Memoir: template-based structure prediction for membrane proteins

Challenges of Protein-Protein Docking of the Membrane Proteins

Despite the recent advances in the determination of high-resolution membrane protein (MP) structures, the structural and functional characterization of MPs remains extremely challenging, mainly due to the hydrophobic nature, low abundance, poor expression, purification, and crystallization difficulties associated with MPs. Whereby the major challenges/hurdles for MP structure determination are associated with the expression, purification, and crystallization procedures. Although there have been significant advances in the experimental determination of MP structures, only a limited number of MP structures (approximately less than 1% of all) are available in the Protein Data Bank (PDB). Therefore, the structures of a large number of MPs still remain unresolved, which leads to the availability of widely unplumbed structural and functional information related to MPs. As a result, recent developments in the drug discovery realm and the significant biological contemplation have led to the development of several novel, low-cost, and time-efficient computational methods that overcome the limitations of experimental approaches, supplement experiments, and provide alternatives for the characterization of MPs. Whereby the fine tuning and optimizations of these computational approaches remains an ongoing endeavor.Computational methods offer a potential way for the elucidation of structural features and the augmentation of currently available MP information. However, the use of computational modeling can be extremely challenging for MPs mainly due to insufficient knowledge of (or gaps in) atomic structures of MPs. Despite the availability of numerous in silico methods for 3D structure determination the applicability of these methods to MPs remains relatively low since all methods are not well-suited or adequate for MPs. However, sophisticated methods for MP structure predictions are constantly being developed and updated to integrate the modifications required for MPs. Currently, different computational methods for (1) MP structure prediction, (2) stability analysis of MPs through molecular dynamics simulations, (3) modeling of MP complexes through docking, (4) prediction of interactions between MPs, and (5) MP interactions with its soluble partner are extensively used. Towards this end, MP docking is widely used. It is notable that the MP docking methods yet few in number might show greater potential in terms of filling the knowledge gap. In this chapter, MP docking methods and associated challenges have been reviewed to improve the applicability, accuracy, and the ability to model macromolecular complexes.

Deep transfer learning for inter-chain contact predictions of transmembrane protein complexes

Membrane proteins are encoded by approximately a quarter of human genes. Inter-chain residue-residue contact information is important for structure prediction of membrane protein complexes and valuable for understanding their molecular mechanism. Although many deep learning methods have been proposed to predict the intra-protein contacts or helix-helix interactions in membrane proteins, it is still challenging to accurately predict their inter-chain contacts due to the limited number of transmembrane proteins. Addressing the challenge, here we develop a deep transfer learning method for predicting inter-chain contacts of transmembrane protein complexes, named DeepTMP, by taking advantage of the knowledge pre-trained from a large data set of non-transmembrane proteins. DeepTMP utilizes a geometric triangle-aware module to capture the correct inter-chain interaction from the coevolution information generated by protein language models. DeepTMP is extensively evaluated on a test set of 52 self-associated transmembrane protein complexes, and compared with state-of-the-art methods including DeepHomo2.0, CDPred, GLINTER, DeepHomo, and DNCON2_Inter. It is shown that DeepTMP considerably improves the precision of inter-chain contact prediction and outperforms the existing approaches in both accuracy and robustness.

Homology Modeling of Transporter Proteins

Membrane transporter proteins are divided into channels/pores and carriers and constitute protein families of physiological and pharmacological importance. Several presently used therapeutic compounds elucidate their effects by targeting membrane transporter proteins, including anti-arrhythmic, anesthetic, antidepressant, anxiolytic and diuretic drugs. The lack of three-dimensional structures of human transporters hampers experimental studies and drug discovery. In this chapter, the use of homology modeling for generating structural models of membrane transporter proteins is reviewed. The increasing number of atomic resolution structures available as templates, together with improvements in methods and algorithms for sequence alignments, secondary structure predictions, and model generation, in addition to the increase in computational power have increased the applicability of homology modeling for generating structural models of transporter proteins. Different pitfalls and hints for template selection, multiple-sequence alignments, generation and optimization, validation of the models, and the use of transporter homology models for structure-based virtual ligand screening are discussed.

Protein Structure Prediction: Conventional and Deep Learning Perspectives

Protein structure prediction is a way to bridge the sequence-structure gap, one of the main challenges in computational biology and chemistry. Predicting any protein's accurate structure is of paramount importance for the scientific community, as these structures govern their function. Moreover, this is one of the complicated optimization problems that computational biologists have ever faced. Experimental protein structure determination methods include X-ray crystallography, Nuclear Magnetic Resonance Spectroscopy and Electron Microscopy. All of these are tedious and time-consuming procedures that require expertise. To make the process less cumbersome, scientists use predictive tools as part of computational methods, using data consolidated in the protein repositories. In recent years, machine learning approaches have raised the interest of the structure prediction community. Most of the machine learning approaches for protein structure prediction are centred on co-evolution based methods. The accuracy of these approaches depends on the number of homologous protein sequences available in the databases. The prediction problem becomes challenging for many proteins, especially those without enough sequence homologs. Deep learning methods allow for the extraction of intricate features from protein sequence data without making any intuitions. Accurately predicted protein structures are employed for drug discovery, antibody designs, understanding protein-protein interactions, and interactions with other molecules. This article provides a review of conventional and deep learning approaches in protein structure prediction. We conclude this review by outlining a few publicly available datasets and deep learning architectures currently employed for protein structure prediction tasks.

Guardians of the Cell: State-of-the-Art of Membrane Proteins from a Computational Point-of-View

Membrane proteins (MPs) encompass a large family of proteins with distinct cellular functions, and although representing over 50% of existing pharmaceutical drug targets, their structural and functional information is still very scarce. Over the last years, in silico analysis and algorithm development were essential to characterize MPs and overcome some limitations of experimental approaches. The optimization and improvement of these methods remain an ongoing process, with key advances in MPs' structure, folding, and interface prediction being continuously tackled. Herein, we discuss the latest trends in computational methods toward a deeper understanding of the atomistic and mechanistic details of MPs.

Integrative modeling of membrane-associated protein assemblies

Membrane proteins are among the most challenging systems to study with experimental structural biology techniques. The increased number of deposited structures of membrane proteins has opened the route to modeling their complexes by methods such as docking. Here, we present an integrative computational protocol for the modeling of membrane-associated protein assemblies. The information encoded by the membrane is represented by artificial beads, which allow targeting of the docking toward the binding-competent regions. It combines efficient, artificial intelligence-based rigid-body docking by LightDock with a flexible final refinement with HADDOCK to remove potential clashes at the interface. We demonstrate the performance of this protocol on eighteen membrane-associated complexes, whose interface lies between the membrane and either the cytosolic or periplasmic regions. In addition, we provide a comparison to another state-of-the-art docking software, ZDOCK. This protocol should shed light on the still dark fraction of the interactome consisting of membrane proteins.

MCART1/SLC25A51 is required for mitochondrial NAD transport

The nicotinamide adenine dinucleotide (NAD⁺/NADH) pair is a cofactor in redox reactions and is particularly critical in mitochondria as it connects substrate oxidation by the tricarboxylic acid (TCA) cycle to adenosine triphosphate generation by the electron transport chain (ETC) and oxidative phosphorylation. While a mitochondrial NAD⁺ transporter has been identified in yeast, how NAD enters mitochondria in metazoans is unknown. Here, we mine gene essentiality data from human cell lines to identify MCART1 (SLC25A51) as coessential with ETC components. MCART1-null cells have large decreases in TCA cycle flux, mitochondrial respiration, ETC complex I activity, and mitochondrial levels of NAD⁺ and NADH. Isolated mitochondria from cells lacking or overexpressing MCART1 have greatly decreased or increased NAD uptake in vitro, respectively. Moreover, MCART1 and NDT1, a yeast mitochondrial NAD⁺ transporter, can functionally complement for each other. Thus, we propose that MCART1 is the long sought mitochondrial transporter for NAD in human cells.

Genetic and functional analysis of a Pacific hagfish opioid system

The actions of endogenous opioids and nociceptin/orphanin FQ are mediated by four homologous G protein-coupled receptors that constitute the opioid receptor family. However, little is known about opioid systems in cyclostomes (living jawless fish) and how opioid systems might have evolved from invertebrates. Here, we leveraged de novo transcriptome and low-coverage whole-genome assembly in the Pacific hagfish (Eptatretus stoutii) to identify and characterize the first full-length coding sequence for a functional opioid receptor in a cyclostome. Additionally, we define two novel endogenous opioid precursors in this species that predict several novel opioid peptides. Bioinformatic analysis shows no closely related opioid receptor genes in invertebrates with regard either to the genomic organization or to conserved opioid receptor-specific sequences that are common in all vertebrates. Furthermore, no proteins analogous to vertebrate opioid precursors could be identified by genomic searches despite previous claims of protein or RNA-derived sequences in several invertebrate species. The presence of an expressed orthologous receptor and opioid precursors in the Pacific hagfish confirms that a functional opioid system was likely present in the common ancestor of all extant vertebrates some 550 million years ago, earlier than all previous authenticated accounts. We discuss the premise that the cyclostome and vertebrate opioid systems evolved from invertebrate systems concerned with antimicrobial defense and speculate that the high concentrations of opioid precursors in tissues such as the testes, gut, and activated immune cells are key remnants of this evolutionary role.

An assessment of water placement algorithms in quantum mechanics/molecular mechanics modeling: the case of rhodopsins' first spectral absorption band maxima

Quantum mechanics/molecular mechanics (QM/MM) models are a widely used tool to obtain detailed insight into the properties and functioning of proteins. The outcome of QM/MM studies heavily depends on the quality of the applied QM/MM model. Prediction and right placement of internal water molecules in protein cavities is one of the critical parts of any QM/MM model construction. Herein, we performed a systematic study of four protein hydration algorithms. We tested these algorithms for their ability to predict X-ray-resolved water molecules for a set of membrane photosensitive rhodopsin proteins, as well as the influence of the applied water placement algorithms on the QM/MM calculated absorption maxima (λ_max) of these proteins. We used 49 rhodopsins and their intermediates with available X-ray structures as the test set. We found that a proper choice of hydration algorithms and setups is needed to predict functionally important water molecules in the chromophore-binding cavity of rhodopsins, such as the water cluster in the N-H region of bacteriorhodopsin or two water molecules in the binding pocket of bovine visual rhodopsin. The QM/MM calculated λ_max of rhodopsins is also quite sensitive to the applied protein hydration protocols. The best methodology allows obtaining an 18.0 nm average value for the absolute deviation of the calculated λ_max from the experimental λ_max. Although the major effect of water molecules on λ_max originates from the water molecules located in the binding pocket, the water molecules outside the binding pocket also affect the calculated λ_max mainly by causing a reorganization of the protein structure. The results reported in this study can be used for the evaluation and further development of hydration methodologies, in general, and rhodopsin QM/MM models, in particular.

Identification of lipid A deacylase as a novel, highly conserved and protective antigen against enterohemorrhagic Escherichia coli

Enterohemorrhagic E. coli (EHEC) is a major cause of large outbreaks worldwide associated with hemorrhagic colitis and hemolytic uremic syndrome. While vaccine development is warranted, a licensed vaccine, specific for human use, against EHEC is not yet available. In this study, the reverse vaccinology approach combined with genomic, transcriptional and molecular epidemiology data was applied on the EHEC O157:H7 genome to select new potential vaccine candidates. Twenty-four potential protein antigens were identified and one of them (MC001) was successfully expressed onto Generalized Modules for Membrane Antigens (GMMA) delivery system. GMMA expressing this vaccine candidate was immunogenic, raising a specific antibody response. Immunization with the MC001 candidate was able to reduce the bacterial load of EHEC O157:H7 strain in feces, colon and caecum tissues after murine infection. MC001 is homologue to lipid A deacylase enzyme (LpxR), and to our knowledge, this is the first study describing it as a potential vaccine candidate. Gene distribution and sequence variability analysis showed that MC001 is present and conserved in EHEC and in enteropathogenic E. coli (EPEC) strains. Given the high genetic variability among and within E. coli pathotypes, the identification of such conserved antigen suggests that its inclusion in a vaccine might represent a solution against major intestinal pathogenic strains.

Alterations in ZnT1 expression and function lead to impaired intracellular zinc homeostasis in cancer

Zinc is vital for the structure and function of ~3000 human proteins and hence plays key physiological roles. Consequently, impaired zinc homeostasis is associated with various human diseases including cancer. Intracellular zinc levels are tightly regulated by two families of zinc transporters: ZIPs and ZnTs; ZIPs import zinc into the cytosol from the extracellular milieu, or from the lumen of organelles into the cytoplasm. In contrast, the vast majority of ZnTs compartmentalize zinc within organelles, whereas the ubiquitously expressed ZnT1 is the sole zinc exporter. Herein, we explored the hypothesis that qualitative and quantitative alterations in ZnT1 activity impair cellular zinc homeostasis in cancer. Towards this end, we first used bioinformatics to analyze inactivating mutations in ZIPs and ZNTs, catalogued in the COSMIC and gnomAD databases, representing tumor specimens and healthy population controls, respectively. ZnT1, ZnT10, ZIP8, and ZIP10 showed extremely high rates of loss of function mutations in cancer as compared to healthy controls. Analysis of the putative functional impact of missense mutations in ZnT1-ZnT10 and ZIP1-ZIP14, using homologous protein alignment and structural predictions, revealed that ZnT1 displays a markedly increased frequency of predicted functionally deleterious mutations in malignant tumors, as compared to a healthy population. Furthermore, examination of ZnT1 expression in 30 cancer types in the TCGA database revealed five tumor types with significant ZnT1 overexpression, which predicted dismal prognosis for cancer patient survival. Novel functional zinc transport assays, which allowed for the indirect measurement of cytosolic zinc levels, established that wild type ZnT1 overexpression results in low intracellular zinc levels. In contrast, overexpression of predicted deleterious ZnT1 missense mutations did not reduce intracellular zinc levels, validating eight missense mutations as loss of function (LoF) mutations. Thus, alterations in ZnT1 expression and LoF mutations in ZnT1 provide a molecular mechanism for impaired zinc homeostasis in cancer formation and/or progression.

Quantum Mechanical and Molecular Mechanics Modeling of Membrane-Embedded Rhodopsins

Computational chemistry provides versatile methods for studying the properties and functioning of biological systems at different levels of precision and at different time scales. The aim of this article is to review the computational methodologies that are applicable to rhodopsins as archetypes for photoactive membrane proteins that are of great importance both in nature and in modern technologies. For each class of computational techniques, from methods that use quantum mechanics for simulating rhodopsin photophysics to less-accurate coarse-grained methodologies used for long-scale protein dynamics, we consider possible applications and the main directions for improvement.

Dissection of Protonation Sites for Antibacterial Recognition and Transport in QacA, a Multi-Drug Efflux Transporter

QacA is a drug:H⁺ antiporter with 14 transmembrane helices that confers antibacterial resistance to methicillin-resistant Staphylococcus aureus strains, with homologs in other pathogenic organisms. It is a highly promiscuous antiporter, capable of H⁺-driven efflux of a wide array of cationic antibacterial compounds and dyes. Our study, using a homology model of QacA, reveals a group of six protonatable residues in its vestibule. Systematic mutagenesis resulted in the identification of D34 (TM1), and a cluster of acidic residues in TM13 including E407 and D411 and D323 in TM10, as being crucial for substrate recognition and transport of monovalent and divalent cationic antibacterial compounds. The transport and binding properties of QacA and its mutants were explored using whole cells, inside-out vesicles, substrate-induced H⁺ release and microscale thermophoresis-based assays. The activity of purified QacA was also observed using proteoliposome-based substrate-induced H⁺ transport assay. Our results identify two sites, D34 and D411 as vital players in substrate recognition, while E407 facilitates substrate efflux as a protonation site. We also observe that E407 plays an additional role as a substrate recognition site for the transport of dequalinium, a divalent quaternary ammonium compound. These observations rationalize the promiscuity of QacA for diverse substrates. The study unravels the role of acidic residues in QacA with implications for substrate recognition, promiscuity and processive transport in multidrug efflux transporters, related to QacA.

Crystal structure of the plant symporter STP10 illuminates sugar uptake mechanism in monosaccharide transporter superfamily

Plants are dependent on controlled sugar uptake for correct organ development and sugar storage, and apoplastic sugar depletion is a defense strategy against microbial infections like rust and mildew. Uptake of glucose and other monosaccharides is mediated by Sugar Transport Proteins, proton-coupled symporters from the Monosaccharide Transporter (MST) superfamily. We present the 2.4 Å structure of Arabidopsis thaliana high affinity sugar transport protein, STP10, with glucose bound. The structure explains high affinity sugar recognition and suggests a proton donor/acceptor pair that links sugar transport to proton translocation. It contains a Lid domain, conserved in all STPs, that locks the mobile transmembrane domains through a disulfide bridge, and creates a protected environment which allows efficient coupling of the proton gradient to drive sugar uptake. The STP10 structure illuminates fundamental principles of sugar transport in the MST superfamily with implications for both plant antimicrobial defense, organ development and sugar storage.

Demonstrating aspects of multiscale modeling by studying the permeation pathway of the human ZnT2 zinc transporter

Multiscale modeling provides a very powerful means of studying complex biological systems. An important component of this strategy involves coarse-grained (CG) simplifications of regions of the system, which allow effective exploration of complex systems. Here we studied aspects of CG modeling of the human zinc transporter ZnT2. Zinc is an essential trace element with 10% of the proteins in the human proteome capable of zinc binding. Thus, zinc deficiency or impairment of zinc homeostasis disrupt key cellular functions. Mammalian zinc transport proceeds via two transporter families: ZnT and ZIP; however, little is known about the zinc permeation pathway through these transporters. As a step towards this end, we herein undertook comprehensive computational analyses employing multiscale techniques, focusing on the human zinc transporter ZnT2 and its bacterial homologue, YiiP. Energy calculations revealed a favorable pathway for zinc translocation via alternating access. We then identified key residues presumably involved in the passage of zinc ions through ZnT2 and YiiP, and functionally validated their role in zinc transport using site-directed mutagenesis of ZnT2 residues. Finally, we use a CG Monte Carlo simulation approach to sample the transition between the inward-facing and the outward-facing states. We present our structural models of the inward- and outward-facing conformations of ZnT2 as a blueprint prototype of the transporter conformations, including the putative permeation pathway and participating residues. The insights gained from this study may facilitate the delineation of the pathways of other zinc transporters, laying the foundations for the molecular basis underlying ion permeation. This may possibly facilitate the development of therapeutic interventions in pathological states associated with zinc deficiency and other disorders based on loss-of-function mutations in solute carriers.

A Comparative Study of Modern Homology Modeling Algorithms for Rhodopsin Structure Prediction

Rhodopsins are seven α-helical membrane proteins that are of great importance in chemistry, biology, and modern biotechnology. Any in silico study on rhodopsin properties and functioning requires a high-quality three-dimensional structure. Due to particular difficulties with obtaining membrane protein structures from the experiment, in silico prediction of the three-dimensional rhodopsin structure based only on its primary sequence is an especially important task. For the last few years, significant progress was made in the field of protein structure prediction, especially for methods based on comparative modeling. However, the majority of this progress was made for soluble proteins and further investigations are needed to achieve similar progress for membrane proteins. In this paper, we evaluate the performance of modern protein structure prediction methodologies (implemented in the Medeller, I-TASSER, and Rosetta packages) for their ability to predict rhodopsin structures. Three widely used methodologies were considered: two general methodologies that are commonly applied to soluble proteins and a methodology that uses constraints that are specific for membrane proteins. The test pool consisted of 36 target-template pairs with different sequence similarities that was constructed on the basis of 24 experimental rhodopsin structures taken from the RCSB database. As a result, we showed that all three considered methodologies allow obtaining rhodopsin structures with the quality that is close to the crystallographic one (root mean square deviation (RMSD) of the predicted structure from the corresponding X-ray structure up to 1.5 Å) if the target-template sequence identity is higher than 40%. Moreover, all considered methodologies provided structures of average quality (RMSD < 4.0 Å) if the target-template sequence identity is higher than 20%. Such structures can be subsequently used for further investigation of molecular mechanisms of protein functioning and for the development of modern protein-based biotechnologies.

Discovering novel SNPs that are correlated with patient outcome in a Singaporean cancer patient cohort treated with gemcitabine-based chemotherapy

Background

Single Nucleotide Polymorphisms (SNPs) can influence patient outcome such as drug response and toxicity after drug intervention. The purpose of this study is to develop a systematic pathway approach to accurately and efficiently predict novel non-synonymous SNPs (nsSNPs) that could be causative to gemcitabine-based chemotherapy treatment outcome in Singaporean non-small cell lung cancer (NSCLC) patients.

Methods

Using a pathway approach that incorporates comprehensive protein-protein interaction data to systematically extend the gemcitabine pharmacologic pathway, we identified 77 related nsSNPs, common in the Singaporean population. After that, we used five computational criteria to prioritize the SNPs based on their importance for protein function. We specifically selected and screened six candidate SNPs in a patient cohort with NSCLC treated with gemcitabine-based chemotherapy.

Result

We performed survival analysis followed by hematologic toxicity analyses and found that three of six candidate SNPs are significantly correlated with the patient outcome (P < 0.05) i.e. ABCG2 Q141K (rs2231142), SLC29A3 S158F (rs780668) and POLR2A N764K (rs2228130).

Conclusions

Our computational SNP candidate enrichment workflow approach was able to identify several high confidence biomarkers predictive for personalized drug treatment outcome while providing a rationale for a molecular mechanism of the SNP effect.

Trial registration

NCT00695994. Registered 10 June, 2008 ‘retrospectively registered’.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-4471-x) contains supplementary material, which is available to authorized users.

A Comprehensive Mutagenesis Screen of the Adhesion GPCR Latrophilin-1/ADGRL1

Adhesion G-protein-coupled receptors (aGPCRs) play critical roles in diverse cellular processes in neurobiology, development, immunity, and numerous diseases. The lack of molecular understanding of their activation mechanisms, especially with regard to the transmembrane domains, hampers further studies to facilitate aGPCR-targeted drug development. Latrophilin-1/ADGRL1 is a model aGPCR that regulates synapse formation and embryogenesis, and its mutations are associated with cancer and attention-deficit/hyperactivity disorder. Here, we established functional assays to monitor latrophilin-1 function and showed the activation of latrophilin-1 by its endogenous agonist peptide. Via a comprehensive mutagenesis screen, we identified transmembrane domain residues essential for latrophilin-1 basal activity and for agonist peptide response. Strikingly, a cancer-associated mutation exhibited increased basal activity and failed to rescue the embryonic developmental phenotype in transgenic worms. These results provide a mechanistic foundation for future aGPCR-targeted drug design.

Network topology of NaV1.7 mutations in sodium channel-related painful disorders

BACKGROUND

Gain-of-function mutations in SCN9A gene that encodes the voltage-gated sodium channel NaV1.7 have been associated with a wide spectrum of painful syndromes in humans including inherited erythromelalgia, paroxysmal extreme pain disorder and small fibre neuropathy. These mutations change the biophysical properties of NaV1.7 channels leading to hyperexcitability of dorsal root ganglion nociceptors and pain symptoms. There is a need for better understanding of how gain-of-function mutations alter the atomic structure of Nav1.7.

RESULTS

We used homology modeling to build an atomic model of NaV1.7 and a network-based theoretical approach, which can predict interatomic interactions and connectivity arrangements, to investigate how pain-related NaV1.7 mutations may alter specific interatomic bonds and cause connectivity rearrangement, compared to benign variants and polymorphisms. For each amino acid substitution, we calculated the topological parameters betweenness centrality (B ct ), degree (D), clustering coefficient (CC ct ), closeness (C ct ), and eccentricity (E ct ), and calculated their variation (Δ value  = mutant value -WT value ). Pathogenic NaV1.7 mutations showed significantly higher variation of |ΔB ct | compared to benign variants and polymorphisms. Using the cut-off value ±0.26 calculated by receiver operating curve analysis, we found that ΔB ct correctly differentiated pathogenic NaV1.7 mutations from variants not causing biophysical abnormalities (nABN) and homologous SNPs (hSNPs) with 76% sensitivity and 83% specificity.

CONCLUSIONS

Our in-silico analyses predict that pain-related pathogenic NaV1.7 mutations may affect the network topological properties of the protein and suggest |ΔB ct | value as a potential in-silico marker.

Split-ubiquitin yeast two-hybrid interaction reveals a novel interaction between a natural resistance associated macrophage protein and a membrane bound thioredoxin in Brassica juncea

Natural resistance associated macrophage proteins (NRAMPs) are evolutionarily conserved metal transporters involved in the transport of essential and nonessential metals in plants. Fifty protein interactors of a Brassica juncea NRAMP protein was identified by a Split-Ubiquitin Yeast-Two-Hybrid screen. The interactors were predicted to function as components of stress response, signaling, development, RNA binding and processing. BjNRAMP4.1 interactors were particularly enriched in proteins taking part in photosynthetic or light regulated processes, or proteins predicted to be localized in plastid/chloroplast. Further, many interactors also had a suggested role in cellular redox regulation. Among these, the interaction of a photosynthesis-related thioredoxin, homologous to Arabidopsis HCF164 (High-chlorophyll fluorescence164) was studied in detail. Homology modeling of BjNRAMP4.1 suggested that it could be redox regulated by BjHCF164. In yeast, the interaction between the two proteins was found to increase in response to metal deficiency; Mn excess and exogenous thiol. Excess Mn also increased the interaction in planta and led to greater accumulation of the complex at the root apoplast. Network analysis of Arabidopsis homologs of BjNRAMP4.1 interactors showed enrichment of many protein components, central to chloroplastic/cellular ROS signaling. BjNRAMP4.1 interacted with BjHCF164 at the root membrane and also in the chloroplast in accordance with its proposed function related to photosynthesis, indicating that this interaction occurred at different sub-cellular locations depending on the tissue. This may serve as a link between metal homeostasis and chloroplastic/cellular ROS through protein-protein interaction.

The CydDC ABC transporter of Escherichia coli: new roles for a reductant efflux pump

The CydDC complex of Escherichia coli is a heterodimeric ATP-binding cassette (ABC) transporter that exports cysteine and glutathione to the periplasm. These reductants are thought to modulate periplasmic redox poise, impacting upon the disulfide folding of periplasmic and secreted proteins involved in bacterial virulence. Diminished CydDC activity abolishes the assembly of functional bd-type respiratory oxidases and perturbs haem ligation during the assembly of c-type cytochromes. The focus herein is upon a newly-discovered interaction of the CydDC complex with a haem cofactor; haem has recently been shown to modulate CydDC activity and structural modelling reveals a potential haem-binding site on the periplasmic surface of the complex. These findings have important implications for future investigations into the potential roles for the CydDC-bound haem in redox sensing and tolerance to nitric oxide (NO).

Structural modeling of G-protein coupled receptors: An overview on automatic web-servers

Despite the significant efforts and discoveries during the last few years in G protein-coupled receptor (GPCR) expression and crystallization, the receptors with known structures to date are limited only to a small fraction of human GPCRs. The lack of experimental three-dimensional structures of the receptors represents a strong limitation that hampers a deep understanding of their function. Computational techniques are thus a valid alternative strategy to model three-dimensional structures. Indeed, recent advances in the field, together with extraordinary developments in crystallography, in particular due to its ability to capture GPCRs in different activation states, have led to encouraging results in the generation of accurate models. This, prompted the community of modelers to render their methods publicly available through dedicated databases and web-servers. Here, we present an extensive overview on these services, focusing on their advantages, drawbacks and their role in successful applications. Future challenges in the field of GPCR modeling, such as the predictions of long loop regions and the modeling of receptor activation states are presented as well.

Computational modeling of membrane proteins

The determination of membrane protein (MP) structures has always trailed that of soluble proteins due to difficulties in their overexpression, reconstitution into membrane mimetics, and subsequent structure determination. The percentage of MP structures in the protein databank (PDB) has been at a constant 1-2% for the last decade. In contrast, over half of all drugs target MPs, only highlighting how little we understand about drug-specific effects in the human body. To reduce this gap, researchers have attempted to predict structural features of MPs even before the first structure was experimentally elucidated. In this review, we present current computational methods to predict MP structure, starting with secondary structure prediction, prediction of trans-membrane spans, and topology. Even though these methods generate reliable predictions, challenges such as predicting kinks or precise beginnings and ends of secondary structure elements are still waiting to be addressed. We describe recent developments in the prediction of 3D structures of both α-helical MPs as well as β-barrels using comparative modeling techniques, de novo methods, and molecular dynamics (MD) simulations. The increase of MP structures has (1) facilitated comparative modeling due to availability of more and better templates, and (2) improved the statistics for knowledge-based scoring functions. Moreover, de novo methods have benefited from the use of correlated mutations as restraints. Finally, we outline current advances that will likely shape the field in the forthcoming decade.

AlignMe--a membrane protein sequence alignment web server

We present a web server for pair-wise alignment of membrane protein sequences, using the program AlignMe. The server makes available two operational modes of AlignMe: (i) sequence to sequence alignment, taking two sequences in fasta format as input, combining information about each sequence from multiple sources and producing a pair-wise alignment (PW mode); and (ii) alignment of two multiple sequence alignments to create family-averaged hydropathy profile alignments (HP mode). For the PW sequence alignment mode, four different optimized parameter sets are provided, each suited to pairs of sequences with a specific similarity level. These settings utilize different types of inputs: (position-specific) substitution matrices, secondary structure predictions and transmembrane propensities from transmembrane predictions or hydrophobicity scales. In the second (HP) mode, each input multiple sequence alignment is converted into a hydrophobicity profile averaged over the provided set of sequence homologs; the two profiles are then aligned. The HP mode enables qualitative comparison of transmembrane topologies (and therefore potentially of 3D folds) of two membrane proteins, which can be useful if the proteins have low sequence similarity. In summary, the AlignMe web server provides user-friendly access to a set of tools for analysis and comparison of membrane protein sequences. Access is available at http://www.bioinfo.mpg.de/AlignMe

Automated procedure for contact-map-based protein structure reconstruction

Knowledge of the three-dimensional structures of ion channels allows for modeling their conductivity characteristics using biophysical models and can lead to discovering their cellular functionality. Recent studies show that quality of structure predictions can be significantly improved using protein contact site information. Therefore, a number of procedures for protein structure prediction based on their contact-map have been proposed. Their comparison is difficult due to different methodologies used for validation. In this work, a Contact Map-to-Structure pipeline (C2S_pipeline) for contact-based protein structure reconstruction is designed and validated. The C2S_pipeline can be used to reconstruct monomeric and multimeric proteins. The median RMSD of structures obtained during validation on a representative set of protein structures, equaled 5.27 Å, and the best structure was reconstructed with RMSD of 1.59 Å. The validation is followed by a detailed case study on the KcsA ion channel. Models of KcsA are reconstructed based on different portions of contact site information. Structural feature analysis of acquired KcsA models is supported by a thorough analysis of electrostatic potential distributions inside the channels. The study shows that electrostatic parameters are correlated with structural quality of models. Therefore, they can be used to discriminate between high and low quality structures. We show that 30 % of contact information is needed to obtain accurate structures of KcsA, if contacts are selected randomly. This number increases to 70 % in case of erroneous maps in which the remaining contacts or non-contacts are changed to the opposite. Furthermore, the study reveals that local reconstruction accuracy is correlated with the number of contacts in which amino acid are involved. This results in higher reconstruction accuracy in the structure core than peripheral regions.