Applications In Structural Biology Research Articles

Protein interface classification by evolutionary analysis

BackgroundDistinguishing biologically relevant interfaces from lattice contacts in protein crystals is a fundamental problem in structural biology. Despite efforts towards the computational prediction of interface character, many issues are still unresolved.ResultsWe present here a protein-protein interface classifier that relies on evolutionary data to detect the biological character of interfaces. The classifier uses a simple geometric measure, number of core residues, and two evolutionary indicators based on the sequence entropy of homolog sequences. Both aim at detecting differential selection pressure between interface core and rim or rest of surface. The core residues, defined as fully buried residues (>95% burial), appear to be fundamental determinants of biological interfaces: their number is in itself a powerful discriminator of interface character and together with the evolutionary measures it is able to clearly distinguish evolved biological contacts from crystal ones. We demonstrate that this definition of core residues leads to distinctively better results than earlier definitions from the literature. The stringent selection and quality filtering of structural and sequence data was key to the success of the method. Most importantly we demonstrate that a more conservative selection of homolog sequences - with relatively high sequence identities to the query - is able to produce a clearer signal than previous attempts.ConclusionsAn evolutionary approach like the one presented here is key to the advancement of the field, which so far was missing an effective method exploiting the evolutionary character of protein interfaces. Its coverage and performance will only improve over time thanks to the incessant growth of sequence databases. Currently our method reaches an accuracy of 89% in classifying interfaces of the Ponstingl 2003 datasets and it lends itself to a variety of useful applications in structural biology and bioinformatics. We made the corresponding software implementation available to the community as an easy-to-use graphical web interface at http://www.eppic-web.org.

Detection of disordered regions in globular proteins using 13C‐detected NMR

Characterization of disordered regions in globular proteins constitutes a significant challenge. Here, we report an approach based on ¹³C-detected nuclear magnetic resonance experiments for the identification and assignment of disordered regions in large proteins. Using this method, we demonstrate that disordered fragments can be accurately identified in two homologs of menin, a globular protein with a molecular weight over 50 kDa. Our work provides an efficient way to characterize disordered fragments in globular proteins for structural biology applications.

A toolkit and benchmark study for FRET-restrained high-precision structural modeling

We present a comprehensive toolkit for Förster resonance energy transfer (FRET)-restrained modeling of biomolecules and their complexes for quantitative applications in structural biology. A dramatic improvement in the precision of FRET-derived structures is achieved by explicitly considering spatial distributions of dye positions, which greatly reduces uncertainties due to flexible dye linkers. The precision and confidence levels of the models are calculated by rigorous error estimation. The accuracy of this approach is demonstrated by docking a DNA primer-template to HIV-1 reverse transcriptase. The derived model agrees with the known X-ray structure with an r.m.s. deviation of 0.5 Å. Furthermore, we introduce FRET-guided 'screening' of a large structural ensemble created by molecular dynamics simulations. We used this hybrid approach to determine the formerly unknown configuration of the flexible single-strand template overhang.

Focus on vacuum and cryogenics

Focus on vacuum and cryogenics

A Comparative Assessment of Ranking Accuracies of Conventional and Machine-Learning-Based Scoring Functions for Protein-Ligand Binding Affinity Prediction

Accurately predicting the binding affinities of large sets of protein-ligand complexes efficiently is a key challenge in computational biomolecular science, with applications in drug discovery, chemical biology, and structural biology. Since a scoring function (SF) is used to score, rank, and identify drug leads, the fidelity with which it predicts the affinity of a ligand candidate for a protein's binding site has a significant bearing on the accuracy of virtual screening. Despite intense efforts in developing conventional SFs, which are either force-field based, knowledge-based, or empirical, their limited ranking accuracy has been a major roadblock toward cost-effective drug discovery. Therefore, in this work, we explore a range of novel SFs employing different machine-learning (ML) approaches in conjunction with a variety of physicochemical and geometrical features characterizing protein-ligand complexes. We assess the ranking accuracies of these new ML-based SFs as well as those of conventional SFs in the context of the 2007 and 2010 PDBbind benchmark data sets on both diverse and protein-family-specific test sets. We also investigate the influence of the size of the training data set and the type and number of features used on ranking accuracy. Within clusters of protein-ligand complexes with different ligands bound to the same target protein, we find that the best ML-based SF is able to rank the ligands correctly based on their experimentally determined binding affinities 62.5 percent of the time and identify the top binding ligand 78.1 percent of the time. For this SF, the Spearman correlation coefficient between ranks of ligands ordered by predicted and experimentally determined binding affinities is 0.771. Given the challenging nature of the ranking problem and that SFs are used to screen millions of ligands, this represents a significant improvement over the best conventional SF we studied, for which the corresponding ranking performance values are 57.8 percent, 73.4 percent, and 0.677.

Purification and Concentration of Alphavirus

The alphaviruses Semliki Forest virus and Sindbis virus have been used frequently as expression vectors in vitro and in vivo. Usually, these systems consist of replication-deficient vectors that require a helper vector for packaging of recombinant particles. Replication-proficient vectors have also been engineered. Alphaviral vectors can be used as nucleic-acid-based vectors (DNA and RNA) or infectious particles. High-titer viral production is achieved in <2 d. The broad host range of alphaviruses facilitates studies in mammalian and nonmammalian cell lines, primary cells in culture, and in vivo. The strong preference for expression in neuronal cells has made alphaviruses particularly useful in neurobiological studies. Unfortunately, their strong cytotoxic effect on host cells, relatively short-term transient expression patterns, and the reasonably high cost of viral production remain drawbacks. However, novel mutant alphaviruses have showed reduced cytotoxicity and prolonged expression. Membrane proteins (which are generally difficult to express at high levels in recombinant systems) have generated high yields and facilitate applications in structural biology. Alphaviruses have also been applied in vaccine development and gene therapy. Generally, purification or concentration of alphaviruses is not necessary. However, for instance, the medium derived from baby hamster kidney cells is toxic to primary neurons in culture. Including a purification step substantially improves the survival of the transduced neurons. Viral concentration and purification may also be advantageous for in vivo studies in animal models and are mandatory for clinical applications. This protocol describes three methods for purification and concentration of alphavirus.

Generation of Recombinant Alphaviral Vectors

The alphaviruses Semliki Forest virus and Sindbis virus have been used frequently as expression vectors in vitro and in vivo. Usually, these systems consist of replication-deficient vectors that require a helper vector for packaging of recombinant particles. Replication-proficient vectors have also been engineered. Alphaviral vectors can be used as nucleic-acid-based vectors (DNA and RNA) or infectious particles. High-titer viral production is achieved in <2 d. The broad host range of alphaviruses facilitates studies in mammalian and nonmammalian cell lines, primary cells in culture, and in vivo. The strong preference for expression in neuronal cells has made alphaviruses particularly useful in neurobiological studies. Unfortunately, their strong cytotoxic effect on host cells, relatively short-term transient expression patterns, and the reasonably high cost of viral production remain drawbacks. However, novel mutant alphaviruses have showed reduced cytotoxicity and prolonged expression. Membrane proteins (which are generally difficult to express at high levels in recombinant systems) have generated high yields and facilitate applications in structural biology. Alphaviruses have also been applied in vaccine development and gene therapy. This protocol describes the production of recombinant alphaviral vectors. The SFV- and SIN-based expression systems apply two vectors for recombinant particle production. In addition to the RNA-based vectors described here, DNA vectors with cytomegalovirus or other RNA polymerase type II promoters can be used for direct plasmid DNA transfections. Cotransfection of SFV-based pSCA expression and pSCA helper vectors generates recombinant viral particles.

ChemInform Abstract: The Application of DNA and RNA G‐Quadruplexes to Therapeutic Medicines

AbstractReview: 255 refs.

Statistical Potential for Modeling and Ranking of Protein–Ligand Interactions

Applications in structural biology and medicinal chemistry require protein-ligand scoring functions for two distinct tasks: (i) ranking different poses of a small molecule in a protein binding site and (ii) ranking different small molecules by their complementarity to a protein site. Using probability theory, we developed two atomic distance-dependent statistical scoring functions: PoseScore was optimized for recognizing native binding geometries of ligands from other poses and RankScore was optimized for distinguishing ligands from nonbinding molecules. Both scores are based on a set of 8,885 crystallographic structures of protein-ligand complexes but differ in the values of three key parameters. Factors influencing the accuracy of scoring were investigated, including the maximal atomic distance and non-native ligand geometries used for scoring, as well as the use of protein models instead of crystallographic structures for training and testing the scoring function. For the test set of 19 targets, RankScore improved the ligand enrichment (logAUC) and early enrichment (EF(1)) scores computed by DOCK 3.6 for 13 and 14 targets, respectively. In addition, RankScore performed better at rescoring than each of seven other scoring functions tested. Accepting both the crystal structure and decoy geometries with all-atom root-mean-square errors of up to 2 Å from the crystal structure as correct binding poses, PoseScore gave the best score to a correct binding pose among 100 decoys for 88% of all cases in a benchmark set containing 100 protein-ligand complexes. PoseScore accuracy is comparable to that of DrugScore(CSD) and ITScore/SE and superior to 12 other tested scoring functions. Therefore, RankScore can facilitate ligand discovery, by ranking complexes of the target with different small molecules; PoseScore can be used for protein-ligand complex structure prediction, by ranking different conformations of a given protein-ligand pair. The statistical potentials are available through the Integrative Modeling Platform (IMP) software package (http://salilab.org/imp) and the LigScore Web server (http://salilab.org/ligscore/).

The Joint Center for Structural Genomics: exploration of the human gut microbiome

For more than a decade, the Joint Center for Structural Genomics (JCSG) [1] has been at the forefront of developing tools and methodologies that allow the application of high-throughput structural biology to a broad range of biological and biomedical investigations. In the previous phases of the National Institutes of Health’s Protein Structure Initiative (PSI; 2000 to 2010) [2], we explored structural coverage of uncharted regions of the protein universe [3], as well as a single organism, allowing complete structural reconstruction of the metabolic network of Thermotoga maritima[4]. In the current phase (PSI: Biology; 2010 to 2015), the JCSG is leveraging its high-throughput platform to explore the structural basis for host-microbe interactions in the human microbiome. The emerging field of metagenomics has been particularly enlightening: the human gut microbiome sequencing projects have already uncovered fascinating new families and expansions of known families for adaptation to this environment. The gut microbiota is dominated by poorly characterized bacterial phyla, which contain an unusually high number of uncharacterized proteins that are largely unstudied. Their influence upon human development, physiology, immunity and nutrition is only starting to surface and is thus an exciting new frontier for structural genomics, where we can structurally investigate the contributions of these microorganisms to human health and disease. The JCSG is located at The Scripps Research Institute, the Genomics Institute of the Novartis Research Foundation, University of California at San Diego, the Sanford-Burnham Medical Research Institute and SSRL/Stanford University.

The application of DNA and RNA G-quadruplexes to therapeutic medicines

The intriguing structural diversity in folded topologies available to guanine-rich nucleic acid repeat sequences have made four-stranded G-quadruplex structures the focus of both basic and applied research, from cancer biology and novel therapeutics through to nanoelectronics. Distributed widely in the human genome as targets for regulating gene expression and chromosomal maintenance, they offer unique avenues for future cancer drug development. In particular, the recent advances in chemical and structural biology have enabled the construction of bespoke selective DNA based aptamers to be used as novel therapeutic agents and access to detailed structural models for structure based drug discovery. In this critical review, we will explore the important underlying characteristics of G-quadruplexes that make them functional, stable, and predictable nanoscaffolds. We will review the current structural database of folding topologies, molecular interfaces and novel interaction surfaces, with a consideration to their future exploitation in drug discovery, molecular biology, supermolecular assembly and aptamer design. In recent years the number of potential applications for G-quadruplex motifs has rapidly grown, so in this review we aim to explore the many future challenges and highlight where possible successes may lie. We will highlight the similarities and differences between DNA and RNA folded G-quadruplexes in terms of stability, distribution, and exploitability as small molecule targets. Finally, we will provide a detailed review of basic G-quadruplex geometry, experimental tools used, and a critical evaluation of the application of high-resolution structural biology and its ability to provide meaningful and valid models for future applications (255 references).

A fast, efficient and sequence-independent method for flexible multiple segmental isotope labeling of RNA using ribozyme and RNase H cleavage

Structural information on RNA, emerging more and more as a major regulator in gene expression, dramatically lags behind compared with information on proteins. Although NMR spectroscopy has proven to be an excellent tool to solve RNA structures, it is hampered by the severe spectral resonances overlap found in RNA, limiting its use for large RNA molecules. Segmental isotope labeling of RNA or ligation of a chemically synthesized RNA containing modified nucleotides with an unmodified RNA fragment have proven to have high potential in overcoming current limitations in obtaining structural information on RNA. However, low yields, cumbersome preparations and sequence requirements have limited its broader application in structural biology. Here we present a fast and efficient approach to generate multiple segmentally labeled RNAs with virtually no sequence requirements with very high yields (up to 10-fold higher than previously reported). We expect this approach to open new avenues in structural biology of RNA.

Structural Mass Spectrometry in Protein Therapeutics Discovery

The protein therapeutics market is one of the highest growing segments of the pharmaceutical industry with an estimated global market value of $77 billion by 2011 (Global Protein Therapeutics Market report by RNCOS: Delhi, India, 2009). This growth has been fueled by several advantages that protein drugs can offer such as higher specificity, reduced side effects, and faster development time compared to small molecule drugs. Major pharmaceutical companies are strategically shifting gears toward protein therapeutics and gradually increasing the biologics portion of their pipelines. Consequently, in the present pharmaceutical industry, there is a rapid growth in the number and types of protein structural mass spectrometry analyses, particularly during the discovery phase where an abundance of new drug candidates are being investigated. This perspective article discusses the role of protein structural mass spectrometry during the discovery of protein therapeutics with focus on recombinant protein production quality control and structural biology applications. The current challenges in technologies associated with this field and the analytical prospects for the future direction will be also discussed.

ESPRIT: An automated, library-based method for mapping and soluble expression of protein domains from challenging targets

Expression of sufficient quantities of soluble protein for structural biology and other applications is often a very difficult task, especially when multimilligram quantities are required. In order to improve yield, solubility or crystallisability of a protein, it is common to subclone shorter genetic constructs corresponding to single- or multi-domain fragments. However, it is not always clear where domain boundaries are located, especially when working on novel targets with little or no sequence similarity to other proteins. Several methods have been described employing aspects of directed evolution to the recombinant expression of challenging proteins. These combine the construction of a random library of genetic constructs of a target with a screening or selection process to identify solubly expressing protein fragments. Here we review several datasets from the ESPRIT (Expression of Soluble Proteins by Random Incremental Truncation) technology to provide a view on its capabilities. Firstly, we demonstrate how it functions using the well-characterised NF-kappaB p50 transcription factor as a model system. Secondly, application of ESPRIT to the challenging PB2 subunit of influenza polymerase has led to several novel atomic resolution structures; here we present an overview of the screening phase of that project. Thirdly, analysis of the human kinase TBK1 is presented to show how the ESPRIT technology rapidly addresses the compatibility of challenging targets with the Escherichia coli expression system.

The eNMR platform for structural biology

The e-NMR project is a European cooperation initiative that aims at providing the bio-NMR user community with a software platform integrating and streamlining the computational approaches necessary for the analysis of bio-NMR data. The e-NMR platform is based on a Grid computational infrastructure. A main focus of the current implementation of the e-NMR platform is on streamlining structure determination protocols. Indeed, to facilitate the use of NMR spectroscopy in the life sciences, the eNMR consortium has set out to provide protocolized services through easy-to-use web interfaces, while still retaining sufficient flexibility to handle specific requests by expert users. Various programs relevant for structural biology applications are already available through the e-NMR portal, including HADDOCK, XPLOR-NIH, CYANA and csRosetta. The implementation of these services, and in particular the distribution of calculations to the GRID infrastructure, has required the development of specific tools. However, the GRID infrastructure is maintained completely transparent to the users. With more than 150 registered users, eNMR is currently the second largest European Virtual Organization in the life sciences.

ESBTL: efficient PDB parser and data structure for the structural and geometric analysis of biological macromolecules

The ever increasing number of structural biological data calls for robust and efficient software for analysis. Easy Structural Biology Template Library (ESBTL) is a lightweight C++ library that allows the handling of PDB data and provides a data structure suitable for geometric constructions and analyses. The parser and data model provided by this ready-to-use include-only library allows adequate treatment of usually discarded information (insertion code, atom occupancy, etc.) while still being able to detect badly formatted files. The template-based structure allows rapid design of new computational structural biology applications and is fully compatible with the new remediated PDB archive format. It also allows the code to be easy-to-use while being versatile enough to allow advanced user developments. ESBTL is freely available under the GNU General Public License from http://esbtl.sf.net. The web site provides the source code, examples, code snippets and documentation.

A microfluidic reactor for rapid, low‐pressure proteolysis with on‐chip electrospray ionization

A microfluidic reactor that enables rapid digestion of proteins prior to on-line analysis by electrospray ionization mass spectrometry (ESI-MS) is introduced. The device incorporates a wide (1.5 cm), shallow (10 microm) reactor 'well' that is functionalized with pepsin-agarose, a design that facilitates low-pressure operation and high clogging resistance. Electrospray ionization is carried out directly from a short metal capillary integrated into the chip outlet. Fabrication, involving laser ablation of polymethyl methacrylate (PMMA), is exceedingly straightforward and inexpensive. High sequence coverage spectra of myoglobin (Mb), ubiquitin (Ub) and bovine serum albumin (BSA) digests were obtained after <4 s of residence time in the reactor. Stress testing showed little loss of performance over approximately 2 h continuous use at high flow rates (30 microL/min). The device provides a convenient platform for a range of applications in proteomics and structural biology, i.e. to enable high-throughput workflows or to limit back-exchange in spatially resolved hydrogen/deuterium exchange (HDX) experiments.

Aberration‐corrected microscopy for structural biology applications

This study explores the potential of a C(s)-corrected transmission electron microscope for structural studies of biological samples, in particular isolated macromolecular complexes. A 300-kV transmission electron microscope, equipped with a C(s) corrector was employed to record sets of images at different defocus and C(s) settings. The experiments were designed to determine whether imaging with large defocus benefits from C(s) correction. Defocus contrast in biological imaging has a stronger influence on image resolution than any other parameter. We find the results are in good agreement with theoretical framework, verifying that the typical imaging conditions required for biological investigations are not affected by C(s) correction.

Computing the arrangement of circles on a sphere, with applications in structural biology

Balls and spheres are the simplest modeling primitives after affine ones, which accounts for their ubiquitousness in Computer Science and Applied Mathematics. Amongst the many applications, we may cite their prevalence when it comes to modeling our ambient 3D space, or to handle molecular shapes using Van der Waals models. If most of the applications developed so far are based upon simple geometric tests between balls, in particular the intersection test, a number of applications would obviously benefit from finer pieces of information. Consider a sphere S 0 and a list of circles on it, each such circle stemming from the intersection between S 0 and another sphere, say S i . Also assume that S i has an accompanying ball B i . This paper develops an integrated framework, based on the generalization of the Bentley–Ottmann algorithm to the spherical setting, to (i) compute the exact arrangement of circles on S 0 (ii) construct in a single pass the half-edge data structure encoding the arrangement induced by the circles (iii) report the covering list of each face of this arrangement, i.e. the list of balls containing it. As an illustration, the covering lists are used as the building block of a geometric optimization algorithm aiming at selecting diverse conformational ensembles for flexible protein–protein docking.

Mocr: A novel fusion tag for enhancing solubility that is compatible with structural biology applications

A persistent problem in heterologous protein production is insolubility of the target protein when expressed to high level in the host cell. A widely employed strategy for overcoming this problem is the use of fusion tags. The best fusion tags promote solubility, may function as purification handles and either do not interfere with downstream applications or may be removed from the passenger protein preparation. A novel fusion tag is identified that meets these criteria. This fusion tag is a monomeric mutant of the Ocr protein (0.3 gene product) of bacteriophage T7. This fusion tag displays solubilizing activity with a variety of different passenger proteins. We show that it may be used as a purification handle similar to other fusion tags. Its small size and compact structure are compatible with its use in downstream applications of the passenger protein or it may be removed and purified away from the passenger protein. The use of monomeric Ocr (Mocr) as a complement to other fusion tags such as maltose-binding protein will provide greater flexibility in protein production and processing for a wide variety of protein applications.