The Protein Model Portal--a comprehensive resource for protein structure and model information

Estimation of Uncertainties in the Global Distance Test (GDT_TS) for CASP Models

The Critical Assessment of techniques for protein Structure Prediction (or CASP) is a community-wide blind test experiment to reveal the best accomplishments of structure modeling. Assessors have been using the Global Distance Test (GDT_TS) measure to quantify prediction performance since CASP3 in 1998. However, identifying significant score differences between close models is difficult because of the lack of uncertainty estimations for this measure. Here, we utilized the atomic fluctuations caused by structure flexibility to estimate the uncertainty of GDT_TS scores. Structures determined by nuclear magnetic resonance are deposited as ensembles of alternative conformers that reflect the structural flexibility, whereas standard X-ray refinement produces the static structure averaged over time and space for the dynamic ensembles. To recapitulate the structural heterogeneous ensemble in the crystal lattice, we performed time-averaged refinement for X-ray datasets to generate structural ensembles for our GDT_TS uncertainty analysis. Using those generated ensembles, our study demonstrates that the time-averaged refinements produced structure ensembles with better agreement with the experimental datasets than the averaged X-ray structures with B-factors. The uncertainty of the GDT_TS scores, quantified by their standard deviations (SDs), increases for scores lower than 50 and 70, with maximum SDs of 0.3 and 1.23 for X-ray and NMR structures, respectively. We also applied our procedure to the high accuracy version of GDT-based score and produced similar results with slightly higher SDs. To facilitate score comparisons by the community, we developed a user-friendly web server that produces structure ensembles for NMR and X-ray structures and is accessible at http://prodata.swmed.edu/SEnCS. Our work helps to identify the significance of GDT_TS score differences, as well as to provide structure ensembles for estimating SDs of any scores.

CoinFold: a web server for protein contact prediction and contact-assisted protein folding

CoinFold (http://raptorx2.uchicago.edu/ContactMap/) is a web server for protein contact prediction and contact-assisted de novo structure prediction. CoinFold predicts contacts by integrating joint multi-family evolutionary coupling (EC) analysis and supervised machine learning. This joint EC analysis is unique in that it not only uses residue coevolution information in the target protein family, but also that in the related families which may have divergent sequences but similar folds. The supervised learning further improves contact prediction accuracy by making use of sequence profile, contact (distance) potential and other information. Finally, this server predicts tertiary structure of a sequence by feeding its predicted contacts and secondary structure to the CNS suite. Tested on the CASP and CAMEO targets, this server shows significant advantages over existing ones of similar category in both contact and tertiary structure prediction.

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.

Phylogenetic analysis of β-xylanase SRXL1 of Sporisorium reilianum and its relationship with families (GH10 and GH11) of Ascomycetes and Basidiomycetes

In this paper, the amino acid sequence of the β-xylanase SRXL1 of Sporisorium reilianum, which is a pathogenic fungus of maize was used as a model protein to find its phylogenetic relationship with other xylanases of Ascomycetes and Basidiomycetes and the information obtained allowed to establish a hypothesis of monophyly and of biological role. 84 amino acid sequences of β-xylanase obtained from the GenBank database was used. Groupings analysis of higher-level in the Pfam database allowed to determine that the proteins under study were classified into the GH10 and GH11 families, based on the regions of highly conserved amino acids, 233-318 and 180-193 respectively, where glutamate residues are responsible for the catalysis.

ABCB4 mutations in adult patients with cholestatic liver disease: impact and phenotypic expression

BACKGROUND

The ABCB4 gene encodes the MDR3 protein. Mutations of this gene cause progressive familial intrahepatic cholestasis type 3 (PFIC3) in children, but their clinical relevance in adults remains ill defined. The study of a well-characterized adult patient series may contribute to refining the genetic data regarding cholangiopathies of unknown origin. Our aim was to evaluate the impact of ABCB4 mutations on clinical expression of cholestasis in adult patients.

METHODS

We consecutively evaluated 2602 subjects with hepatobiliary disease. Biochemical evidence of a chronic cholestatic profile (CCP) with elevated serum gamma-glutamyltransferase activity or diagnosis of intrahepatic cholestasis of pregnancy (ICP) and juvenile cholelithiasis (JC) were inclusion criteria. The personal/family history of additional cholestatic liver disease (PFH-CLD), which includes ICP, JC, or hormone-induced cholestasis, was investigated. Mutation screening of ABCB4 was carried out in 90 patients with idiopathic chronic cholestasis (ICC), primary biliary cirrhosis (PBC), primary sclerosing cholangitis (PSC), ICP, and JC.

RESULTS

Eighty patients had CCP. PSC and ICC patients with PFH-CLD had earlier onset of disease than those without it (p = 0.003 and p = 0.023, respectively). The mutation frequency ranged from 50% (ICP, JC) to 17.6% (PBC). Among CCP patients, presence or absence of PFH-CLD was associated with ABCB4 mutations in 26.8 vs 5.1% (p = 0.013), respectively; in the subset of ICC and PSC patients, the corresponding figures were 44.4 vs 0% (p = 0.012) and 28.6 vs 8.7% (p = 0.173).

CONCLUSIONS

Cholangiopathies attributable to highly penetrant ABCB4 mutant alleles are identifiable in a substantial proportion of adults that generally have PFH-CLD. In PSC and ICC phenotypes, patients with MDR3 deficiency have early onset of disease.

Building a virtual ligand screening pipeline using free software: a survey

Virtual screening, the search for bioactive compounds via computational methods, provides a wide range of opportunities to speed up drug development and reduce the associated risks and costs. While virtual screening is already a standard practice in pharmaceutical companies, its applications in preclinical academic research still remain under-exploited, in spite of an increasing availability of dedicated free databases and software tools. In this survey, an overview of recent developments in this field is presented, focusing on free software and data repositories for screening as alternatives to their commercial counterparts, and outlining how available resources can be interlinked into a comprehensive virtual screening pipeline using typical academic computing facilities. Finally, to facilitate the set-up of corresponding pipelines, a downloadable software system is provided, using platform virtualization to integrate pre-installed screening tools and scripts for reproducible application across different operating systems.

Structural analysis of dihydrofolate reductases enables rationalization of antifolate binding affinities and suggests repurposing possibilities

Antifolates are competitive inhibitors of dihydrofolate reductase (DHFR), a conserved enzyme that is central to metabolism and widely targeted in pathogenic diseases, cancer and autoimmune disorders. Although most clinically used antifolates are known to be target specific, some display a fair degree of cross-reactivity with DHFRs from other species. A method that enables identification of determinants of affinity and specificity in target DHFRs from different species and provides guidelines for the design of antifolates is currently lacking. To address this, we first captured the potential druggable space of a DHFR in a substructure called the 'supersite' and classified supersites of DHFRs from 56 species into 16 'site-types' based on pairwise structural similarity. Analysis of supersites across these site-types revealed that DHFRs exhibit varying extents of dissimilarity at structurally equivalent positions in and around the binding site. We were able to explain the pattern of affinities towards chemically diverse antifolates exhibited by DHFRs of different site-types based on these structural differences. We then generated an antifolate-DHFR network by mapping known high-affinity antifolates to their respective supersites and used this to identify antifolates that can be repurposed based on similarity between supersites or antifolates. Thus, we identified 177 human-specific and 458 pathogen-specific antifolates, a large number of which are supported by available experimental data. Thus, in the light of the clinical importance of DHFR, we present a novel approach to identifying differences in the druggable space of DHFRs that can be utilized for rational design of antifolates.

Improved de novo structure prediction in CASP11 by incorporating coevolution information into Rosetta

We describe CASP11 de novo blind structure predictions made using the Rosetta structure prediction methodology with both automatic and human assisted protocols. Model accuracy was generally improved using coevolution derived residue-residue contact information as restraints during Rosetta conformational sampling and refinement, particularly when the number of sequences in the family was more than three times the length of the protein. The highlight was the human assisted prediction of T0806, a large and topologically complex target with no homologs of known structure, which had unprecedented accuracy-<3.0 Å root-mean-square deviation (RMSD) from the crystal structure over 223 residues. For this target, we increased the amount of conformational sampling over our fully automated method by employing an iterative hybridization protocol. Our results clearly demonstrate, in a blind prediction scenario, that coevolution derived contacts can considerably increase the accuracy of template-free structure modeling. Proteins 2016; 84(Suppl 1):67-75. © 2015 Wiley Periodicals, Inc.

New Biological Insights from Better Structure Models

Structure validation is a key component of all steps in the structure determination process, from structure building, refinement, deposition, and evaluation all the way to post-deposition optimisation of structures in the Protein Data Bank (PDB) by re-refinement and re-building. Today, many aspects of protein structures are understood better than 10years ago, and combined with improved software and more computing power, the automated PDB_REDO procedure can significantly improve about 85% of all X-ray structures ever deposited in the PDB. We review structure validation, structure improvement, and a series of validation resources and facilities that give access to improved PDB files and to reports on the quality of the original and the improved structures. Post-deposition optimisation generally leads to improved protein structures and a series of examples will illustrate how that, in turn, leads to improved or even novel biological insights.

Comparative analyses of the proteins from Mycobacterium tuberculosis and human genomes: Identification of potential tuberculosis drug targets

Tuberculosis, one of the major infectious diseases affecting human beings is caused by the bacillus Mycobacterium tuberculosis. Increased resistance to known drugs commonly used for the treatment of tuberculosis has created an urgent need to identify new targets for validation and to develop drugs. In this study, we have used various bioinformatics tools, to compare the protein sequences from twenty-three M. tuberculosis genome strains along with the known human protein sequences, in order to identify the 'conserved' M. tuberculosis proteins absent in human. Further, based on the analysis of protein interaction networks, we selected one-hundred and forty proteins that were predicted as potential M. tuberculosis drug targets and prioritized according to the ranking of 'clusters' of interacting proteins. Comparison of the predicted 140 TB targets with literature indicated that 46 of them were previously reported, thereby increasing the confidence in our predictions of the remaining 94 targets too. The analyses of the structures and functions corresponding to the predicted potential TB drug targets indicated a diverse range of proteins that included ten 'druggable' targets with some of the known drugs.

ProQ2: estimation of model accuracy implemented in Rosetta

Motivation: Model quality assessment programs are used to predict the quality of modeled protein structures. They can be divided into two groups depending on the information they are using: ensemble methods using consensus of many alternative models and methods only using a single model to do its prediction. The consensus methods excel in achieving high correlations between prediction and true quality measures. However, they frequently fail to pick out the best possible model, nor can they be used to generate and score new structures. Single-model methods on the other hand do not have these inherent shortcomings and can be used both to sample new structures and to improve existing consensus methods.

Results: Here, we present an implementation of the ProQ2 program to estimate both local and global model accuracy as part of the Rosetta modeling suite. The current implementation does not only make it possible to run large batch runs locally, but it also opens up a whole new arena for conformational sampling using machine learned scoring functions and to incorporate model accuracy estimation in to various existing modeling schemes. ProQ2 participated in CASP11 and results from CASP11 are used to benchmark the current implementation. Based on results from CASP11 and CAMEO-QE, a continuous benchmark of quality estimation methods, it is clear that ProQ2 is the single-model method that performs best in both local and global model accuracy.

Availability and implementation:https://github.com/bjornwallner/ProQ_scripts

Contact:bjornw@ifm.liu.se

Supplementary information:Supplementary data are available at Bioinformatics online.

In silico Identification and Characterization of Protein-Ligand Binding Sites

Protein-ligand binding site prediction methods aim to predict, from amino acid sequence, protein-ligand interactions, putative ligands, and ligand binding site residues using either sequence information, structural information, or a combination of both. In silico characterization of protein-ligand interactions has become extremely important to help determine a protein's functionality, as in vivo-based functional elucidation is unable to keep pace with the current growth of sequence databases. Additionally, in vitro biochemical functional elucidation is time-consuming, costly, and may not be feasible for large-scale analysis, such as drug discovery. Thus, in silico prediction of protein-ligand interactions must be utilized to aid in functional elucidation. Here, we briefly discuss protein function prediction, prediction of protein-ligand interactions, the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and the Continuous Automated EvaluatiOn (CAMEO) competitions, along with their role in shaping the field. We also discuss, in detail, our cutting-edge web-server method, FunFOLD for the structurally informed prediction of protein-ligand interactions. Furthermore, we provide a step-by-step guide on using the FunFOLD web server and FunFOLD3 downloadable application, along with some real world examples, where the FunFOLD methods have been used to aid functional elucidation.

Protein Structure Databases

Web-based protein structure databases come in a wide variety of types and levels of information content. Those having the most general interest are the various atlases that describe each experimentally determined protein structure and provide useful links, analyses, and schematic diagrams relating to its 3D structure and biological function. Also of great interest are the databases that classify 3D structures by their folds as these can reveal evolutionary relationships which may be hard to detect from sequence comparison alone. Related to these are the numerous servers that compare folds-particularly useful for newly solved structures, and especially those of unknown function. Beyond these are a vast number of databases for the more specialized user, dealing with specific families, diseases, structural features, and so on.

Protein Structure and Function Prediction Using I-TASSER

I-TASSER is a hierarchical protocol for automated protein structure prediction and structure-based function annotation. Starting from the amino acid sequence of target proteins, I-TASSER first generates full-length atomic structural models from multiple threading alignments and iterative structural assembly simulations followed by atomic-level structure refinement. The biological functions of the protein, including ligand-binding sites, enzyme commission number, and gene ontology terms, are then inferred from known protein function databases based on sequence and structure profile comparisons. I-TASSER is freely available as both an on-line server and a stand-alone package. This unit describes how to use the I-TASSER protocol to generate structure and function prediction and how to interpret the prediction results, as well as alternative approaches for further improving the I-TASSER modeling quality for distant-homologous and multi-domain protein targets.

Proteins and Their Interacting Partners: An Introduction to Protein-Ligand Binding Site Prediction Methods

Elucidating the biological and biochemical roles of proteins, and subsequently determining their interacting partners, can be difficult and time consuming using in vitro and/or in vivo methods, and consequently the majority of newly sequenced proteins will have unknown structures and functions. However, in silico methods for predicting protein-ligand binding sites and protein biochemical functions offer an alternative practical solution. The characterisation of protein-ligand binding sites is essential for investigating new functional roles, which can impact the major biological research spheres of health, food, and energy security. In this review we discuss the role in silico methods play in 3D modelling of protein-ligand binding sites, along with their role in predicting biochemical functionality. In addition, we describe in detail some of the key alternative in silico prediction approaches that are available, as well as discussing the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and the Continuous Automated Model EvaluatiOn (CAMEO) projects, and their impact on developments in the field. Furthermore, we discuss the importance of protein function prediction methods for tackling 21st century problems.

The SIB Swiss Institute of Bioinformatics' resources: focus on curated databases

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article.

Unexpected features of the dark proteome

We surveyed the "dark" proteome-that is, regions of proteins never observed by experimental structure determination and inaccessible to homology modeling. For 546,000 Swiss-Prot proteins, we found that 44-54% of the proteome in eukaryotes and viruses was dark, compared with only ∼14% in archaea and bacteria. Surprisingly, most of the dark proteome could not be accounted for by conventional explanations, such as intrinsic disorder or transmembrane regions. Nearly half of the dark proteome comprised dark proteins, in which the entire sequence lacked similarity to any known structure. Dark proteins fulfill a wide variety of functions, but a subset showed distinct and largely unexpected features, such as association with secretion, specific tissues, the endoplasmic reticulum, disulfide bonding, and proteolytic cleavage. Dark proteins also had short sequence length, low evolutionary reuse, and few known interactions with other proteins. These results suggest new research directions in structural and computational biology.

Automatic generation of bioinformatics tools for predicting protein-ligand binding sites

MOTIVATION

Predictive tools that model protein-ligand binding on demand are needed to promote ligand research in an innovative drug-design environment. However, it takes considerable time and effort to develop predictive tools that can be applied to individual ligands. An automated production pipeline that can rapidly and efficiently develop user-friendly protein-ligand binding predictive tools would be useful.

RESULTS

We developed a system for automatically generating protein-ligand binding predictions. Implementation of this system in a pipeline of Semantic Web technique-based web tools will allow users to specify a ligand and receive the tool within 0.5-1 day. We demonstrated high prediction accuracy for three machine learning algorithms and eight ligands.

AVAILABILITY AND IMPLEMENTATION

The source code and web application are freely available for download at http://utprot.net They are implemented in Python and supported on Linux.

CONTACT

shimizu@bi.a.u-tokyo.ac.jp

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

MECP2 missense mutations outside the canonical MBD and TRD domains in males with intellectual disability

Methyl-CpG binding protein 2 (MeCP2) is a nuclear protein highly expressed in neurons that is involved in transcriptional modulation and chromatin remodeling. Mutations in MECP2 in females are associated with Rett syndrome, a neurological disorder characterized by a normal neonatal period, followed by the arrest of development and regression of acquired skills. Although it was initially thought that MECP2 pathogenic mutations in males were not compatible with life, starting from 1999 about 60 male patients have been identified and their phenotype varies from severe neonatal encephalopathy to mild intellectual disability. Targeted Next Generation Sequencing of a panel of intellectual disability related genes was performed on two unrelated male patients, and two missense variants in MECP2 were identified (p.Gly185Val and p.Arg167Trp). These variants lie outside the canonical MBD and TRD domains, where the pathogenicity of missense variants is more difficult to establish. In both families, variants were found in all affected siblings and were inherited from the asymptomatic mother, showing skewed X-chromosome inactivation. We report here the first missense variant located in AT-hook domain 1 and we underline the importance of MECP2 substitutions outside the canonical MeCP2 domains in X-linked intellectual disability.

Antisera Against Certain Conserved Surface-Exposed Peptides of Nontypeable Haemophilus influenzae Are Protective

Nontypeable Haemophilus influenzae (NTHi) cause significant disease, including otitis media in children, exacerbations of chronic obstructive pulmonary disease, and invasive disease in susceptible populations. No vaccine is currently available to prevent NTHi disease. The interactions of NTHi and the human host are primarily mediated by lipooligosaccharide and a complex array of surface-exposed proteins (SEPs) that act as receptors, sensors and secretion systems. We hypothesized that certain SEPs are present in all NTHi strains and that a subset of these may be antibody accessible and represent protective epitopes. Initially we used 15 genomic sequences available in the GenBank database along with an additional 11 genomic sequences generated by ourselves to identify the core set of putative SEPs present in all strains. Using bioinformatics, 56 core SEPs were identified. Molecular modeling generated putative structures of the SEPs from which potential surface exposed regions were defined. Synthetic peptides corresponding to ten of these highly conserved surface-exposed regions were used to raise antisera in rats. These antisera were used to assess passive protection in the infant rat model of invasive NTHi infection. Five of the antisera were protective, thus demonstrating their in vivo antibody accessibility. These five peptide regions represent potential targets for peptide vaccine candidates to protect against NTHi infection.

Dynamic profiling of double-stranded RNA binding proteins

Double-stranded (ds) RNA is a key player in numerous biological activities in cells, including RNA interference, anti-viral immunity and mRNA transport. The class of proteins responsible for recognizing dsRNA is termed double-stranded RNA binding proteins (dsRBP). However, little is known about the molecular mechanisms underlying the interaction between dsRBPs and dsRNA. Here we examined four human dsRBPs, ADAD2, TRBP, Staufen 1 and ADAR1 on six dsRNA substrates that vary in length and secondary structure. We combined single molecule pull-down (SiMPull), single molecule protein-induced fluorescence enhancement (smPIFE) and molecular dynamics (MD) simulations to investigate the dsRNA-dsRBP interactions. Our results demonstrate that despite the highly conserved dsRNA binding domains, the dsRBPs exhibit diverse substrate specificities and dynamic properties when in contact with different RNA substrates. While TRBP and ADAR1 have a preference for binding simple duplex RNA, ADAD2 and Staufen1 display higher affinity to highly structured RNA substrates. Upon interaction with RNA substrates, TRBP and Staufen1 exhibit dynamic sliding whereas two deaminases ADAR1 and ADAD2 mostly remain immobile when bound. MD simulations provide a detailed atomic interaction map that is largely consistent with the affinity differences observed experimentally. Collectively, our study highlights the diverse nature of substrate specificity and mobility exhibited by dsRBPs that may be critical for their cellular function.

Outcome of the First wwPDB Hybrid/Integrative Methods Task Force Workshop

Structures of biomolecular systems are increasingly computed by integrative modeling that relies on varied types of experimental data and theoretical information. We describe here the proceedings and conclusions from the first wwPDB Hybrid/Integrative Methods Task Force Workshop held at the European Bioinformatics Institute in Hinxton, UK, on October 6 and 7, 2014. At the workshop, experts in various experimental fields of structural biology, experts in integrative modeling and visualization, and experts in data archiving addressed a series of questions central to the future of structural biology. How should integrative models be represented? How should the data and integrative models be validated? What data should be archived? How should the data and models be archived? What information should accompany the publication of integrative models?

Novel Drug Targets for Food-Borne Pathogen Campylobacter jejuni: An Integrated Subtractive Genomics and Comparative Metabolic Pathway Study

Campylobacters are a major global health burden and a cause of food-borne diarrheal illness and economic loss worldwide. In developing countries, Campylobacter infections are frequent in children under age two and may be associated with mortality. In developed countries, they are a common cause of bacterial diarrhea in early adulthood. In the United States, antibiotic resistance against Campylobacter is notably increased from 13% in 1997 to nearly 25% in 2011. Novel drug targets are urgently needed but remain a daunting task to accomplish. We suggest that omics-guided drug discovery is timely and worth considering in this context. The present study employed an integrated subtractive genomics and comparative metabolic pathway analysis approach. We identified 16 unique pathways from Campylobacter when compared against H. sapiens with 326 non-redundant proteins; 115 of these were found to be essential in the Database of Essential Genes. Sixty-six proteins among these were non-homologous to the human proteome. Six membrane proteins, of which four are transporters, have been proposed as potential vaccine candidates. Screening of 66 essential non-homologous proteins against DrugBank resulted in identification of 34 proteins with drug-ability potential, many of which play critical roles in bacterial growth and survival. Out of these, eight proteins had approved drug targets available in DrugBank, the majority serving crucial roles in cell wall synthesis and energy metabolism and therefore having the potential to be utilized as drug targets. We conclude by underscoring that screening against these proteins with inhibitors may aid in future discovery of novel therapeutics against campylobacteriosis in ways that will be pathogen specific, and thus have minimal toxic effect on host. Omics-guided drug discovery and bioinformatics analyses offer the broad potential for veritable advances in global health relevant novel therapeutics.

Buried ionizable networks are an ancient hallmark of G protein-coupled receptor activation

Seven-transmembrane receptors (7TMRs) have evolved in prokaryotes and eukaryotes over hundreds of millions of years. Comparative structural analysis suggests that these receptors may share a remote evolutionary origin, despite their lack of sequence similarity. Here we used structure-based computations to compare 221 7TMRs from all domains of life. Unexpectedly, we discovered that these receptors contain spatially conserved networks of buried ionizable groups. In microbial 7TMRs these networks are used to pump ions across the cell membrane in response to light. In animal 7TMRs, which include light- and ligand-activated G protein-coupled receptors (GPCRs), homologous networks were found to be characteristic of activated receptor conformations. These networks are likely relevant to receptor function because they connect the ligand-binding pocket of the receptor to the nucleotide-binding pocket of the G protein. We propose that agonist and G protein binding facilitate the formation of these electrostatic networks and promote important structural rearrangements such as the displacement of transmembrane helix-6. We anticipate that robust classification of activated GPCR structures will aid the identification of ligands that target activated GPCR structural states.

RBO Aleph: leveraging novel information sources for protein structure prediction

RBO Aleph is a novel protein structure prediction web server for template-based modeling, protein contact prediction and ab initio structure prediction. The server has a strong emphasis on modeling difficult protein targets for which templates cannot be detected. RBO Aleph's unique features are (i) the use of combined evolutionary and physicochemical information to perform residue–residue contact prediction and (ii) leveraging this contact information effectively in conformational space search. RBO Aleph emerged as one of the leading approaches to ab initio protein structure prediction and contact prediction during the most recent Critical Assessment of Protein Structure Prediction experiment (CASP11, 2014). In addition to RBO Aleph's main focus on ab initio modeling, the server also provides state-of-the-art template-based modeling services. Based on template availability, RBO Aleph switches automatically between template-based modeling and ab initio prediction based on the target protein sequence, facilitating use especially for non-expert users. The RBO Aleph web server offers a range of tools for visualization and data analysis, such as the visualization of predicted models, predicted contacts and the estimated prediction error along the model's backbone. The server is accessible at http://compbio.robotics.tu-berlin.de/rbo_aleph/.

I-TASSER server: new development for protein structure and function predictions

The I-TASSER server (http://zhanglab.ccmb.med.umich.edu/I-TASSER) is an online resource for automated protein structure prediction and structure-based function annotation. In I-TASSER, structural templates are first recognized from the PDB using multiple threading alignment approaches. Full-length structure models are then constructed by iterative fragment assembly simulations. The functional insights are finally derived by matching the predicted structure models with known proteins in the function databases. Although the server has been widely used for various biological and biomedical investigations, numerous comments and suggestions have been reported from the user community. In this article, we summarize recent developments on the I-TASSER server, which were designed to address the requirements from the user community and to increase the accuracy of modeling predictions. Focuses have been made on the introduction of new methods for atomic-level structure refinement, local structure quality estimation and biological function annotations. We expect that these new developments will improve the quality of the I-TASSER server and further facilitate its use by the community for high-resolution structure and function prediction.

IntFOLD: an integrated server for modelling protein structures and functions from amino acid sequences

IntFOLD is an independent web server that integrates our leading methods for structure and function prediction. The server provides a simple unified interface that aims to make complex protein modelling data more accessible to life scientists. The server web interface is designed to be intuitive and integrates a complex set of quantitative data, so that 3D modelling results can be viewed on a single page and interpreted by non-expert modellers at a glance. The only required input to the server is an amino acid sequence for the target protein. Here we describe major performance and user interface updates to the server, which comprises an integrated pipeline of methods for: tertiary structure prediction, global and local 3D model quality assessment, disorder prediction, structural domain prediction, function prediction and modelling of protein-ligand interactions. The server has been independently validated during numerous CASP (Critical Assessment of Techniques for Protein Structure Prediction) experiments, as well as being continuously evaluated by the CAMEO (Continuous Automated Model Evaluation) project. The IntFOLD server is available at: http://www.reading.ac.uk/bioinf/IntFOLD/.

Discovery of novel new Delhi metallo-β-lactamases-1 inhibitors by multistep virtual screening

The emergence of NDM-1 containing multi-antibiotic resistant "Superbugs" necessitates the needs of developing of novel NDM-1inhibitors. In this study, we report the discovery of novel NDM-1 inhibitors by multi-step virtual screening. From a 2,800,000 virtual drug-like compound library selected from the ZINC database, we generated a focused NDM-1 inhibitor library containing 298 compounds of which 44 chemical compounds were purchased and evaluated experimentally for their ability to inhibit NDM-1 in vitro. Three novel NDM-1 inhibitors with micromolar IC50 values were validated. The most potent inhibitor, VNI-41, inhibited NDM-1 with an IC50 of 29.6 ± 1.3 μM. Molecular dynamic simulation revealed that VNI-41 interacted extensively with the active site. In particular, the sulfonamide group of VNI-41 interacts directly with the metal ion Zn1 that is critical for the catalysis. These results demonstrate the feasibility of applying virtual screening methodologies in identifying novel inhibitors for NDM-1, a metallo-β-lactamase with a malleable active site and provide a mechanism base for rational design of NDM-1 inhibitors using sulfonamide as a functional scaffold.

C-Terminally fused affinity Strep-tag II is removed by proteolysis from recombinant human erythropoietin expressed in transgenic tobacco plants

KEY MESSAGE

C -terminally fused Strep -tag II is removed from rhuEPO expressed in tobacco plants. The finding suggests that direct fusion of purification tags at the C -terminus of rhuEPO should be avoided. Asialo-erythropoietin (asialo-EPO), a desialylated form of EPO, is a potent tissue-protective agent. Recently, we and others have exploited a low-cost plant-based expression system to produce recombinant human asialo-EPO (asialo-rhuEPO(P)). To facilitate purification from plant extracts, Strep-tag II was engineered at the C-terminus of EPO. Although asialo-rhuEPO(P) was efficiently expressed in transgenic tobacco plants, affinity purification based on Strep -tag II did not result in the recovery of the protein. In this study, we investigated the stability of Strep-tag II tagged asialo-rhuEPO(P) expressed in tobacco plants to understand whether this fused tag is cleaved or inaccessible. Sequencing RT-PCR products confirmed that fused DNA sequences encoding Strep-tag II were properly transcribed, and three-dimensional protein structure model revealed that the tag must be fully accessible. However, Western blot analysis of leaf extracts and purified asialo-rhuEPO(P) revealed that the Strep-tag II was absent on the protein. Additionally, no peptide fragment containing Strep-tag II was identified in the LC-MS/MS analysis of purified protein further supporting that the affinity tag was absent on asialo-rhuEPO(P). However, Strep-tag II was detected on asialo-rhuEPO(P) that was retained in the endoplasmic reticulum, suggesting that the Strep-tag II is removed during protein secretion or extraction. These findings together with recent reports that C-terminally fused Strep-tag II or IgG Fc domain are also removed from EPO in tobacco plants, suggest that its C-terminus may be highly susceptible to proteolysis in tobacco plants. Therefore, direct fusion of purification tags at the C-terminus of EPO should be avoided while expressing it in tobacco plants.

Haemoglobin, a new major allergen of Anisakis simplex

Gastro-allergic anisakiasis and Anisakis sensitisation associated chronic urticaria are diseases which differ in their IgE and IgG4 responses against both crude extract and specific allergens. Anisakis and Ascaris are closely related nematodes that usually cause problems with specificity in immunodiagnostics. In this study we measured IgE and IgG4 antibodies against Anisakis simplex sensu lato (s. l.) and Ascaris suum haemoglobins in sera of 21 gastro-allergic anisakiasis and 23 chronic urticaria patients. We used a capture ELISA with the anti-Anisakis haemoglobin monoclonal antibody 4E8g, which also recognises Ascaris haemoglobin. In addition, we determined specific IgE and IgG4 to both nematodes by indirect ELISA and immunoblotting. Anti-A. simplex s. l. haemoglobin IgE and IgG4 levels were higher in gastro-allergic anisakiasis than in chronic urticaria patients (P=0.002 and 0.026, respectively). Surprisingly, no patient had detectable IgE levels against A. suum haemoglobin. Finally, we carried out an in silico study of the B-cell epitopes of both haemoglobin molecules. Five epitopes were predicted in Anisakis pegreffii and four in A. suum haemoglobin. The epitope propensity values of Anisakis haemoglobin in the equivalent IgE binding region of the allergenic haemoglobin Chi t 1 from Chironomus thummi, were higher those of the Ascaris haemoglobin. In conclusion, we describe A. simplex haemoglobin as a new major allergen (Ani s 13), being recognised by a large number (64.3%) of sensitised patients and up to 80.9% in patients with gastro-allergic anisakiasis. The presence of a specific epitope and the different values of epitope propensity between Anisakis and Ascaris haemoglobin could explain the lack of cross-reactivity between the two molecules. The absence of IgE reactivity to Ascaris haemoglobin in Anisakis patients makes Anisakis haemoglobin (Ani s 13) a potential candidate for developing more specific diagnosis tools.

Assessing the local structural quality of transmembrane protein models using statistical potentials (QMEANBrane)

Motivation: Membrane proteins are an important class of biological macromolecules involved in many cellular key processes including signalling and transport. They account for one third of genes in the human genome and >50% of current drug targets. Despite their importance, experimental structural data are sparse, resulting in high expectations for computational modelling tools to help fill this gap. However, as many empirical methods have been trained on experimental structural data, which is biased towards soluble globular proteins, their accuracy for transmembrane proteins is often limited.

Results: We developed a local model quality estimation method for membrane proteins (‘QMEANBrane’) by combining statistical potentials trained on membrane protein structures with a per-residue weighting scheme. The increasing number of available experimental membrane protein structures allowed us to train membrane-specific statistical potentials that approach statistical saturation. We show that reliable local quality estimation of membrane protein models is possible, thereby extending local quality estimation to these biologically relevant molecules.

Availability and implementation: Source code and datasets are available on request.

Contact:torsten.schwede@unibas.ch

Supplementary Information:Supplementary data are available at Bioinformatics online.

Protein structure annotation resources

A key reason three-dimensional (3-D) protein structures are annotated with supporting or derived information is to understand the molecular basis of protein function. To this end, protein structure annotation databases curate key facts and observations, based on community-accepted standards, about the ~100,000 3-D experimental protein structures to date. This review will introduce the primary structure repositories, databases, and value-added structural annotation databases, as well as the range of information they provide. The different levels of annotation data (primary vs. derived vs. inferred) and how they should all be considered accordingly will also be described.

Computational peptide vaccinology

Immunoinformatics focuses on modeling immune responses for better understanding of the immune system and in many cases for proposing agents able to modify the immune system. The most classical of these agents are vaccines derived from living organisms such as smallpox or polio. More modern vaccines comprise recombinant proteins, protein domains, and in some cases peptides. Generating a vaccine from peptides however requires technologies and concepts very different from classical vaccinology. Immunoinformatics therefore provides the computational tools to propose peptides suitable for formulation into vaccines. This chapter introduces the essential biological concepts affecting design and efficacy of peptide vaccines and discusses current methods and workflows applied to design successful peptide vaccines using computers.

Data to knowledge: how to get meaning from your result

Structural and functional studies require the development of sophisticated 'Big Data' technologies and software to increase the knowledge derived and ensure reproducibility of the data. This paper presents summaries of the Structural Biology Knowledge Base, the VIPERdb Virus Structure Database, evaluation of homology modeling by the Protein Model Portal, the ProSMART tool for conformation-independent structure comparison, the LabDB 'super' laboratory information management system and the Cambridge Structural Database. These techniques and technologies represent important tools for the transformation of crystallographic data into knowledge and information, in an effort to address the problem of non-reproducibility of experimental results.

A computational analysis of the structural determinants of APOBEC3's catalytic activity and vulnerability to HIV-1 Vif

APOBEC3s (A3) are Zn(2+) dependent cytidine deaminases with diverse biological functions and implications for cancer and immunity. Four of the seven human A3s restrict HIV by 'hypermutating' the reverse-transcribed viral genomic DNA. HIV Virion Infectivity Factor (Vif) counters this restriction by targeting A3s to proteasomal degradation. However, there is no apparent correlation between catalytic activity, Vif binding, and sequence similarity between A3 domains. Our comparative structural analysis reveals features required for binding Vif and features influencing polynucleotide deaminase activity in A3 proteins. All Vif-binding A3s share a negatively charged surface region that includes residues previously implicated in binding the highly-positively charged Vif. Additionally, catalytically active A3s share a positively charged groove near the Zn(2+) coordinating active site, which may accommodate the negatively charged polynucleotide substrate. Our findings suggest surface electrostatics, as well as the spatial extent of substrate accommodating region, are critical determinants of substrate and Vif binding across A3 proteins with implications for anti-retroviral and anti-cancer therapeutic design.

Uncertainty in integrative structural modeling

Integrative structural modeling uses multiple types of input information and proceeds in four stages: (i) gathering information, (ii) designing model representation and converting information into a scoring function, (iii) sampling good-scoring models, and (iv) analyzing models and information. In the first stage, uncertainty originates from data that are sparse, noisy, ambiguous, or derived from heterogeneous samples. In the second stage, uncertainty can originate from a representation that is too coarse for the available information or a scoring function that does not accurately capture the information. In the third stage, the major source of uncertainty is insufficient sampling. In the fourth stage, clustering, cross-validation, and other methods are used to estimate the precision and accuracy of the models and information.

Comparative Protein Structure Modeling Using MODELLER

Functional characterization of a protein sequence is one of the most frequent problems in biology. This task is usually facilitated by accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.

In vitro activation of NAD-dependent alcohol dehydrogenases by Nudix hydrolases is more widespread than assumed

In the Gram-positive methylotroph Bacillus methanolicus, methanol oxidation is catalyzed by an NAD-dependent methanol dehydrogenase (Mdh) that belongs to the type III alcohol dehydrogenase (Adh) family. It was previously shown that the in vitro activity of B. methanolicus Mdh is increased by the endogenous activator protein Act, a Nudix hydrolase. Here we show that this feature is not unique, but more widespread among type III Adhs in combination with Act or other Act-like Nudix hydrolases. In addition, we studied the effect of site directed mutations in the predicted active site of Mdh and two other type III Adhs with regard to activity and activation by Act.

The phosphoinositide-dependent protein kinase 1 inhibitor, UCN-01, induces fragmentation: possible role of metalloproteinases

Phosphoinositide-dependent protein kinase 1 (PDK1) is a key enzyme, master regulator of cellular proliferation and metabolism; it is considered a key target for pharmacological intervention. Using membranes obtained from DDT1 MF-2 cells, phospho-PDK1 was identified by Western blotting, as two major protein bands of Mr 58-68 kDa. Cell incubation with the PDK1 inhibitor, UCN-01, induced a time- and concentration-dependent decrease in the amount of phospho-PDK1 with a concomitant appearance of a ≈42 kDa phosphorylated fragment. Knocking down PDK1 diminished the amount of phospho-PDK1 detected in membranes, accompanied by similarly decreased fragment generation. UCN-01-induced fragment generation was also observed in membranes from cells stably expressing a myc-tagged PDK1 construct. Other PDK1 inhibitors were also tested: OSU-03012 induced a clear decrease in phospho-PDK1 and increased the presence of the phosphorylated fragment in membrane preparations; in contrast, GSK2334470 and staurosporine induced only marginal increases in the amount of PDK1 fragment. Galardin and batimastat, two metalloproteinase inhibitors, markedly attenuated inhibitor-induced PDK1 fragment generation. Metalloproteinases 2, 3, and 9 co-immunoprecipitated with myc-PDK1 under baseline conditions and this interaction was stimulated by UCN-01; batimastat also markedly diminished this effect of the PDK1 inhibitor. Our results indicate that a series of protein kinase inhibitors, namely UCN-01 and OSU-03012 and to a lesser extent GSK2334470 and staurosporine induce PDK1 fragmentation and suggest that metalloproteinases could participate in this effect.

SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information

Protein structure homology modelling has become a routine technique to generate 3D models for proteins when experimental structures are not available. Fully automated servers such as SWISS-MODEL with user-friendly web interfaces generate reliable models without the need for complex software packages or downloading large databases. Here, we describe the latest version of the SWISS-MODEL expert system for protein structure modelling. The SWISS-MODEL template library provides annotation of quaternary structure and essential ligands and co-factors to allow for building of complete structural models, including their oligomeric structure. The improved SWISS-MODEL pipeline makes extensive use of model quality estimation for selection of the most suitable templates and provides estimates of the expected accuracy of the resulting models. The accuracy of the models generated by SWISS-MODEL is continuously evaluated by the CAMEO system. The new web site allows users to interactively search for templates, cluster them by sequence similarity, structurally compare alternative templates and select the ones to be used for model building. In cases where multiple alternative template structures are available for a protein of interest, a user-guided template selection step allows building models in different functional states. SWISS-MODEL is available at http://swissmodel.expasy.org/.

Protein modeling: what happened to the "protein structure gap"?

Computational modeling of three-dimensional macromolecular structures and complexes from their sequence has been a long-standing vision in structural biology. Over the last 2 decades, a paradigm shift has occurred: starting from a large "structure knowledge gap" between the huge number of protein sequences and small number of known structures, today, some form of structural information, either experimental or template-based models, is available for the majority of amino acids encoded by common model organism genomes. With the scientific focus of interest moving toward larger macromolecular complexes and dynamic networks of interactions, the integration of computational modeling methods with low-resolution experimental techniques allows the study of large and complex molecular machines. One of the open challenges for computational modeling and prediction techniques is to convey the underlying assumptions, as well as the expected accuracy and structural variability of a specific model, which is crucial to understanding its limitations.

Comparative Modeling of Drug Target Proteins☆

In this perspective, we begin by describing the comparative protein structure modeling technique and the accuracy of the corresponding models. We then discuss the significant role that comparative prediction plays in drug discovery. We focus on virtual ligand screening against comparative models and illustrate the state-of-the-art by a number of specific examples.

An association-adjusted consensus deleterious scheme to classify homozygous Mis-sense mutations for personal genome interpretation

BACKGROUND

Personal genome analysis is now being considered for evaluation of disease risk in healthy individuals, utilizing both rare and common variants. Multiple scores have been developed to predict the deleteriousness of amino acid substitutions, using information on the allele frequencies, level of evolutionary conservation, and averaged structural evidence. However, agreement among these scores is limited and they likely over-estimate the fraction of the genome that is deleterious.

METHOD

This study proposes an integrative approach to identify a subset of homozygous non-synonymous single nucleotide polymorphisms (nsSNPs). An 8-level classification scheme is constructed from the presence/absence of deleterious predictions combined with evidence of association with disease or complex traits. Detailed literature searches and structural validations are then performed for a subset of homozygous 826 mis-sense mutations in 575 proteins found in the genomes of 12 healthy adults.

RESULTS

Implementation of the Association-Adjusted Consensus Deleterious Scheme (AACDS) classifies 11% of all predicted highly deleterious homozygous variants as most likely to influence disease risk. The number of such variants per genome ranges from 0 to 8 with no significant difference between African and Caucasian Americans. Detailed analysis of mutations affecting the APOE, MTMR2, THSB1, CHIA, αMyHC, and AMY2A proteins shows how the protein structure is likely to be disrupted, even though the associated phenotypes have not been documented in the corresponding individuals.

CONCLUSIONS

The classification system for homozygous nsSNPs provides an opportunity to systematically rank nsSNPs based on suggestive evidence from annotations and sequence-based predictions. The ranking scheme, in-depth literature searches, and structural validations of highly prioritized mis-sense mutations compliment traditional sequence-based approaches and should have particular utility for the development of individualized health profiles. An online tool reporting the AACDS score for any variant is provided at the authors' website.

PortEco: a resource for exploring bacterial biology through high-throughput data and analysis tools

PortEco (http://porteco.org) aims to collect, curate and provide data and analysis tools to support basic biological research in Escherichia coli (and eventually other bacterial systems). PortEco is implemented as a ‘virtual’ model organism database that provides a single unified interface to the user, while integrating information from a variety of sources. The main focus of PortEco is to enable broad use of the growing number of high-throughput experiments available for E. coli, and to leverage community annotation through the EcoliWiki and GONUTS systems. Currently, PortEco includes curated data from hundreds of genome-wide RNA expression studies, from high-throughput phenotyping of single-gene knockouts under hundreds of annotated conditions, from chromatin immunoprecipitation experiments for tens of different DNA-binding factors and from ribosome profiling experiments that yield insights into protein expression. Conditions have been annotated with a consistent vocabulary, and data have been consistently normalized to enable users to find, compare and interpret relevant experiments. PortEco includes tools for data analysis, including clustering, enrichment analysis and exploration via genome browsers. PortEco search and data analysis tools are extensively linked to the curated gene, metabolic pathway and regulation content at its sister site, EcoCyc.

ModBase, a database of annotated comparative protein structure models and associated resources

ModBase (http://salilab.org/modbase) is a database of annotated comparative protein structure models. The models are calculated by ModPipe, an automated modeling pipeline that relies primarily on Modeller for fold assignment, sequence-structure alignment, model building and model assessment (http://salilab.org/modeller/). ModBase currently contains almost 30 million reliable models for domains in 4.7 million unique protein sequences. ModBase allows users to compute or update comparative models on demand, through an interface to the ModWeb modeling server (http://salilab.org/modweb). ModBase models are also available through the Protein Model Portal (http://www.proteinmodelportal.org/). Recently developed associated resources include the AllosMod server for modeling ligand-induced protein dynamics (http://salilab.org/allosmod), the AllosMod-FoXS server for predicting a structural ensemble that fits an SAXS profile (http://salilab.org/allosmod-foxs), the FoXSDock server for protein–protein docking filtered by an SAXS profile (http://salilab.org/foxsdock), the SAXS Merge server for automatic merging of SAXS profiles (http://salilab.org/saxsmerge) and the Pose & Rank server for scoring protein–ligand complexes (http://salilab.org/poseandrank). In this update, we also highlight two applications of ModBase: a PSI:Biology initiative to maximize the structural coverage of the human alpha-helical transmembrane proteome and a determination of structural determinants of human immunodeficiency virus-1 protease specificity.