding motif, then summarize our prediction and identification of EF-hand Ca²⁺ binding sites on a genome-wide scale in bacteria and virus, and next report the application of the grafting approach to probe the metal binding capability of predicted EF-hand motifs within the streptococcal hemoprotein receptor (Shr) of Streptococcus pyrogenes and the nonstructural protein 1 (nsP1) of Sindbis virus. When methods such as the grafting approach are developed in conjunction with prediction algorithms we are better able to probe continuous Ca²⁺-binding sites that have been previously underrepresented due to the limitation of conventional methodology.     

The influence of gapped positions in multiple sequence alignments on secondary structure prediction methods

All currently leading protein secondary structure prediction methods use a multiple protein sequence alignment to predict the secondary structure of the top sequence. In most of these methods, prior to prediction, alignment positions showing a gap in the top sequence are deleted, consequently leading to shrinking of the alignment and loss of position-specific information. In this paper we investigate the effect of this removal of information on secondary structure prediction accuracy. To this end, we have designed SymSSP, an algorithm that post-processes the predicted secondary structure of all sequences in a multiple sequence alignment by (i) making use of the alignment's evolutionary information and (ii) re-introducing most of the information that would otherwise be lost. The post-processed information is then given to a new dynamic programming routine that produces an optimally segmented consensus secondary structure for each of the multiple alignment sequences. We have tested our method on the state-of-the-art secondary structure prediction methods PHD, PROFsec, SSPro2 and JNET using the HOMSTRAD database of reference alignments. Our consensus-deriving dynamic programming strategy is consistently better at improving the segmentation quality of the predictions compared to the commonly used majority voting technique. In addition, we have applied several weighting schemes from the literature to our novel consensus-deriving dynamic programming routine. Finally, we have investigated the level of noise introduced by prediction errors into the consensus and show that predictions of edges of helices and strands are half the time wrong for all the four tested prediction methods.     

Improved helix and kink characterization in membrane proteins allows evaluation of kink sequence predictors

Although the α-helical secondary structure of proteins is well-defined, the exact causes and structures of helical kinks are not. This is especially important for transmembrane (TM) helices of integral membrane proteins, many of which contain kinks providing functional diversity despite predominantly helical structure. We have developed a Monte Carlo method based algorithm, MC-HELAN, to determine helical axes alongside positions and angles of helical kinks. Analysis of all nonredundant high-resolution α-helical membrane protein structures (842 TM helices from 205 polypeptide chains) revealed kinks in 64% of TM helices, demonstrating that a significantly greater proportion of TM helices are kinked than those indicated by previous analyses. The residue proline is over-represented by a factor >5 if it is two or three residues C-terminal to a bend. Prolines also cause kinks with larger kink angles than other residues. However, only 33% of TM kinks are in proximity to a proline. Machine learning techniques were used to test for sequence-based predictors of kinks. Although kinks are somewhat predicted by sequence, kink formation appears to be driven predominantly by other factors. This study provides an improved view of the prevalence and architecture of kinks in helical membrane proteins and highlights the fundamental inaccuracy of the typical topological depiction of helical membrane proteins as series of ideal helices.     

Structural genomics on membrane proteins: mini review

Structural genomics, structure-based analysis of gene products, has so far mainly concentrated on soluble proteins because of their less demanding requirements for overexpression, purification and crystallisation compared to membrane proteins. This so-called "low-hanging fruit" approach has generated more than 25,000 structures deposited in databases. In contrast, the substantially more complex membrane proteins, in relation to all steps from overexpression to high-resolution structure determination, represent less than 1% of available crystal structures. This is in sharp contrast to the importance of this type of proteins, particularly G protein-coupled receptors (GPCRs), as today 60-70% of the current drug targets are based on membrane proteins. The key to improved success with membrane protein structural elucidation is technology development. The most efficient approach constitutes parallel studies on a large number of targets and evaluation of various systems for expression. Next, high throughput format solubilisation and refolding screening methods for a wide range of detergents and additives in numerous concentrations should be established. Today, several networks dealing with structural genomics approaches of membrane proteins have been initiated, among them the Membrane Protein Network (MePNet) programme that deals with the pharmaceutically important mammalian GPCRs. In MePNet, three overexpression systems have been employed for the evaluation of 101 GPCRs, which has generated large quantities of numerous recombinant GPCRs, compatible for structural biology applications.     

Dipolar waves map the structure and topology of helices in membrane proteins

Dipolar waves describe the structure and topology of helices in membrane proteins. The fit of sinusoids with the 3.6 residues per turn period of ideal alpha-helices to experimental measurements of dipolar couplings as a function of residue number makes it possible to simultaneously identify the residues in the helices, detect kinks or curvature in the helices, and determine the absolute rotations and orientations of helices in completely aligned bilayer samples and relative rotations and orientations of helices in a common molecular frame in weakly aligned micelle samples. Since as much as 80% of the structured residues in a membrane protein are in helices, the analysis of dipolar waves provides a significant step toward structure determination of helical membrane proteins by NMR spectroscopy.     

Towards a native environment: structure and function of membrane proteins in lipid bilayers by NMR

Solid-state NMR (ssNMR) is a versatile technique that can be used for the characterization of various materials, ranging from small molecules to biological samples, including membrane proteins. ssNMR can probe both the structure and dynamics of membrane proteins, revealing protein function in a near-native lipid bilayer environment. The main limitation of the method is spectral resolution and sensitivity, however recent developments in ssNMR hardware, including the commercialization of 28 T magnets (1.2 GHz proton frequency) and ultrafast MAS spinning (<100 kHz) promise to accelerate acquisition, while reducing sample requirement, both of which are critical to membrane protein studies. Here, we review recent advances in ssNMR methodology used for structure determination of membrane proteins in native and mimetic environments, as well as the study of protein functions such as protein dynamics, and interactions with ligands, lipids and cholesterol.

Solid-state NMR (ssNMR) is a versatile technique that can be used for the characterization of various materials, ranging from small molecules to biological samples, including membrane proteins, as reviewed here.     

Internal sequence analysis of proteins separated on polyacrylamide gels at the submicrogram level: improved methods, applications and gene cloning strategies

The fields of protein chemistry and molecular biology are currently merging for study of biologically relevant events and conditions. To obtain partial sequences of microamounts of protein, efficient integration of high resolution separation and sequencing technologies is required. We report here on improved methods that allow extensive internal sequencing of 10 to 20 picomoles protein recovered from one- or two-dimensional gels. Each step of the standard protocol of Aebersold et al. (Proc. Natl. Acad. Sci. USA 1987, 84, 6970-6974) and the required instrumentation were examined and specifically adapted for use with submicrogram amounts of protein. Optimizations of in situ microdigests and liquid chromatography were needed for improved peptide recovery. Subsequent automated sequencing required subpicomole analysis. New methods for S-alkylation of gel-separated proteins and accurate identification of tryptophan-containing peptides were introduced to insure overall higher efficiencies. The acquired internal sequences facilitated cloning of the genes and several strategies are discussed. Applying our method, several proteins of unknown structure were sequenced and successfully identified or cloned. Internal sequences of submicrogram protein amounts, recovered from a single two-dimensional gel of Escherichia coli total protein (120 micrograms), allowed unambiguous identification of the spots but pre-gel enrichment will be required for analysis of most (90-95%) other spots. Integration of comprehensive two-dimensional gel protein databases with methods and strategies outlined here could potentially be an abundant source of DNA probes and markers useful for guidance of the human genome sequencing project and for analysis of the emerging vast amounts of data.