Membrane protein prediction methods

Protein Sorting Prediction

Many computational methods are available for predicting protein sorting in bacteria. When comparing them, it is important to know that they can be grouped into three fundamentally different approaches: signal-based, global property-based, and homology-based prediction. In this chapter, the strengths and drawbacks of each of these approaches are described through many examples of methods that predict secretion, integration into membranes, or subcellular locations in general. The aim of this chapter is to provide a user-level introduction to the field with a minimum of computational theory.

An Update on "Reverse Vaccinology": The Pathway from Genomes and Epitope Predictions to Tailored, Recombinant Vaccines

In this chapter, we review the computational approaches that have led to a new generation of vaccines in recent years. There are many alternative routes to develop vaccines based on the concept of reverse vaccinology. They all follow the same basic principles-mining available genome and proteome information for antigen candidates, and recombinantly expressing them for vaccine production. Some of the same principles have been used successfully for cancer therapy approaches. In this review, we focus on infectious diseases, describing the general workflow from bioinformatic predictions of antigens and epitopes down to examples where such predictions have been used successfully for vaccine development.

Easy Synthesis of Complex Biomolecular Assemblies: Wheat Germ Cell-Free Protein Expression in Structural Biology

Cell-free protein synthesis (CFPS) systems are gaining more importance as universal tools for basic research, applied sciences, and product development with new technologies emerging for their application. Huge progress was made in the field of synthetic biology using CFPS to develop new proteins for technical applications and therapy. Out of the available CFPS systems, wheat germ cell-free protein synthesis (WG-CFPS) merges the highest yields with the use of a eukaryotic ribosome, making it an excellent approach for the synthesis of complex eukaryotic proteins including, for example, protein complexes and membrane proteins. Separating the translation reaction from other cellular processes, CFPS offers a flexible means to adapt translation reactions to protein needs. There is a large demand for such potent, easy-to-use, rapid protein expression systems, which are optimally serving protein requirements to drive biochemical and structural biology research. We summarize here a general workflow for a wheat germ system providing examples from the literature, as well as applications used for our own studies in structural biology. With this review, we want to highlight the tremendous potential of the rapidly evolving and highly versatile CFPS systems, making them more widely used as common tools to recombinantly prepare particularly challenging recombinant eukaryotic proteins.

Ligand Pose Predictions for Human G Protein-Coupled Receptors: Insights from the Amber-Based Hybrid Molecular Mechanics/Coarse-Grained Approach

Human G protein-coupled receptors (hGPCRs) are the most frequent targets of Food and Drug Administration (FDA)-approved drugs. Structural bioinformatics, along with molecular simulation, can support structure-based drug design targeting hGPCRs. In this context, several years ago, we developed a hybrid molecular mechanics (MM)/coarse-grained (CG) approach to predict ligand poses in low-resolution hGPCR models. The approach was based on the GROMOS96 43A1 and PRODRG united-atom force fields for the MM part. Here, we present a new MM/CG implementation using, instead, the Amber 14SB and GAFF all-atom potentials for proteins and ligands, respectively. The new implementation outperforms the previous one, as shown by a variety of applications on models of hGPCR/ligand complexes at different resolutions, and it is also more user-friendly. Thus, it emerges as a useful tool to predict poses in low-resolution models and provides insights into ligand binding similarly to all-atom molecular dynamics, albeit at a lower computational cost.

Comparative Genomics of Thiohalobacter thiocyanaticus HRh1T and Guyparkeria sp. SCN-R1, Halophilic Chemolithoautotrophic Sulfur-Oxidizing Gammaproteobacteria Capable of Using Thiocyanate as Energy Source

The genomes of Thiohalobacter thiocyanaticus and Guyparkeria (formerly known as Halothiobacillus) sp. SCN-R1, two gammaproteobacterial halophilic sulfur-oxidizing bacteria (SOB) capable of thiocyanate oxidation via the "cyanate pathway", have been analyzed with a particular focus on their thiocyanate-oxidizing potential and sulfur oxidation pathways. Both genomes encode homologs of the enzyme thiocyanate dehydrogenase (TcDH) that oxidizes thiocyanate via the "cyanate pathway" in members of the haloalkaliphilic SOB of the genus Thioalkalivibrio. However, despite the presence of conservative motives indicative of TcDH, the putative TcDH of the halophilic SOB have a low overall amino acid similarity to the Thioalkalivibrio enzyme, and also the surrounding genes in the TcDH locus were different. In particular, an alternative copper transport system Cus is present instead of Cop and a putative zero-valent sulfur acceptor protein gene appears just before TcDH. Moreover, in contrast to the thiocyanate-oxidizing Thioalkalivibrio species, both genomes of the halophilic SOB contained a gene encoding the enzyme cyanate hydratase. The sulfur-oxidizing pathway in the genome of Thiohalobacter includes a Fcc type of sulfide dehydrogenase, a rDsr complex/AprAB/Sat for oxidation of zero-valent sulfur to sulfate, and an incomplete Sox pathway, lacking SoxCD. The sulfur oxidation pathway reconstructed from the genome of Guyparkeria sp. SCN-R1 was more similar to that of members of the Thiomicrospira-Hydrogenovibrio group, including a Fcc type of sulfide dehydrogenase and a complete Sox complex. One of the outstanding properties of Thiohalobacter is the presence of a Na+-dependent ATP synthase, which is rarely found in aerobic Prokaryotes.Overall, the results showed that, despite an obvious difference in the general sulfur-oxidation pathways, halophilic and haloalkaliphilic SOB belonging to different genera within the Gammaproteobacteria developed a similar unique thiocyanate-degrading mechanism based on the direct oxidative attack on the sulfane atom of thiocyanate.

Membrane topology of rat sodium-coupled neutral amino acid transporter 2 (SNAT2)

Sodium-coupled neutral amino acid transporter 2 (SNAT2) is a subtype of the amino acid transport system A that is widely expressed in mammalian tissues. It plays critical roles in glutamic acid-glutamine circulation, liver gluconeogenesis and other biological pathway. However, the topology of the SNAT2 amino acid transporter is unknown. Here we identified the topological structure of SNAT2 using bioinformatics analysis, Methoxy-polyethylene glycol maleimide (mPEG-Mal) chemical modification, protease cleavage assays, immunofluorescence and examination of glycosylation. Our results show that SNAT2 contains 11 transmembrane domains (TMDs) with an intracellular N terminus and an extracellular C terminus. Three N-glycosylation sites were verified at the largest extracellular loop. This model is consistent with the previous model of SNAT2 with the exception of a difference in number of glycosylation sites. This is the first time to confirm the SNAT2 membrane topology using experimental methods. Our study on SNAT2 topology provides valuable structural information of one of the solute carrier family 38 (SLC38) members.

Protein Sorting Prediction

Many computational methods are available for predicting protein sorting in bacteria. When comparing them, it is important to know that they can be grouped into three fundamentally different approaches: signal-based, global-property-based and homology-based prediction. In this chapter, the strengths and drawbacks of each of these approaches is described through many examples of methods that predict secretion, integration into membranes, or subcellular locations in general. The aim of this chapter is to provide a user-level introduction to the field with a minimum of computational theory.

Predicting Alpha Helical Transmembrane Proteins Using HMMs

Alpha helical transmembrane (TM) proteins constitute an important structural class of membrane proteins involved in a wide variety of cellular functions. The prediction of their transmembrane topology, as well as their discrimination in newly sequenced genomes, is of great importance for the elucidation of their structure and function. Several methods have been applied for the prediction of the transmembrane segments and the topology of alpha helical transmembrane proteins utilizing different algorithmic techniques. Hidden Markov Models (HMMs) have been efficiently used in the development of several computational methods used for this task. In this chapter we give a brief review of different available prediction methods for alpha helical transmembrane proteins pointing out sequence and structural features that should be incorporated in a prediction method. We then describe the procedure of the design and development of a Hidden Markov Model capable of predicting the transmembrane alpha helices in proteins and discriminating them from globular proteins.

Predicting Subcellular Localization of Proteins by Bioinformatic Algorithms

When predicting the subcellular localization of proteins from their amino acid sequences, there are basically three approaches: signal-based, global property-based, and homology-based. Each of these has its advantages and drawbacks, and it is important when comparing methods to know which approach was used. Various statistical and machine learning algorithms are used with all three approaches, and various measures and standards are employed when reporting the performances of the developed methods. This chapter presents a number of available methods for prediction of sorting signals and subcellular localization, but rather than providing a checklist of which predictors to use, it aims to function as a guide for critical assessment of prediction methods.

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 ).

Systematic evaluation of CS-Rosetta for membrane protein structure prediction with sparse NOE restraints

We critically test and validate the CS-Rosetta methodology for de novo structure prediction of α-helical membrane proteins (MPs) from NMR data, such as chemical shifts and NOE distance restraints. By systematically reducing the number and types of NOE restraints, we focus on determining the regime in which MP structures can be reliably predicted and pinpoint the boundaries of the approach. Five MPs of known structure were used as test systems, phototaxis sensory rhodopsin II (pSRII), a subdomain of pSRII, disulfide binding protein B (DsbB), microsomal prostaglandin E2 synthase-1 (mPGES-1), and translocator protein (TSPO). For pSRII and DsbB, where NMR and X-ray structures are available, resolution-adapted structural recombination (RASREC) CS-Rosetta yields structures that are as close to the X-ray structure as the published NMR structures if all available NMR data are used to guide structure prediction. For mPGES-1 and Bacillus cereus TSPO, where only X-ray crystal structures are available, highly accurate structures are obtained using simulated NMR data. One main advantage of RASREC CS-Rosetta is its robustness with respect to even a drastic reduction of the number of NOEs. Close-to-native structures were obtained with one randomly picked long-range NOEs for every 14, 31, 38, and 8 residues for full-length pSRII, the pSRII subdomain, TSPO, and DsbB, respectively, in addition to using chemical shifts. For mPGES-1, atomically accurate structures could be predicted even from chemical shifts alone. Our results show that atomic level accuracy for helical membrane proteins is achievable with CS-Rosetta using very sparse NOE restraint sets to guide structure prediction. Proteins 2017; 85:812-826. © 2016 Wiley Periodicals, Inc.

Evaluation of Different N-Glycopeptide Enrichment Methods for N-Glycosylation Sites Mapping in Mouse Brain

N-Glycosylation of proteins plays a critical role in many biological pathways. Because highly heterogeneous N-glycopeptides are present in biological sources, the enrichment procedure is a crucial step for mass spectrometry analysis. Five enrichment methods, including IP-ZIC-HILIC, hydrazide chemistry, lectin affinity, ZIC-HILIC-FA, and TiO2 affinity were evaluated and compared in the study of mapping N-glycosylation sites in mouse brain. On the basis of our results, the identified N-glycosylation sites were 1891, 1241, 891, 869, and 710 and the FDR values were 3.29, 5.62, 9.54, 9.54, and 20.02%, respectively. Therefore, IP-ZIC-HILIC enrichment method displayed the highest sensitivity and specificity. In this work, we identified a total of 3446 unique glycosylation sites conforming to the N-glycosylation consensus motif (N-X-T/S/C; X ≠ P) with (18)O labeling in 1597 N-glycoproteins. N-glycosylation site information was used to confirm or correct the transmembrane topology of the 57 novel transmembrane N-glycoproteins.

A novel N-terminal region of the membrane β-hexosyltransferase: its role in secretion of soluble protein by Pichia pastoris

The β-hexosyltransferase (BHT) from Sporobolomyces singularis is a membrane-bound enzyme that catalyses transgalactosylation reactions to synthesize galacto-oligosaccharides (GOSs). To increase the secretion of the active soluble version of this protein, we examined the uncharacterized novel N-terminal region (amino acids 1-110), which included two predicted endogenous structural domains. The first domain (amino acids 1-22) may act as a classical leader while a non-classical signal was located within the remaining region (amino acids 23-110). A functional analysis of these domains was performed by evaluating the amounts of the rBHT forms secreted by recombinant P. pastoris strains carrying combinations of the predicted structural domains and the α mating factor (MFα) from Saccharomyces cerevisiae as positive control. Upon replacement of the leader domain (amino acids 1-22) by MFα (MFα-rBht(23-594)), protein secretion increased and activity of both soluble and membrane-bound enzymes was improved 53- and 14-fold, respectively. Leader interference was demonstrated when MFα preceded the putative classical rBHT(1-22) leader (amino acids 1-22), explaining the limited secretion of soluble protein by P. pastoris (GS115 : : MFα-rBht(1-594)). To validate the role of the N-terminal domains in promoting protein secretion, we tested the domains using a non-secreted protein, the anti-β-galactosidase single-chain variable antibody fragment scFv13R4. The recombinants carrying chimeras of the N-terminal 1-110 regions of rBHT preceding scFv13R4 correlated with the secretion strength of soluble protein observed with the rBHT recombinants. Finally, soluble bioactive HIS-tagged and non-tagged rBHT (purified to homogeneity) obtained from the most efficient recombinants (GS115 : : MFα-rBht(23-594)-HIS and GS115 : : MFα-rBht(23-594)) showed comparable activity rates of GOS generation.

Reverse Vaccinology: The Pathway from Genomes and Epitope Predictions to Tailored Recombinant Vaccines

In this chapter, we review the computational approaches that have led to a new generation of vaccines in recent years. There are many alternative routes to develop vaccines based on the technology of reverse vaccinology. We focus here on bacterial infectious diseases, describing the general workflow from bioinformatic predictions of antigens and epitopes down to examples where such predictions have been used successfully for vaccine development.

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.

Insights into the binding modes of CC chemokine receptor 4 (CCR4) inhibitors: a combined approach involving homology modelling, docking, and molecular dynamics simulation studies

CC chemokine receptor 4 (CCR4), a G protein-coupled receptor (GPCR), plays a vital role in the progression of asthma, T-cell lymphoma, inflammation, and Alzheimer's disease. To date, the structure of CCR4 has not been determined. Therefore, the nature of the interactions between inhibitors and CCR4 is not well known. In this study, we used CCR5 as a template to model the structure of CCR4. Docking studies were performed for four naphthalene-sulphonamide derivatives and crucial ligand-protein interactions were analysed. Molecular dynamics (MD) simulations of these complexes (100 ns each) were carried out to gain insights into the interactions between ligands and CCR4. MD simulations revealed that the residues identified by the docking were displaced and new residues were inserted near the ligands. Results of a principal component analysis (PCA) suggested that CCR4 unfolds at the extracellular site surrounding the ligands. Our simulations identified crucial residues involved in CCR4 antagonism, which were supported by previous mutational studies. Additionally, we identified Ser3.29, Leu3.33, Ser5.39, Phe6.47, Ile7.35, Thr7.38, Thr7.40, and Ala7.42 as residues that play crucial roles in CCR4 antagonism. Mutational studies will help elucidate the significance of these residues in CCR4 antagonism. An understanding of ligand-CCR4 interactions might aid in the design of novel CCR4 inhibitors.

Molecular modeling and ligand docking for solute carrier (SLC) transporters

Solute Carrier (SLC) transporters are membrane proteins that transport solutes, such as ions, metabolites, peptides, and drugs, across biological membranes, using diverse energy coupling mechanisms. In human, there are 386 SLC transporters, many of which contribute to the absorption, distribution, metabolism, and excretion of drugs and/or can be targeted directly by therapeutics. Recent atomic structures of SLC transporters determined by X-ray crystallography and NMR spectroscopy have significantly expanded the applicability of structure-based prediction of SLC transporter ligands, by enabling both comparative modeling of additional SLC transporters and virtual screening of small molecules libraries against experimental structures as well as comparative models. In this review, we begin by describing computational tools, including sequence analysis, comparative modeling, and virtual screening, that are used to predict the structures and functions of membrane proteins such as SLC transporters. We then illustrate the applications of these tools to predicting ligand specificities of select SLC transporters, followed by experimental validation using uptake kinetic measurements and other assays. We conclude by discussing future directions in the discovery of the SLC transporter ligands.

CLPTM1L promotes growth and enhances aneuploidy in pancreatic cancer cells

Genome-wide association studies (GWAS) of 10 different cancers have identified pleiotropic cancer predisposition loci across a region of chromosome 5p15.33 that includes the TERT and CLPTM1L genes. Of these, susceptibility alleles for pancreatic cancer have mapped to the CLPTM1L gene, thus prompting an investigation of the function of CLPTM1L in the pancreas. Immunofluorescence analysis indicated that CLPTM1L localized to the endoplasmic reticulum where it is likely embedded in the membrane, in accord with multiple predicted transmembrane domains. Overexpression of CLPTM1L enhanced growth of pancreatic cancer cells in vitro (1.3-1.5-fold; PDAY7 < 0.003) and in vivo (3.46-fold; PDAY68 = 0.039), suggesting a role in tumor growth; this effect was abrogated by deletion of two hydrophilic domains. Affinity purification followed by mass spectrometry identified an interaction between CLPTM1L and non-muscle myosin II (NMM-II), a protein involved in maintaining cell shape, migration, and cytokinesis. The two proteins colocalized in the cytoplasm and, after treatment with a DNA-damaging agent, at the centrosomes. Overexpression of CLPTM1L and depletion of NMM-II induced aneuploidy, indicating that CLPTM1L may interfere with normal NMM-II function in regulating cytokinesis. Immunohistochemical analysis revealed enhanced staining of CLPTM1L in human pancreatic ductal adenocarcinoma (n = 378) as compared with normal pancreatic tissue samples (n = 17; P = 1.7 × 10(-4)). Our results suggest that CLPTM1L functions as a growth-promoting gene in the pancreas and that overexpression may lead to an abrogation of normal cytokinesis, indicating that it should be considered as a plausible candidate gene that could explain the effect of pancreatic cancer susceptibility alleles on chr5p15.33.

Membrane transport piece by piece: production of transmembrane peptides for structural and functional studies

Membrane proteins are involved in all cellular processes from signaling cascades to nutrient uptake and waste disposal. Because of these essential functions, many membrane proteins are recognized as important, yet elusive, clinical targets. Recent advances in structural biology have answered many questions about how membrane proteins function, yet one of the major bottlenecks remains the ability to obtain sufficient quantities of pure and homogeneous protein. This is particularly true for human membrane proteins, where novel expression strategies and structural techniques are needed to better characterize their function and therapeutic potential. One way to approach this challenge is to determine the structure of smaller pieces of membrane proteins that can be assembled into models of the complete protein. This unit describes the rationale for working with single or multiple transmembrane segments and provides a description of strategies and methods to express and purify them for structural and functional studies using a maltose binding protein (MBP) fusion. The bulk of the unit outlines a detailed methodology and justification for producing these peptides under native-like conditions.

Small-scale expression of proteins in E. coli

Proteins participate in virtually every cellular activity, and a knowledge of protein function is essential for an understanding of biological systems. However, protein diversity necessitates the application of an array of in vivo and in vitro approaches for characterization of the functional and biochemical properties of proteins. Methods that enable production of proteins for in vitro studies are critical for determination of the molecular, kinetic, and thermodynamic properties of these molecules. Ideally, proteins could be purified from the original source; however, the native host is often unsuitable for a number of reasons. Consequently, systems for heterologous protein production are commonly used to produce large amounts of protein. Heterologous expression hosts are chosen using a number of criteria, including genetic tractability, advantageous production or processing characteristics (secretion or posttranslational modifications), or economy of time and growth requirements. The subcloning process also provides an opportunity to introduce purification tags, epitope tags, fusions, truncations, and mutations into the coding sequence that may be useful in downstream purification or characterization applications. Bacterial systems for heterologous protein expression have advantages in ease of use, cost, short generation times, and scalability. These expression systems have been widely used by high-throughput protein production projects and often represent an initial experiment for any expression target. Escherichia coli has been studied for many years as a model bacterial organism and is one of the most popular hosts for heterologous protein expression (Terpe, 2006). Its protein production capabilities have been intensively studied, and the ease of genetic manipulation in this organism has led to the development of strains engineered exclusively for use in protein expression. These resources are widely available from commercial sources and public repositories. Despite these advantages, many targets are unsuitable for expression in E. coli, and attempts will not yield protein that can be utilized in downstream applications. A thorough understanding of the protein target, the requirements of the final application, and available tools are all essential for planning a successful expression experiment. This protocol is designed to optimize expression and solubility using an E. coli host and expression vector with an IPTG-inducible T7 promoter. The general features of the method are easily extended to other organisms and expression systems. Small-scale expression cultures are used to identify the optimum expression parameters for a given target. Thorough analysis of the total cell content and soluble fraction is used to screen out failed targets and those unlikely to succeed in large-scale purification cultures. The protocol listed here can be used in individual tubes for a small number of targets or adapted for use in 48-well plates for high throughput applications (Abdullah et al., 2009). Using the same culture for initial expression analysis and solubility analysis reduces variability between expression trials and saves the time required to produce separate cultures.

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

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

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.

Simultaneous prediction of protein secondary structure and transmembrane spans

Prediction of transmembrane spans and secondary structure from the protein sequence is generally the first step in the structural characterization of (membrane) proteins. Preference of a stretch of amino acids in a protein to form secondary structure and being placed in the membrane are correlated. Nevertheless, current methods predict either secondary structure or individual transmembrane states. We introduce a method that simultaneously predicts the secondary structure and transmembrane spans from the protein sequence. This approach not only eliminates the necessity to create a consensus prediction from possibly contradicting outputs of several predictors but bears the potential to predict conformational switches, i.e., sequence regions that have a high probability to change for example from a coil conformation in solution to an α-helical transmembrane state. An artificial neural network was trained on databases of 177 membrane proteins and 6048 soluble proteins. The output is a 3 × 3 dimensional probability matrix for each residue in the sequence that combines three secondary structure types (helix, strand, coil) and three environment types (membrane core, interface, solution). The prediction accuracies are 70.3% for nine possible states, 73.2% for three-state secondary structure prediction, and 94.8% for three-state transmembrane span prediction. These accuracies are comparable to state-of-the-art predictors of secondary structure (e.g., Psipred) or transmembrane placement (e.g., OCTOPUS). The method is available as web server and for download at www.meilerlab.org.

Membrane protein native state discrimination by implicit membrane models

Four implicit membrane models [IMM1, generalized Born (GB)-surface area-implicit membrane (GBSAIM), GB with a simple switching (GBSW), and heterogeneous dielectric GB (HDGB)] were tested for their ability to discriminate the native conformation of five membrane proteins from 450 decoys generated by the Rosetta-Membrane program. The energy ranking of the native state and Z-scores were used to assess the performance of the models. The effect of membrane thickness was examined and was found to be substantial. Quite satisfactory discrimination was achieved with the all-atom IMM1 and GBSW models at 25.4 Å thickness and with the HDGB model at 28.5 Å thickness. The energy components by themselves were not discriminative. Both van der Waals and electrostatic interactions contributed to native state discrimination, to a different extent in each model. Computational efficiency of the models decreased in the order: extended-atom IMM1 > all-atom IMM1 > GBSAIM > GBSW > HDGB. These results encourage the further development and use of implicit membrane models for membrane protein structure prediction.

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.

Design of transmembrane peptides: coping with sticky situations

Membrane proteins have central roles in cellular processes ranging from nutrient uptake to cell-cell communication, and are key drug targets. However, research on α-helical integral membrane proteins is in its relative infancy vs. water-soluble proteins, largely because of their water insolubility when extracted from their native membrane environment. Peptides with sequences that correspond to the membrane-spanning segments of α-helical integral membrane proteins, termed transmembrane (TM) peptides, provide valuable tools for the characterization of these molecules. Here we describe in detail protocols for the design of TM peptides from the sequences of natural α-helical integral membrane proteins and outline strategies for their synthesis and for improving their solubility properties.

The simulation approach to lipid-protein interactions

The interactions between lipids and proteins are crucial for a range of biological processes, from the folding and stability of membrane proteins to signaling and metabolism facilitated by lipid-binding proteins. However, high-resolution structural details concerning functional lipid/protein interactions are scarce due to barriers in both experimental isolation of native lipid-bound complexes and subsequent biophysical characterization. The molecular dynamics (MD) simulation approach provides a means to complement available structural data, yielding dynamic, structural, and thermodynamic data for a protein embedded within a physiologically realistic, modelled lipid environment. In this chapter, we provide a guide to current methods for setting up and running simulations of membrane proteins and soluble, lipid-binding proteins, using standard atomistically detailed representations, as well as simplified, coarse-grained models. In addition, we outline recent studies that illustrate the power of the simulation approach in the context of biologically relevant lipid/protein interactions.

A dominant negative heterozygous G87R mutation in the zinc transporter, ZnT-2 (SLC30A2), results in transient neonatal zinc deficiency

Zinc is an essential mineral, and infants are particularly vulnerable to zinc deficiency as they require large amounts of zinc for their normal growth and development. We have recently described the first loss-of-function mutation (H54R) in the zinc transporter ZnT-2 (SLC30A2) in mothers with infants harboring transient neonatal zinc deficiency (TNZD). Here we identified and characterized a novel heterozygous G87R ZnT-2 mutation in two unrelated Ashkenazi Jewish mothers with infants displaying TNZD. Transient transfection of G87R ZnT-2 resulted in endoplasmic reticulum-Golgi retention, whereas the WT transporter properly localized to intracellular secretory vesicles in HC11 and MCF-7 cells. Consequently, G87R ZnT-2 showed decreased stability compared with WT ZnT-2 as revealed by Western blot analysis. Three-dimensional homology modeling based on the crystal structure of YiiP, a close zinc transporter homologue from Escherichia coli, revealed that the basic arginine residue of the mutant G87R points toward the membrane lipid core, suggesting misfolding and possible loss-of-function. Indeed, functional assays including vesicular zinc accumulation, zinc secretion, and cytoplasmic zinc pool assessment revealed markedly impaired zinc transport in G87R ZnT-2 transfectants. Moreover, co-transfection experiments with both mutant and WT transporters revealed a dominant negative effect of G87R ZnT-2 over the WT ZnT-2; this was associated with mislocalization, decreased stability, and loss of zinc transport activity of the WT ZnT-2 due to homodimerization observed upon immunoprecipitation experiments. These findings establish that inactivating ZnT-2 mutations are an underlying basis of TNZD and provide the first evidence for the dominant inheritance of heterozygous ZnT-2 mutations via negative dominance due to homodimer formation.

Structural genomics plucks high-hanging membrane proteins

Recent years have seen the establishment of structural genomics centers that explicitly target integral membrane proteins. Here, we review the advances in targeting these extremely high-hanging fruits of structural biology in high-throughput mode. We observe that the experimental determination of high-resolution structures of integral membrane proteins is increasingly successful both in terms of getting structures and of covering important protein families, for example, from Pfam. Structural genomics has begun to contribute significantly toward this progress. An important component of this contribution is the set up of robotic pipelines that generate a wealth of experimental data for membrane proteins. We argue that prediction methods for the identification of membrane regions and for the comparison of membrane proteins largely suffice to meet the challenges of target selection for structural genomics of membrane proteins. In contrast, we need better methods to prioritize the most promising members in a family of closely related proteins and to annotate protein function from sequence and structure in absence of homology.

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.

Identification and localization of Myxococcus xanthus porins and lipoproteins

Myxococcus xanthus DK1622 contains inner (IM) and outer membranes (OM) separated by a peptidoglycan layer. Integral membrane, β-barrel proteins are found exclusively in the OM where they form pores allowing the passage of nutrients, waste products and signals. One porin, Oar, is required for intercellular communication of the C-signal. An oar mutant produces CsgA but is unable to ripple or stimulate csgA mutants to develop suggesting that it is the channel for C-signaling. Six prediction programs were evaluated for their ability to identify β-barrel proteins. No program was reliable unless the predicted proteins were first parsed using Signal P, Lipo P and TMHMM, after which TMBETA-SVM and TMBETADISC-RBF identified β-barrel proteins most accurately. 228 β-barrel proteins were predicted from among 7331 protein coding regions, representing 3.1% of total genes. Sucrose density gradients were used to separate vegetative cell IM and OM fractions, and LC-MS/MS of OM proteins identified 54 β-barrel proteins. Another class of membrane proteins, the lipoproteins, are anchored in the membrane via a lipid moiety at the N-terminus. 44 OM proteins identified by LC-MS/MS were predicted lipoproteins. Lipoproteins are distributed between the IM, OM and ECM according to an N-terminal sorting sequence that varies among species. Sequence analysis revealed conservation of alanine at the +7 position of mature ECM lipoproteins, lysine at the +2 position of IM lipoproteins, and no noticable conservation within the OM lipoproteins. Site directed mutagenesis and immuno transmission electron microscopy showed that alanine at the +7 position is essential for sorting of the lipoprotein FibA into the ECM. FibA appears at normal levels in the ECM even when a +2 lysine is added to the signal sequence. These results suggest that ECM proteins have a unique method of secretion. It is now possible to target lipoproteins to specific IM, OM and ECM locations by manipulating the amino acid sequence near the +1 cysteine processing site.

Membrane topological structure of neutral system N/A amino acid transporter 4 (SNAT4) protein

Members of system N/A amino acid transporter (SNAT) family mediate transport of neutral amino acids, including l-alanine, l-glutamine, and l-histidine, across the plasma membrane and are involved in a variety of cellular functions. By using chemical labeling, glycosylation, immunofluorescence combined with molecular modeling approaches, we resolved the membrane topological structure of SNAT4, a transporter expressed predominantly in liver. To analyze the orientation using the chemical labeling and biotinylation approach, the "Cys-null" mutant of SNAT4 was first generated by mutating all five endogenous cysteine residues. Based on predicted topological structures, a single cysteine residue was introduced individually into all possible nontransmembrane domains of the Cys-null mutant. The cells expressing these mutants were labeled with N-biotinylaminoethyl methanethiosulfonate, a membrane-impermeable cysteine-directed reagent. We mapped the orientations of N- and C-terminal domains. There are three extracellular loop domains, and among them, the second loop domain is the largest that spans from amino acid residue ∼242 to ∼335. The orientation of this domain was further confirmed by the identification of two N-glycosylated residues, Asn-260 and Asn-264. Together, we showed that SNAT4 contains 10 transmembrane domains with extracellular N and C termini and a large N-glycosylated, extracellular loop domain. This is the first report concerning membrane topological structure of mammalian SNAT transporters, which will provide important implications for our understanding of structure-function of the members in this amino acid transporter family.

Amino acid interaction preferences in helical membrane proteins

Membrane proteins are involved in a number of important biological functions. Yet, they are poorly understood from the structure and folding point of view. The external environment being drastically different from that of globular proteins, the intra-protein interactions in membrane proteins are also expected to be different. Hence, statistical potentials representing the features of inter-residue interactions based exclusively on the structures of membrane proteins are much needed. Currently, a reasonable number of structures are available, making it possible to undertake such an analysis on membrane proteins. In this study we have examined the inter-residue interaction propensities of amino acids in the membrane spanning regions of the alpha-helical membrane (HM) proteins. Recently we have shown that valuable information can be obtained on globular proteins by the evaluation of the pair-wise interactions of amino acids by classifying them into different structural environments, based on factors such as the secondary structure or the number of contacts that a residue can make. Here we have explored the possible ways of classifying the intra-protein environment of HM proteins and have developed scoring functions based on different classification schemes. On evaluation of different schemes, we find that the scheme which classifies amino acids to different intra-contact environment is the most promising one. Based on this classification scheme, we also redefine the hydrophobicity scale of amino acids in HM proteins.

A new in-silico method for determination of helical transmembrane domains based on the PepLook scan: application to IL-2Rβ and IL-2Rγc receptor chains

BACKGROUND

Modeling of transmembrane domains (TMDs) requires correct prediction of interfacial residues for in-silico modeling and membrane insertion studies. This implies the defining of a target sequence long enough to contain interfacial residues. However, too long sequences induce artifactual polymorphism: within tested modeling methods, the longer the target sequence, the more variable the secondary structure, as though the procedure were stopped before the end of the calculation (which may in fact be unreachable). Moreover, delimitation of these TMDs can produce variable results with sequence based two-dimensional prediction methods, especially for sequences showing polymorphism. To solve this problem, we developed a new modeling procedure using the PepLook method. We scanned the sequences by modeling peptides from the target sequence with a window of 19 residues.

RESULTS

Using sequences whose NMR-structures are already known (GpA, EphA1 and Erb2-HER2), we first determined that the hydrophobic to hydrophilic accessible surface area ratio (ASAr) was the best criterion for delimiting the TMD sequence. The length of the helical structure and the Impala method further supported the determination of the TMD limits. This method was applied to the IL-2Rβ and IL-2Rγ TMD sequences of Homo sapiens, Rattus norvegicus, Mus musculus and Bos taurus.

CONCLUSIONS

We succeeded in reducing the variation in the TMD limits to only 2 residues and in gaining structural information.

Mapping the protein-protein interface between a toxin and its cognate antitoxin from the bacterial pathogen Streptococcus pyogenes

Protein--protein interactions are ubiquitous and essential for most biological processes. Although new proteomic technologies have generated large catalogs of interacting proteins, considerably less is known about these interactions at the molecular level, information that would aid in predicting protein interactions, designing therapeutics to alter these interactions, and understanding the effects of disease-producing mutations. Here we describe mapping the interacting surfaces of the bacterial toxin SPN (Streptococcus pyogenes NAD(+) hydrolase) in complex with its antitoxin IFS (immunity factor for SPN) by using hydrogen-deuterium amide exchange and electrospray ionization mass spectrometry. This approach affords data in a relatively short time for small amounts of protein, typically 5-7 pmol per analysis. The results show a good correspondence with a recently determined crystal structure of the IFS--SPN complex but additionally provide strong evidence for a folding transition of the IFS protein that accompanies its binding to SPN. The outcome shows that mass-based chemical footprinting of protein interaction surfaces can provide information about protein dynamics that is not easily obtained by other methods and can potentially be applied to large, multiprotein complexes that are out of range for most solution-based methods of biophysical analysis.

A folding pathway-dependent score to recognize membrane proteins

While various approaches exist to study protein localization, it is still a challenge to predict where proteins localize. Here, we consider a mechanistic viewpoint for membrane localization. Taking into account the steps for the folding pathway of α-helical membrane proteins and relating biophysical parameters to each of these steps, we create a score capable of predicting the propensity for membrane localization and call it FP(3)mem. This score is driven from the principal component analysis (PCA) of the biophysical parameters related to membrane localization. FP(3)mem allows us to rationalize the colocalization of a number of channel proteins with the Cav1.2 channel by their fewer propensities for membrane localization.