Quality assessment of protein model-structures using evolutionary conservation

Molecular modelling and de novo fragment-based design of potential inhibitors of beta-tubulin gene of Necator americanus from natural products

The emergence of drug resistance against the known hookworm drugs namely albendazole and mebendazole and their reduced efficacies necessitate the need for new drugs. Chemically diverse natural products present plausible templates to augment hookworm drug discovery. The present work utilized pharmacoinformatics techniques to predict African natural compounds ZINC95486082, ZINC95486052 and euphohelionon as potential inhibitory molecules of the hookworm Necator americanus β tubulin gene. A library of 3390 compounds was screened against a homology-modelled structure of β tubulin. The docking results obtained from AutoDock Vina was validated with an acceptable area under the curve (AUC) of 0.714 computed from the receiver operating characteristic (ROC) curve. The three selected compounds had favourable binding affinities and were predicted to form no interactions with the resistance-associated mutations Phe167, Glu198 and Phe200. The compounds were predicted as anthelmintics using a Bayesian-based technique and were pharmacologically profiled to be druglike. Further molecular dynamics simulations and MM-PBSA calculations showed the compounds as promising anthelmintic drug leads. Novel critical residues comprising Leu246, Asn247 and Asn256 were also predicted for binding. Euphohelionon was selected as a template for the de novo fragment-based design of five compounds labelled A1, A2, A3, A4 and A5; with four of them having SAscore values below 6, denoting easy synthesis. All the five de novo molecules docked firmly in the binding pocket of the β tubulin with no binding interactions with the three known resistance mutation residues. Binding energies of −8.2, −7.6, −7.3, −7.2 and −6.8 kcal/mol were obtained for A1, A2, A3, A4 and A5, respectively. The identified compounds can serve as treasure troves from which future potent anthelmintics can be designed. The current study strives to assuage the hookworm disease burden, especially making available molecules with the potential to circumvent the chemoresistance.

QDeep: distance-based protein model quality estimation by residue-level ensemble error classifications using stacked deep residual neural networks

MOTIVATION

Protein model quality estimation, in many ways, informs protein structure prediction. Despite their tight coupling, existing model quality estimation methods do not leverage inter-residue distance information or the latest technological breakthrough in deep learning that has recently revolutionized protein structure prediction.

RESULTS

We present a new distance-based single-model quality estimation method called QDeep by harnessing the power of stacked deep residual neural networks (ResNets). Our method first employs stacked deep ResNets to perform residue-level ensemble error classifications at multiple predefined error thresholds, and then combines the predictions from the individual error classifiers for estimating the quality of a protein structural model. Experimental results show that our method consistently outperforms existing state-of-the-art methods including ProQ2, ProQ3, ProQ3D, ProQ4, 3DCNN, MESHI, and VoroMQA in multiple independent test datasets across a wide-range of accuracy measures; and that predicted distance information significantly contributes to the improved performance of QDeep.

AVAILABILITY AND IMPLEMENTATION

https://github.com/Bhattacharya-Lab/QDeep.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

In silico designing of putative peptides for targeting pathological protein Htt in Huntington's disease

Huntington's disease is a neurodegenerative disease caused by CAG repeat in the first exon of HTT (Huntingtin) gene, leading to abnormal form of Htt protein containing enlarged polyglutamine strands of variable length that stick together to form aggregates and is toxic to brain causing brain damage. Complete reversal of brain damage is not possible till date but recovery may be possible by peptide therapy. The peptide-based therapy for Huntington's disease includes both poly Q peptide as well as non poly Q peptides like (QBP1)2, p42, Exendin 4, ED11, CaM, BiP, Leuprorelin peptide. The novel approach that is currently being tested in this article is the peptide-based therapy to target the mutated protein. This approach is based on the principle of preventing the aggregation of mutant Htt by blocking the potential sites responsible for protein aggregation and thereby ameliorating the disease symptoms. Herein, we have screened a variety of potential peptides that were known to prevent the protein aggregation, comparatively analyzed their binding affinity with homology modeled Htt protein, designed novel peptides based upon conservation analysis among screened potential peptides as a therapeutic agent, comparatively analyzed the therapeutic potential of novel peptides against modeled Htt protein for investigating the therapeutic prospects of Huntington's disease. We have designed a peptide for the therapy of Huntington's disease by comparing several peptides, which are already in use for Huntington's disease.

Interactome Analysis and Docking Sites of MutS Homologs Reveal New Physiological Roles in Arabidopsis thaliana

Due to their sedentary lifestyle, plants are constantly exposed to different stress stimuli. Stress comes in variety of forms where factors like radiation, free radicals, “replication errors, polymerase slippage”, and chemical mutagens result in genotoxic or cytotoxic damage. In order to face “the base oxidation or DNA replication stress”, plants have developed many sophisticated mechanisms. One of them is the DNA mismatch repair (MMR) pathway. The main part of the MMR is the MutS homologue (MSH) protein family. The genome of Arabidopsis thaliana encodes at least seven homologues of the MSH family: AtMSH1, AtMSH2, AtMSH3, AtMSH4, AtMSH5, AtMSH6, and AtMSH7. Despite their importance, the functions of AtMSH homologs have not been investigated. In this work, bioinformatics tools were used to obtain a better understanding of MSH-mediated DNA repair mechanisms in Arabidopsis thaliana and to understand the additional biological roles of AtMSH family members. In silico analysis, including phylogeny tracking, prediction of 3D structure, interactome analysis, and docking site prediction, suggested interactions with proteins were important for physiological development of A. thaliana. The MSH homologs extensively interacted with both TIL1 and TIL2 (DNA polymerase epsilon catalytic subunit), proteins involved in cell fate determination during plant embryogenesis and involved in flowering time repression. Additionally, interactions with the RECQ protein family (helicase enzymes) and proteins of nucleotide excision repair pathway were detected. Taken together, the results presented here confirm the important role of AtMSH proteins in mismatch repair and suggest important new physiological roles.

Structural and functional insights into TRiC chaperonin from a psychrophilic yeast, Glaciozyma antarctica

Studies on TCP1-1 ring complex (TRiC) chaperonin have shown its indispensable role in folding cytosolic proteins in eukaryotes. In a psychrophilic organism, extreme cold temperature creates a low-energy environment that potentially causes protein denaturation with loss of activity. We hypothesized that TRiC may undergo evolution in terms of its structural molecular adaptation in order to facilitate protein folding in low-energy environment. To test this hypothesis, we isolated G. antarctica TRiC (GaTRiC) and found that the expression of GaTRiC mRNA in G. antarctica was consistently expressed at all temperatures indicating their importance in cell regulation. Moreover, we showed GaTRiC has the ability of a chaperonin whereby denatured luciferase can be folded to the functional stage in its presence. Structurally, three categories of residue substitutions were found in α, β, and δ subunits: (i) bulky/polar side chains to alanine or valine, (ii) charged residues to alanine, and (iii) isoleucine to valine that would be expected to increase intramolecular flexibility within the GaTRiC. The residue substitutions observed in the built structures possibly affect the hydrophobic, hydrogen bonds, and ionic and aromatic interactions which lead to an increase in structural flexibility. Our structural and functional analysis explains some possible structural features which may contribute to cold adaptation of the psychrophilic TRiC folding chamber.

An overview of comparative modelling and resources dedicated to large-scale modelling of genome sequences

Computational modelling of proteins has been a major catalyst in structural biology. Bioinformatics groups have exploited the repositories of known structures to predict high-quality structural models with high efficiency at low cost. This article provides an overview of comparative modelling, reviews recent developments and describes resources dedicated to large-scale comparative modelling of genome sequences. The value of subclustering protein domain superfamilies to guide the template-selection process is investigated. Some recent cases in which structural modelling has aided experimental work to determine very large macromolecular complexes are also cited.

Targeting the ubiquitin-conjugating enzyme E2D4 for cancer drug discovery-a structure-based approach

Cancer progression is a global burden. The incidence and mortality now reach 30 million deaths per year. Several pathways of cancer are under investigation for the discovery of effective therapeutics. The present study highlights the structural details of the ubiquitin protein 'Ubiquitin-conjugating enzyme E2D4' (UBE2D4) for the novel lead structure identification in cancer drug discovery process. The evaluation of 3D structure of UBE2D4 was carried out using homology modelling techniques. The optimized structure was validated by standard computational protocols. The active site region of the UBE2D4 was identified using computational tools like CASTp, Q-site Finder and SiteMap. The hydrophobic pocket which is responsible for binding with its natural receptor ubiquitin ligase CHIP (C-terminal of Hsp 70 interacting protein) was identified through protein-protein docking study. Corroborating the results obtained from active site prediction tools and protein-protein docking study, the domain of UBE2D4 which is responsible for cancer cell progression is sorted out for further docking study. Virtual screening with large structural database like CB_Div Set and Asinex BioDesign small molecular structural database was carried out. The obtained new ligand molecules that have shown affinity towards UBE2D4 were considered for ADME prediction studies. The identified new ligand molecules with acceptable parameters of docking, ADME are considered as potent UBE2D4 enzyme inhibitors for cancer therapy.

DeepQA: improving the estimation of single protein model quality with deep belief networks

BACKGROUND

Protein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem.

RESULTS

We introduce a novel single-model quality assessment method DeepQA based on deep belief network that utilizes a number of selected features describing the quality of a model from different perspectives, such as energy, physio-chemical characteristics, and structural information. The deep belief network is trained on several large datasets consisting of models from the Critical Assessment of Protein Structure Prediction (CASP) experiments, several publicly available datasets, and models generated by our in-house ab initio method. Our experiments demonstrate that deep belief network has better performance compared to Support Vector Machines and Neural Networks on the protein model quality assessment problem, and our method DeepQA achieves the state-of-the-art performance on CASP11 dataset. It also outperformed two well-established methods in selecting good outlier models from a large set of models of mostly low quality generated by ab initio modeling methods.

CONCLUSION

DeepQA is a useful deep learning tool for protein single model quality assessment and protein structure prediction. The source code, executable, document and training/test datasets of DeepQA for Linux is freely available to non-commercial users at http://cactus.rnet.missouri.edu/DeepQA/ .

Protein single-model quality assessment by feature-based probability density functions

Protein quality assessment (QA) has played an important role in protein structure prediction. We developed a novel single-model quality assessment method–Qprob. Qprob calculates the absolute error for each protein feature value against the true quality scores (i.e. GDT-TS scores) of protein structural models, and uses them to estimate its probability density distribution for quality assessment. Qprob has been blindly tested on the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) as MULTICOM-NOVEL server. The official CASP result shows that Qprob ranks as one of the top single-model QA methods. In addition, Qprob makes contributions to our protein tertiary structure predictor MULTICOM, which is officially ranked 3rd out of 143 predictors. The good performance shows that Qprob is good at assessing the quality of models of hard targets. These results demonstrate that this new probability density distribution based method is effective for protein single-model quality assessment and is useful for protein structure prediction. The webserver of Qprob is available at: http://calla.rnet.missouri.edu/qprob/. The software is now freely available in the web server of Qprob.

Metal binding spectrum and model structure of the Bacillus anthracis virulence determinant MntA

The potentially lethal human pathogen Bacillus anthracis expresses a putative metal import system, MntBCA, which belongs to the large family of ABC transporters. MntBCA is essential for virulence of Bacillus anthracis: deletion of MntA, the system's substrate binding protein, yields a completely non-virulent strain. Here we determined the metal binding spectrum of MntA. In contrast to what can be inferred from growth complementation studies we find no evidence that MntA binds Fe(2+) or Fe(3+). Rather, MntA binds a variety of other metal ions, including Mn(2+), Zn(2+), Cd(2+), Co(2+), and Ni(2+) with affinities ranging from 10(-6) to 10(-8) M. Binding of Zn(2+) and Co(2+) have a pronounced thermo-stabilizing effect on MntA, with Mn(2+) having a milder effect. The thermodynamic stability of MntA, competition experiments, and metal binding and release experiments all suggest that Mn(2+) is the metal that is likely transported by MntBCA and is therefore the limiting factor for virulence of Bacillus anthracis. A homology-model of MntA shows a single, highly conserved metal binding site, with four residues that participate in metal coordination: two histidines, a glutamate, and an aspartate. The metals bind to this site in a mutually exclusive manner, yet surprisingly, mutational analysis shows that for proper coordination each metal requires a different subset of these four residues. ConSurf evolutionary analysis and structural comparison of MntA and its homologues suggest that substrate binding proteins (SBPs) of metal ions use a pair of highly conserved prolines to interact with their cognate ABC transporters. This proline pair is found exclusively in ABC import systems of metal ions.

Customised fragments libraries for protein structure prediction based on structural class annotations

Background

Since experimental techniques are time and cost consuming, in silico protein structure prediction is essential to produce conformations of protein targets. When homologous structures are not available, fragment-based protein structure prediction has become the approach of choice. However, it still has many issues including poor performance when targets’ lengths are above 100 residues, excessive running times and sub-optimal energy functions. Taking advantage of the reliable performance of structural class prediction software, we propose to address some of the limitations of fragment-based methods by integrating structural constraints in their fragment selection process.

Results

Using Rosetta, a state-of-the-art fragment-based protein structure prediction package, we evaluated our proposed pipeline on 70 former CASP targets containing up to 150 amino acids. Using either CATH or SCOP-based structural class annotations, enhancement of structure prediction performance is highly significant in terms of both GDT_TS (at least +2.6, p-values < 0.0005) and RMSD (−0.4, p-values < 0.005). Although CATH and SCOP classifications are different, they perform similarly. Moreover, proteins from all structural classes benefit from the proposed methodology. Further analysis also shows that methods relying on class-based fragments produce conformations which are more relevant to user and converge quicker towards the best model as estimated by GDT_TS (up to 10% in average). This substantiates our hypothesis that usage of structurally relevant templates conducts to not only reducing the size of the conformation space to be explored, but also focusing on a more relevant area.

Conclusions

Since our methodology produces models the quality of which is up to 7% higher in average than those generated by a standard fragment-based predictor, we believe it should be considered before conducting any fragment-based protein structure prediction. Despite such progress, ab initio prediction remains a challenging task, especially for proteins of average and large sizes. Apart from improving search strategies and energy functions, integration of additional constraints seems a promising route, especially if they can be accurately predicted from sequence alone.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0576-2) contains supplementary material, which is available to authorized users.

Structure, dynamics and implied gating mechanism of a human cyclic nucleotide-gated channel

Cyclic nucleotide-gated (CNG) ion channels are nonselective cation channels, essential for visual and olfactory sensory transduction. Although the channels include voltage-sensor domains (VSDs), their conductance is thought to be independent of the membrane potential, and their gating regulated by cytosolic cyclic nucleotide-binding domains. Mutations in these channels result in severe, degenerative retinal diseases, which remain untreatable. The lack of structural information on CNG channels has prevented mechanistic understanding of disease-causing mutations, precluded structure-based drug design, and hampered in silico investigation of the gating mechanism. To address this, we built a 3D model of the cone tetrameric CNG channel, based on homology to two distinct templates with known structures: the transmembrane (TM) domain of a bacterial channel, and the cyclic nucleotide-binding domain of the mouse HCN2 channel. Since the TM-domain template had low sequence-similarity to the TM domains of the CNG channels, and to reconcile conflicts between the two templates, we developed a novel, hybrid approach, combining homology modeling with evolutionary coupling constraints. Next, we used elastic network analysis of the model structure to investigate global motions of the channel and to elucidate its gating mechanism. We found the following: (i) In the main mode of motion, the TM and cytosolic domains counter-rotated around the membrane normal. We related this motion to gating, a proposition that is supported by previous experimental data, and by comparison to the known gating mechanism of the bacterial KirBac channel. (ii) The VSDs could facilitate gating (supplementing the pore gate), explaining their presence in such 'voltage-insensitive' channels. (iii) Our elastic network model analysis of the CNGA3 channel supports a modular model of allosteric gating, according to which protein domains are quasi-independent: they can move independently, but are coupled to each other allosterically.

Structure and regulatory interactions of the cytoplasmic terminal domains of serotonin transporter

Uptake of neurotransmitters by sodium-coupled monoamine transporters of the NSS family is required for termination of synaptic transmission. Transport is tightly regulated by protein–protein interactions involving the small cytoplasmic segments at the amino- and carboxy-terminal ends of the transporter. Although structures of homologues provide information about the transmembrane regions of these transporters, the structural arrangement of the terminal domains remains largely unknown. Here, we combined molecular modeling, biochemical, and biophysical approaches in an iterative manner to investigate the structure of the 82-residue N-terminal and 30-residue C-terminal domains of human serotonin transporter (SERT). Several secondary structures were predicted in these domains, and structural models were built using the Rosetta fragment-based methodology. One-dimensional ¹H nuclear magnetic resonance and circular dichroism spectroscopy supported the presence of helical elements in the isolated SERT N-terminal domain. Moreover, introducing helix-breaking residues within those elements altered the fluorescence resonance energy transfer signal between terminal cyan fluorescent protein and yellow fluorescent protein tags attached to full-length SERT, consistent with the notion that the fold of the terminal domains is relatively well-defined. Full-length models of SERT that are consistent with these and published experimental data were generated. The resultant models predict confined loci for the terminal domains and predict that they move apart during the transport-related conformational cycle, as predicted by structures of homologues and by the “rocking bundle” hypothesis, which is consistent with spectroscopic measurements. The models also suggest the nature of binding to regulatory interaction partners. This study provides a structural context for functional and regulatory mechanisms involving SERT terminal domains.

Structural and mechanistic insight into alkane hydroxylation by Pseudomonas putida AlkB

Integral membrane non-haem di-iron alkane hydroxylases (AlkBs) are enzymes of unknown structure that allow bacteria to grow on alkanes. Catalysis-linked modifications with the inhibitor 1-octyne, mutagenesis studies and ab initio modelling provided novel insights into the structure and function of AlkB.

Designing and evaluating the MULTICOM protein local and global model quality prediction methods in the CASP10 experiment

BACKGROUND

Protein model quality assessment is an essential component of generating and using protein structural models. During the Tenth Critical Assessment of Techniques for Protein Structure Prediction (CASP10), we developed and tested four automated methods (MULTICOM-REFINE, MULTICOM-CLUSTER, MULTICOM-NOVEL, and MULTICOM-CONSTRUCT) that predicted both local and global quality of protein structural models.

RESULTS

MULTICOM-REFINE was a clustering approach that used the average pairwise structural similarity between models to measure the global quality and the average Euclidean distance between a model and several top ranked models to measure the local quality. MULTICOM-CLUSTER and MULTICOM-NOVEL were two new support vector machine-based methods of predicting both the local and global quality of a single protein model. MULTICOM-CONSTRUCT was a new weighted pairwise model comparison (clustering) method that used the weighted average similarity between models in a pool to measure the global model quality. Our experiments showed that the pairwise model assessment methods worked better when a large portion of models in the pool were of good quality, whereas single-model quality assessment methods performed better on some hard targets when only a small portion of models in the pool were of reasonable quality.

CONCLUSIONS

Since digging out a few good models from a large pool of low-quality models is a major challenge in protein structure prediction, single model quality assessment methods appear to be poised to make important contributions to protein structure modeling. The other interesting finding was that single-model quality assessment scores could be used to weight the models by the consensus pairwise model comparison method to improve its accuracy.

Assessing the quality of modelled 3D protein structures using the ModFOLD server

Model quality assessment programs (MQAPs) aim to assess the quality of modelled 3D protein structures. The provision of quality scores, describing both global and local (per-residue) accuracy are extremely important, as without quality scores we are unable to determine the usefulness of a 3D model for further computational and experimental wet lab studies.Here, we briefly discuss protein tertiary structure prediction, along with the biennial Critical Assessment of Techniques for Protein Structure Prediction (CASP) competition and their key role in driving the field of protein model quality assessment methods (MQAPs). We also briefly discuss the top MQAPs from the previous CASP competitions. Additionally, we describe our downloadable and webserver-based model quality assessment methods: ModFOLD3, ModFOLDclust, ModFOLDclustQ, ModFOLDclust2, and IntFOLD-QA. We provide a practical step-by-step guide on using our downloadable and webserver-based tools and include examples of their application for improving tertiary structure prediction, ligand binding site residue prediction, and oligomer predictions.

NEW MDS AND CLUSTERING BASED ALGORITHMS FOR PROTEIN MODEL QUALITY ASSESSMENT AND SELECTION

In protein tertiary structure prediction, assessing the quality of predicted models is an essential task. Over the past years, many methods have been proposed for the protein model quality assessment (QA) and selection problem. Despite significant advances, the discerning power of current methods is still unsatisfactory. In this paper, we propose two new algorithms, CC-Select and MDS-QA, based on multidimensional scaling and k-means clustering. For the model selection problem, CC-Select combines consensus with clustering techniques to select the best models from a given pool. Given a set of predicted models, CC-Select first calculates a consensus score for each structure based on its average pairwise structural similarity to other models. Then, similar structures are grouped into clusters using multidimensional scaling and clustering algorithms. In each cluster, the one with the highest consensus score is selected as a candidate model. For the QA problem, MDS-QA combines single-model scoring functions with consensus to determine more accurate assessment score for every model in a given pool. Using extensive benchmark sets of a large collection of predicted models, we compare the two algorithms with existing state-of-the-art quality assessment methods and show significant improvement.

BeEP Server: Using evolutionary information for quality assessment of protein structure models

The BeEP Server (http://www.embnet.qb.fcen.uba.ar/embnet/beep.php) is an online resource aimed to help in the endgame of protein structure prediction. It is able to rank submitted structural models of a protein through an explicit use of evolutionary information, a criterion differing from structural or energetic considerations commonly used in other assessment programs. The idea behind BeEP (Best Evolutionary Pattern) is to benefit from the substitution pattern derived from structural constraints present in a set of homologous proteins adopting a given protein conformation. The BeEP method uses a model of protein evolution that takes into account the structure of a protein to build site-specific substitution matrices. The suitability of these substitution matrices is assessed through maximum likelihood calculations from which position-specific and global scores can be derived. These scores estimate how well the structural constraints derived from each structural model are represented in a sequence alignment of homologous proteins. Our assessment on a subset of proteins from the Critical Assessment of techniques for protein Structure Prediction (CASP) experiment has shown that BeEP is capable of discriminating the models and selecting one or more native-like structures. Moreover, BeEP is not explicitly parameterized to find structural similarities between models and given targets, potentially helping to explore the conformational ensemble of the native state.

Identification of molecular hinge points mediating alternating access in the vesicular monoamine transporter VMAT2

Vesicular monoamine transporter 2 (VMAT2) catalyzes transport of monoamines into storage vesicles in a process that involves exchange of the charged monoamine with two protons. VMAT2 is a member of the DHA12 family of multidrug transporters that belongs to the major facilitator superfamily (MFS) of secondary transporters. Here we present a homology model of VMAT2, which has the standard MFS fold, that is, with two domains of six transmembrane helices each which are related by twofold pseudosymmetry and whose axis runs normal to the membrane and between the two halves. Demonstration of the essential role of a membrane-embedded glutamate and confirmation of the existence of a hydrogen bond probably involved in proton transport provide experimental evidence that validates some of the predictions inherent to the model. Moreover, we show the essential role of residues at two anchor points between the two bundles. These residues appear to function as molecular hinge points about which the two six transmembrane-helix bundles flex and straighten to open and close the pathways on either side of the membrane as required for transport. Polar residues that create a hydrogen bond cluster form one of the anchor points of VMAT2. The other results from hydrophobic interactions. Residues at the anchor points are strongly conserved in other MFS transporters in one way or another, suggesting that interactions at these locations will be critical in most, if not all, MFS transporters.

Long indels are disordered: a study of disorder and indels in homologous eukaryotic proteins

Proteins evolve through point mutations as well as by insertions and deletions (indels). During the last decade it has become apparent that protein regions that do not fold into three-dimensional structures, i.e. intrinsically disordered regions, are quite common. Here, we have studied the relationship between protein disorder and indels using HMM-HMM pairwise alignments in two sets of orthologous eukaryotic protein pairs. First, we show that disordered residues are much more frequent among indel residues than among aligned residues and, also are more prevalent among indels than in coils. Second, we observed that disordered residues are particularly common in longer indels. Disordered indels of short-to-medium size are prevalent in the non-terminal regions of proteins while the longest indels, ordered and disordered alike, occur toward the termini of the proteins where new structural units are comparatively well tolerated. Finally, while disordered regions often evolve faster than ordered regions and disorder is common in indels, there are some previously recognized protein families where the disordered region is more conserved than the ordered region. We find that these rare proteins are often involved in information processes, such as RNA processing and translation. This article is part of a Special Issue entitled: The emerging dynamic view of proteins: Protein plasticity in allostery, evolution and self-assembly.

Dissecting a regulatory calcium-binding site of CLC-K kidney chloride channels

The kidney and inner ear CLC-K chloride channels, which are involved in salt absorption and endolymph production, are regulated by extracellular Ca²⁺ in the millimolar concentration range. Recently, Gradogna et al. (2010. J. Gen. Physiol.http://dx.doi.org/10.1085/jgp.201010455) identified a pair of acidic residues (E261 and D278) located in the loop between helices I and J as forming a putative intersubunit Ca²⁺-binding site in hClC-Ka. In this study, we sought to explore the properties of the binding site in more detail. First, we verified that the site is conserved in hClC-Kb and rClC-K1. In addition, we could confer Ca²⁺ sensitivity to the Torpedo marmorata ClC-0 channel by exchanging its I–J loop with that from ClC-Ka, demonstrating a direct role of the loop in Ca²⁺ binding. Based on a structure of a bacterial CLC and a new sequence alignment, we built homology models of ClC-Ka. The models suggested additional amino acids involved in Ca²⁺ binding. Testing mutants of these residues, we could restrict the range of plausible models and positively identify two more residues (E259 and E281) involved in Ca²⁺ coordination. To investigate cation specificity, we applied extracellular Zn²⁺, Mg²⁺, Ba²⁺, Sr²⁺, and Mn²⁺. Zn²⁺ blocks ClC-Ka as well as its Ca²⁺-insensitive mutant, suggesting that Zn²⁺ binds to a different site. Mg²⁺ does not activate CLC-Ks, but the channels are activated by Ba²⁺, Sr²⁺, and Mn²⁺ with a rank order of potency of Ca²⁺ > Ba²⁺ > Sr²⁺ = Mn²⁺ for the human CLC-Ks. Dose–response analysis indicates that the less potent Ba²⁺ has a lower affinity rather than a lower efficacy. Interestingly, rClC-K1 shows an altered rank order (Ca²⁺ > Sr²⁺ >> Ba²⁺), but homology models suggest that residues outside the I–J loop are responsible for this difference. Our detailed characterization of the regulatory Ca²⁺-binding site provides a solid basis for the understanding of the physiological modulation of CLC-K channel function in the kidney and inner ear.

Quality assessment of protein model-structures based on structural and functional similarities

Background

Experimental determination of protein 3D structures is expensive, time consuming and sometimes impossible. A gap between number of protein structures deposited in the World Wide Protein Data Bank and the number of sequenced proteins constantly broadens. Computational modeling is deemed to be one of the ways to deal with the problem. Although protein 3D structure prediction is a difficult task, many tools are available. These tools can model it from a sequence or partial structural information, e.g. contact maps. Consequently, biologists have the ability to generate automatically a putative 3D structure model of any protein. However, the main issue becomes evaluation of the model quality, which is one of the most important challenges of structural biology.

Results

GOBA - Gene Ontology-Based Assessment is a novel Protein Model Quality Assessment Program. It estimates the compatibility between a model-structure and its expected function. GOBA is based on the assumption that a high quality model is expected to be structurally similar to proteins functionally similar to the prediction target. Whereas DALI is used to measure structure similarity, protein functional similarity is quantified using standardized and hierarchical description of proteins provided by Gene Ontology combined with Wang's algorithm for calculating semantic similarity. Two approaches are proposed to express the quality of protein model-structures. One is a single model quality assessment method, the other is its modification, which provides a relative measure of model quality. Exhaustive evaluation is performed on data sets of model-structures submitted to the CASP8 and CASP9 contests.

Conclusions

The validation shows that the method is able to discriminate between good and bad model-structures. The best of tested GOBA scores achieved 0.74 and 0.8 as a mean Pearson correlation to the observed quality of models in our CASP8 and CASP9-based validation sets. GOBA also obtained the best result for two targets of CASP8, and one of CASP9, compared to the contest participants. Consequently, GOBA offers a novel single model quality assessment program that addresses the practical needs of biologists. In conjunction with other Model Quality Assessment Programs (MQAPs), it would prove useful for the evaluation of single protein models.

Improved model quality assessment using ProQ2

Background

Employing methods to assess the quality of modeled protein structures is now standard practice in bioinformatics. In a broad sense, the techniques can be divided into methods relying on consensus prediction on the one hand, and single-model methods on the other. Consensus methods frequently perform very well when there is a clear consensus, but this is not always the case. In particular, they frequently fail in selecting the best possible model in the hard cases (lacking consensus) or in the easy cases where models are very similar. In contrast, single-model methods do not suffer from these drawbacks and could potentially be applied on any protein of interest to assess quality or as a scoring function for sampling-based refinement.

Results

Here, we present a new single-model method, ProQ2, based on ideas from its predecessor, ProQ. ProQ2 is a model quality assessment algorithm that uses support vector machines to predict local as well as global quality of protein models. Improved performance is obtained by combining previously used features with updated structural and predicted features. The most important contribution can be attributed to the use of profile weighting of the residue specific features and the use features averaged over the whole model even though the prediction is still local.

Conclusions

ProQ2 is significantly better than its predecessors at detecting high quality models, improving the sum of Z-scores for the selected first-ranked models by 20% and 32% compared to the second-best single-model method in CASP8 and CASP9, respectively. The absolute quality assessment of the models at both local and global level is also improved. The Pearson’s correlation between the correct and local predicted score is improved from 0.59 to 0.70 on CASP8 and from 0.62 to 0.68 on CASP9; for global score to the correct GDT_TS from 0.75 to 0.80 and from 0.77 to 0.80 again compared to the second-best single methods in CASP8 and CASP9, respectively. ProQ2 is available at http://proq2.wallnerlab.org.

How does KCNE1 regulate the Kv7.1 potassium channel? Model-structure, mutations, and dynamics of the Kv7.1-KCNE1 complex

The voltage-gated potassium channel Kv7.1 and its auxiliary subunit KCNE1 are expressed in the heart and give rise to the major repolarization current. The interaction of Kv7.1 with the single transmembrane helix of KCNE1 considerably slows channel activation and deactivation, raises single-channel conductance, and prevents slow voltage-dependent inactivation. We built a Kv7.1-KCNE1 model-structure. The model-structure agrees with previous disulfide mapping studies and enables us to derive molecular interpretations of electrophysiological recordings that we obtained for two KCNE1 mutations. An elastic network analysis of Kv7.1 fluctuations in the presence and absence of KCNE1 suggests a mechanistic perspective on the known effects of KCNE1 on Kv7.1 function: slow deactivation is attributed to the low mobility of the voltage-sensor domains upon KCNE1 binding, abolishment of voltage-dependent inactivation could result from decreased fluctuations in the external vestibule, and amalgamation of the fluctuations in the pore region is associated with enhanced ion conductivity.

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.

A structural model of the copper ATPase ATP7B to facilitate analysis of Wilson disease-causing mutations and studies of the transport mechanism

The copper-transporting ATPase ATP7B has an essential role in human physiology, particularly for the liver and brain function. Inactivation of ATP7B is associated with a severe hepato-neurologic disorder, Wilson disease (WD). Hundreds of WD related mutations have been identified in ATP7B to date. The low frequency and the compound-heterozygous nature of causative mutations complicate the analysis of individual mutants and the establishment of genotype-phenotype correlations. To facilitate studies of disease-causing mutations and mechanistic understanding of WD, we have homology-modelled the ATP7B core (residues 643-1377) using the recent structure of the bacterial copper-ATPase LCopA as a template. The model, supported by evolutionary conservation and hydrophobicity analysis, as well as existing and new mutagenesis data, allows molecular interpretations of experimentally characterized clinical mutations. We also illustrate that structure and conservation can be used to grade potential deleterious effects for many WD mutations, which were clinically detected but have not yet been experimentally characterized. Finally, we compare the structural features of ATP7B and LCopA and discuss specific features of the eukaryotic copper pump.

FunFOLDQA: a quality assessment tool for protein-ligand binding site residue predictions

The estimation of prediction quality is important because without quality measures, it is difficult to determine the usefulness of a prediction. Currently, methods for ligand binding site residue predictions are assessed in the function prediction category of the biennial Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiment, utilizing the Matthews Correlation Coefficient (MCC) and Binding-site Distance Test (BDT) metrics. However, the assessment of ligand binding site predictions using such metrics requires the availability of solved structures with bound ligands. Thus, we have developed a ligand binding site quality assessment tool, FunFOLDQA, which utilizes protein feature analysis to predict ligand binding site quality prior to the experimental solution of the protein structures and their ligand interactions. The FunFOLDQA feature scores were combined using: simple linear combinations, multiple linear regression and a neural network. The neural network produced significantly better results for correlations to both the MCC and BDT scores, according to Kendall’s τ, Spearman’s ρ and Pearson’s r correlation coefficients, when tested on both the CASP8 and CASP9 datasets. The neural network also produced the largest Area Under the Curve score (AUC) when Receiver Operator Characteristic (ROC) analysis was undertaken for the CASP8 dataset. Furthermore, the FunFOLDQA algorithm incorporating the neural network, is shown to add value to FunFOLD, when both methods are employed in combination. This results in a statistically significant improvement over all of the best server methods, the FunFOLD method (6.43%), and one of the top manual groups (FN293) tested on the CASP8 dataset. The FunFOLDQA method was also found to be competitive with the top server methods when tested on the CASP9 dataset. To the best of our knowledge, FunFOLDQA is the first attempt to develop a method that can be used to assess ligand binding site prediction quality, in the absence of experimental data.

Improving a consensus approach for protein structure selection by removing redundancy

In protein tertiary structure prediction, a crucial step is to select near-native structures from a large number of predicted structural models. Over the years, extensive research has been conducted for the protein structure selection problem with most approaches focusing on developing more accurate energy or scoring functions. Despite significant advances in this area, the discerning power of current approaches is still unsatisfactory. In this paper, we propose a novel consensus-based algorithm for the selection of predicted protein structures. Given a set of predicted models, our method first removes redundant structures to derive a subset of reference models. Then, a structure is ranked based on its average pairwise similarity to the reference models. Using the CASP8 data set containing a large collection of predicted models for 122 targets, we compared our method with the best CASP8 quality assessment (QA) servers, which are all consensus based, and showed that our QA scores correlate better with the GDT-TSs than those of the CASP8 QA servers. We also compared our method with the state-of-the-art scoring functions and showed its improved performance for near-native model selection. The GDT-TSs of the top models picked by our method are on average more than 8 percent better than the ones selected by the best performing scoring function.

Evaluation of model quality predictions in CASP9

CASP has been assessing the state of the art in the a priori estimation of accuracy of protein structure prediction since 2006. The inclusion of model quality assessment category in CASP contributed to a rapid development of methods in this area. In the last experiment, 46 quality assessment groups tested their approaches to estimate the accuracy of protein models as a whole and/or on a per-residue basis. We assessed the performance of these methods predominantly on the basis of the correlation between the predicted and observed quality of the models on both global and local scales. The ability of the methods to identify the models closest to the best one, to differentiate between good and bad models, and to identify well modeled regions was also analyzed. Our evaluations demonstrate that even though global quality assessment methods seem to approach perfection point (weighted average per-target Pearson's correlation coefficients are as high as 0.97 for the best groups), there is still room for improvement. First, all top-performing methods use consensus approaches to generate quality estimates, and this strategy has its own limitations. Second, the methods that are based on the analysis of individual models lag far behind clustering techniques and need a boost in performance. The methods for estimating per-residue accuracy of models are less accurate than global quality assessment methods, with an average weighted per-model correlation coefficient in the range of 0.63-0.72 for the best 10 groups.

The alternating-access mechanism of MFS transporters arises from inverted-topology repeats

Lactose permease (LacY) is the prototype of the major facilitator superfamily (MFS) of secondary transporters. Available structures of LacY reveal a state in which the substrate is exposed to the cytoplasm but is occluded from the periplasm. However, the alternating-access transport mechanism requires the existence of a periplasm-facing state. We recently showed that inverted-topology structural repeats provide the foundation for the mechanisms of two transporter families with folds distinct from the MFS. Here, we generated a structural model of LacY by swapping the conformations of inverted-topology repeats identified in its two domains. The model exhibits all required properties of an outward-facing conformation, i.e., closure of the binding site to the cytoplasm and exposure to the periplasm. Furthermore, the model agrees with double electron-electron resonance distance changes, accessibility to cysteine-modifying reagents, cysteine cross-linking data, and a recent structure of a distantly related transporter. Analysis of the intradomain differences between the two states suggests a role for conserved sequence motifs in occluding the central pathway through kinking of the pore-lining helices. In addition, predicted re-pairing of critical salt-bridging residues in the binding sites agrees remarkably well with previous proposals, allowing a description of the proton/sugar transport mechanism. More fundamentally, our model demonstrates that inverted-topology repeats provide the foundation for the alternating-access mechanisms of MFS transporters.