Coordinate Files Research Articles

PolySac3DB: an annotated data base of 3 dimensional structures of polysaccharides

BackgroundPolysaccharides are ubiquitously present in the living world. Their structural versatility makes them important and interesting components in numerous biological and technological processes ranging from structural stabilization to a variety of immunologically important molecular recognition events. The knowledge of polysaccharide three-dimensional (3D) structure is important in studying carbohydrate-mediated host-pathogen interactions, interactions with other bio-macromolecules, drug design and vaccine development as well as material science applications or production of bio-ethanol.DescriptionPolySac3DB is an annotated database that contains the 3D structural information of 157 polysaccharide entries that have been collected from an extensive screening of scientific literature. They have been systematically organized using standard names in the field of carbohydrate research into 18 categories representing polysaccharide families. Structure-related information includes the saccharides making up the repeat unit(s) and their glycosidic linkages, the expanded 3D representation of the repeat unit, unit cell dimensions and space group, helix type, diffraction diagram(s) (when applicable), experimental and/or simulation methods used for structure description, link to the abstract of the publication, reference and the atomic coordinate files for visualization and download. The database is accompanied by a user-friendly graphical user interface (GUI). It features interactive displays of polysaccharide structures and customized search options for beginners and experts, respectively. The site also serves as an information portal for polysaccharide structure determination techniques. The web-interface also references external links where other carbohydrate-related resources are available.ConclusionPolySac3DB is established to maintain information on the detailed 3D structures of polysaccharides. All the data and features are available via the web-interface utilizing the search engine and can be accessed at http://polysac3db.cermav.cnrs.fr.

Proteins of Unknown Function in the Protein Data Bank (PDB): An Inventory of True Uncharacterized Proteins and Computational Tools for Their Analysis

Proteins of uncharacterized functions form a large part of many of the currently available biological databases and this situation exists even in the Protein Data Bank (PDB). Our analysis of recent PDB data revealed that only 42.53% of PDB entries (1084 coordinate files) that were categorized under “unknown function” are true examples of proteins of unknown function at this point in time. The remainder 1465 entries also annotated as such appear to be able to have their annotations re-assessed, based on the availability of direct functional characterization experiments for the protein itself, or for homologous sequences or structures thus enabling computational function inference.

Quantum.Ligand.Dock: protein-ligand docking with quantum entanglement refinement on a GPU system

Quantum.Ligand.Dock (protein–ligand docking with graphic processing unit (GPU) quantum entanglement refinement on a GPU system) is an original modern method for in silico prediction of protein–ligand interactions via high-performance docking code. The main flavour of our approach is a combination of fast search with a special account for overlooked physical interactions. On the one hand, we take care of self-consistency and proton equilibria mutual effects of docking partners. On the other hand, Quantum.Ligand.Dock is the the only docking server offering such a subtle supplement to protein docking algorithms as quantum entanglement contributions. The motivation for development and proposition of the method to the community hinges upon two arguments—the fundamental importance of quantum entanglement contribution in molecular interaction and the realistic possibility to implement it by the availability of supercomputing power. The implementation of sophisticated quantum methods is made possible by parallelization at several bottlenecks on a GPU supercomputer. The high-performance implementation will be of use for large-scale virtual screening projects, structural bioinformatics, systems biology and fundamental research in understanding protein–ligand recognition. The design of the interface is focused on feasibility and ease of use. Protein and ligand molecule structures are supposed to be submitted as atomic coordinate files in PDB format. A customization section is offered for addition of user-specified charges, extra ionogenic groups with intrinsic pKa values or fixed ions. Final predicted complexes are ranked according to obtained scores and provided in PDB format as well as interactive visualization in a molecular viewer. Quantum.Ligand.Dock server can be accessed at http://87.116.85.141/LigandDock.html.

GIS에 의한 3차원 동물서식도 제작

현재 세계에 밝혀진 생물종은 160만여 종으로 한국에 밝혀진 자생종은 3만여 종으로 기록되어 있으며, 한국 국립공원 생물종은 1만5천여 종에 이르고 있다. 이 중 한국 국립공원내 멸종위기종은 133종으로 전체 멸종위기종의 60%를 차지하고 있으며, 이들의 보호와 관리를 위해 실질적인 분포와 이들의 서식지 관계에 대한 것을 바탕으로 한 연구가 필요한 실정이다. 본 연구에서는 지리산국립공원 내에 분포하는 동물 개체군을 대상으로 GIS 프로그램을 통해 주요 서식지를 등록하고, 3차원 서식도를 제작함으로써 국립공원에 서식 중인 동물의 보전과 관리를 위한 새로운 방안을 제시하고자 하였다. 동식물보호단 동물좌표 파일과 국립공원 경계 폴리곤파일 및 ASTER GDEM 수치표고모델을 이용하여 서식도를 제작하였다. 최신의 지형정보와 서식지정보의 융합을 통해 구축된 새로운 개념의 동물서식도는 향후, 동물의 종별 보호 관리 계획 및 서식지 보호 계획 시 유용한 정보로 활용될 것이다. At present, about 1.6 million bio-species have been discovered in the world. Approximately 30 thousand indigenes have been recorded in Korea and about fifteen thousand species of biology inhabit in Korea national park. Korea national park where has been lived 133 species at 60 percent of endangered species is a very important wildlife protection area. The construction of database about substantive distribution and habitat of wildlife is urgently needed to protect and manage endangered species. In this study, main habitats about animals were registered using GIS program in Jirisan National Park and 3 dimensional habitat map was produced. Also, new plan was suggested to preserve and manage animals in national parks by producing 3 dimensional habitat map. The habitat map was produced using coordinate file of animals, polygon file about boundary of national park, and ASTER GDEM. New conceptional animal habitat map will be used by means of the valuable information when the plans for preservation/management and habitat protection about animals are designed.

Exosite Interactions Impact Matrix Metalloproteinase Collagen Specificities

Members of the matrix metalloproteinase (MMP) family selectively cleave collagens in vivo. However, the substrate structural determinants that facilitate interaction with specific MMPs are not well defined. We hypothesized that type I-III collagen sequences located N- or C-terminal to the physiological cleavage site mediate substrate selectivity among MMP-1, MMP-2, MMP-8, MMP-13, and MMP-14/membrane-type 1 (MT1)-MMP. The enzyme kinetics for hydrolysis of three fluorogenic triple-helical peptides (fTHPs) was evaluated herein. The first fTHP contained consensus residues 769-783 from type I-III collagens, the second inserted α1(II) collagen residues 763-768 N-terminal to the consensus sequence, and the third inserted α1(II) collagen residues 784-792 C-terminal to the consensus sequence. Our analyses showed that insertion of the C-terminal residues significantly increased k(cat)/K(m) and k(cat) for MMP-1. MMP-13 showed the opposite behavior with a decreased k(cat)/K(m) and k(cat) and a greatly improved K(m) in response to the C-terminal residues. Insertion of the N-terminal residues enhanced k(cat)/K(m) and k(cat) for MMP-8 and MT1-MMP. For MMP-2, the C-terminal residues enhanced K(m) and dramatically decreased k(cat), resulting in a decrease in the overall activity. These changes in activities and kinetic parameters represented the collagen preferences of MMP-8, MMP-13, and MT1-MMP well. Thus, interactions with secondary binding sites (exosites) helped direct the specificity of these enzymes. However, MMP-1 collagen preferences were not recapitulated by the fTHP studies. The preference of MMP-1 for type III collagen appears to be primarily based on the flexibility of the hydrolysis site of type III collagen compared with types I and II. Further characterization of exosite determinants that govern interactions of MMPs with collagenous substrates should aid the development of pharmacotherapeutics that target individual MMPs.

Electrostatic Interactions Involving the Extreme C Terminus of Nuclear Export Factor CRM1 Modulate Its Affinity for Cargo

The toroid-shaped nuclear protein export factor CRM1 is constructed from 21 tandem HEAT repeats, each of which contains an inner (B) and outer (A) α-helix joined by loops. Proteins targeted for export have a nuclear export signal (NES) that binds between the A-helices of HEAT repeats 11 and 12 on the outer surface of CRM1. RanGTP binding increases the affinity of CRM1 for NESs. In the absence of RanGTP, the CRM1 C-terminal helix, together with the HEAT repeat 9 loop, modulates its affinity for NESs. Here we show that there is an electrostatic interaction between acidic residues at the extreme distal tip of the C-terminal helix and basic residues on the HEAT repeat 12 B-helix that lies on the inner surface of CRM1 beneath the NES binding site. Small angle x-ray scattering indicates that the increased affinity for NESs generated by mutations in the C-terminal helix is not associated with large scale changes in CRM1 conformation, consistent with the modulation of NES affinity being mediated by a local change in CRM1 near the NES binding site. These data also suggest that in the absence of RanGTP, the C-terminal helix lies across the CRM1 toroid in a position similar to that seen in the CRM1-Snurportin crystal structure. By creating local changes that stabilize the NES binding site in its closed conformation and thereby reducing the affinity of CRM1 for NESs, the C-terminal helix and HEAT 9 loop facilitate release of NES-containing cargo in the cytoplasm and also inhibit their return to the nucleus.

GPU.proton.DOCK: Genuine Protein Ultrafast proton equilibria consistent DOCKing

GPU.proton.DOCK (Genuine Protein Ultrafast proton equilibria consistent DOCKing) is a state of the art service for in silico prediction of protein–protein interactions via rigorous and ultrafast docking code. It is unique in providing stringent account of electrostatic interactions self-consistency and proton equilibria mutual effects of docking partners. GPU.proton.DOCK is the first server offering such a crucial supplement to protein docking algorithms—a step toward more reliable and high accuracy docking results. The code (especially the Fast Fourier Transform bottleneck and electrostatic fields computation) is parallelized to run on a GPU supercomputer. The high performance will be of use for large-scale structural bioinformatics and systems biology projects, thus bridging physics of the interactions with analysis of molecular networks. We propose workflows for exploring in silico charge mutagenesis effects. Special emphasis is given to the interface-intuitive and user-friendly. The input is comprised of the atomic coordinate files in PDB format. The advanced user is provided with a special input section for addition of non-polypeptide charges, extra ionogenic groups with intrinsic pKa values or fixed ions. The output is comprised of docked complexes in PDB format as well as interactive visualization in a molecular viewer. GPU.proton.DOCK server can be accessed at http://gpudock.orgchm.bas.bg/.

SDF2XYZ2SDF: how to exploit TINKER power in cheminformatics projects

An open-source tool capable of converting SD files (and virtually any other format through OpenBabel) into MMFF-typed XYZ coordinate files to be used with TINKER is described. SDF2XYZ2SDF allows including the power of TINKER molecular mechanics computations in automated cheminformatics workflows, such as conformational searches and virtual screening protocols.

Defining a Structural and Kinetic Rationale for Paralogous Copies of Phenylacetate-CoA Ligases from the Cystic Fibrosis Pathogen Burkholderia cenocepacia J2315

The phenylacetic acid (PAA) degradation pathway is the sole aerobic route for phenylacetic acid metabolism in bacteria and facilitates degradation of environmental pollutants such as styrene and ethylbenzene. The PAA pathway also is implicated in promoting Burkholderia cenocepacia infections in cystic fibrosis patients. Intriguingly, the first enzyme in the PAA pathway is present in two copies (paaK1 and paaK2), yet each subsequent enzyme is present in only a single copy. Furthermore, sequence divergence indicates that PaaK1 and PaaK2 form a unique subgroup within the adenylate-forming enzyme (AFE) superfamily. To establish a biochemical rationale for the existence of the PaaK paralogs in B. cenocepacia, we present high resolution x-ray crystal structures of a selenomethionine derivative of PaaK1 in complex with ATP and adenylated phenylacetate intermediate complexes of PaaK1 and PaaK2 in distinct conformations. Structural analysis reveals a novel N-terminal microdomain that may serve to recruit subsequent PAA enzymes, whereas a bifunctional role is proposed for the P-loop in stabilizing the C-terminal domain in conformation 2. The potential for different kinetic profiles was suggested by a structurally divergent extension of the aryl substrate pocket in PaaK1 relative to PaaK2. Functional characterization confirmed this prediction, with PaaK1 possessing a lower K(m) for phenylacetic acid and better able to accommodate 3' and 4' substitutions on the phenyl ring. Collectively, these results offer detailed insight into the reaction mechanism of a novel subgroup of the AFE superfamily and provide a clear biochemical rationale for the presence of paralogous copies of PaaK of B. cenocepacia.

Automated tertiary structure prediction with accurate local model quality assessment using the intfold‐ts method

The IntFOLD-TS method was developed according to the guiding principle that the model quality assessment (QA) would be the most critical stage for our template-based modeling pipeline. Thus, the IntFOLD-TS method firstly generates numerous alternate models, using in-house versions of several different sequence-structure alignment methods, which are then ranked in terms of global quality using our top performing QA method-ModFOLDclust2. In addition to the predicted global quality scores, the predictions of local errors are also provided in the resulting coordinate files, using scores that represent the predicted deviation of each residue in the model from the equivalent residue in the native structure. The IntFOLD-TS method was found to generate high quality 3D models for many of the CASP9 targets, whilst also providing highly accurate predictions of their per-residue errors. This important information may help to make the 3D models that are produced by the IntFOLD-TS method more useful for guiding future experimental work.

A tool for the qualitative comparison of membrane-embedded and detergent-solubilized membrane protein structures in projection

The calculation of projection structures (PSs) from Protein Data Bank (PDB)-coordinate files of membrane proteins is not well-established. Reports on such attempts exist but are rare. In addition, the different procedures are barely described and thus difficult if not impossible to reproduce. Here we present a simple, fast and well-documented method for the calculation and visualization of PSs from PDB-coordinate files of membrane proteins: the projection structure visualization (PSV)-method. The PSV-method was successfully validated using the PS of aquaporin-1 (AQP1) from 2D crystals and cryo-transmission electron microscopy, and the PDB-coordinate file of AQP1 determined from 3D crystals and X-ray crystallography. Besides AQP1, which is a relatively rigid protein, we also studied a flexible membrane transport protein, i.e. the L-arginine/agmatine antiporter AdiC. Comparison of PSs calculated from the existing PDB-coordinate files of substrate-free and L-arginine-bound AdiC indicated that conformational changes are detected in projection. Importantly, structural differences were found between the PSV-method calculated PSs of the detergent-solubilized AdiC proteins and the PS from cryo-TEM of membrane-embedded AdiC. These differences are particularly exciting since they may reflect a different conformation of AdiC induced by the lateral pressure in the lipid bilayer.

ContPro: A web tool for calculating amino acid contact distances in protein from 3D -structures at different distance threshold.

To investigate the functional sites on a protein and the prediction of binding sites (residues)in proteins, it is often required to identify the binding site residues at different distance threshold from protein three dimensional (3D)structures. For the study of a particular protein chain and its interaction with the ligand in complex form, researchers have to parse the output of different available tools or databases for finding binding-site residues. Here we have developed a tool for calculating amino acid contact distances in proteins at different distance threshold from the 3D-structure of the protein. For an input of protein 3D-structure, ContPro can quickly find all binding-site residues in the protein by calculating distances and also allows researchers to select the different distance threshold, protein chain and ligand of interest. Additionally, it can also parse the protein model (in case of multi model protein coordinate file)and the sequence of selected protein chain in Fasta format from the input 3D-structure. The developed tool will be useful for the identification and analysis of binding sites of proteins from 3D-structure at different distance thresholds. IT CAN BE ACCESSED AT: http://procarb.org/contpro/

CCRXP: exploring clusters of conserved residues in protein structures

Conserved residues forming tightly packed clusters have been shown to be energy hot spots in both protein–protein and protein–DNA complexes. A number of analyses on these clusters of conserved residues (CCRs) have been reported, all pointing to a crucial role that these clusters play in protein function, especially protein–protein and protein–DNA interactions. However, currently there is no publicly available tool to automatically detect such clusters. Here, we present a web server that takes a coordinate file in PDB format as input and automatically executes all the steps to identify CCRs in protein structures. In addition, it calculates the structural properties of each residue and of the CCRs. We also present statistics to show that CCRs, determined by these procedures, are significantly enriched in ‘hot spots’ in protein–protein and protein–RNA complexes, which supplements our more detailed similar results on protein–DNA complexes. We expect that CCRXP web server will be useful in studies of protein structures and their interactions and selecting mutagenesis targets. The web server can be accessed at http://ccrxp.netasa.org.

Dimeric Arrangement of the Parathyroid Hormone Receptor and a Structural Mechanism for Ligand-induced Dissociation

The parathyroid hormone receptor (PTH1R) is a class B G protein-coupled receptor that is activated by parathyroid hormone (PTH) and PTH-related protein (PTHrP). Little is known about the oligomeric state of the receptor and its regulation by hormone. The crystal structure of the ligand-free PTH1R extracellular domain (ECD) reveals an unexpected dimer in which the C-terminal segment of both ECD protomers forms an alpha-helix that mimics PTH/PTHrP by occupying the peptide binding groove of the opposing protomer. ECD-mediated oligomerization of intact PTH1R was confirmed in living cells by bioluminescence and fluorescence resonance energy transfer experiments. As predicted by the structure, PTH binding disrupted receptor oligomerization. A receptor rendered monomeric by mutations in the ECD retained wild-type PTH binding and cAMP signaling ability. Our results are consistent with the hypothesis that PTH1R forms constitutive dimers that are dissociated by ligand binding and that monomeric PTH1R is capable of activating G protein.

Laboratory Communications inActa Crystallographica Section F

The last year has seen a number of enhancements to our still very young journal, Acta Cryst. F. There was, for instance, the introduction of a tool for creating interactive figures (Einspahr & Guss, 2008b ▶) which can then be included in journal articles. The first Acta Cryst. F paper with such an enhanced figure was published by Mueller-Dieckmann et al. (2008 ▶). Also introduced was the ability to upload an mmCIF file (obtained upon depositing coordinate and structure-factor amplitude files with the PDB, for example) and create experimental details tables (or even a complete article) for publication (Einspahr & Weiss, 2008 ▶). We have also invited the NMR community to publish structures in Acta Cryst. F and devised an updated set of standards for this purpose (Einspahr & Guss, 2008a ▶). More information on these standards can be found at http://journals.iucr.org/services/nmr/nmrmeetingsummary.html. Another enhancement is the introduction of a new category of papers to Acta Cryst. F, Laboratory Communications. In parallel with the categories of Structural Communications and Crystallization Communications, this new category is for papers that describe special methods, equipment modifications, techniques for accomplishing certain tasks etc. that are related to crystallization or other fields of structural biology. The papers we want to attract contain observations made in the laboratory that are likely to be useful to our readers. Very often, these observations range from tips and tricks to novel procedures or adaptations that are hidden in the methods paragraphs of long papers, or may not be published at all. Our aim is to collect these for Acta Cryst. F and make them accessible to our readers. This issue contains the first example of a paper in the Laboratory Communications category by Tobias Beck and colleagues (Beck et al., 2009 ▶) and is titled How to get the magic triangle and the MAD triangle into your protein crystal. It describes the practical aspects of handling the compounds I3C (also known as the magic triangle) and B3C (the MAD triangle) for derivatizing protein crystals and phase determination. As an example, orthorhombic crystals of the serine protease trypsin were derivatized by co-crystallization with B3C. The paper conforms exactly to the spirit of the new category in the sense that it describes a successful experiment in sufficient detail so that it can be reproduced by others and, more importantly, that it can be applied to other problems. We hope that this new category will serve our readers well and we would like to invite you to write up your observations and submit them to Acta Cryst. F.

CHAMBER: Comprehensive support for CHARMM force fields within the AMBER software

AbstractThe similarity of the AMBER force field's energy functional form with that of the CHARMM force field, gives the potential for direct translation of common bonding and nonbonding terms, along with their parameters, present in CHARMM topology and parameter files, with the intent of evaluation within the AMBER software; specifically the SANDER and PMEMD dynamics engines. To this extent, we have created a tool, CHAMBER, which can take a CHARMM protein structure file (PSF), coordinate file (COR) and associated forcefield files, and convert these to an AMBER topology file (prmtop) and associated coordinate file (inpcrd). CHAMBER opens a conversion route which enables the simulation of CHARMM parameterized models using AMBER's PMEMD engine; thus providing improved serial efficiency as well as parallel efficiency over large numbers of CPUs. Significant effort has been expended in ensuring a true representation of the CHARMM force field in AMBER providing energies and forces that are the same to the limits of machine precision. This software will be released in the upcoming version 1.3 of the free AMBERTools suite.1 © 2009 Wiley Periodicals, Inc. Int J Quantum Chem, 2009

ADAN: a database for prediction of protein–protein interaction of modular domains mediated by linear motifs

Most of the structures and functions of proteome globular domains are yet unknown. We can use high-resolution structures from different modular domains in combination with automatic protein design algorithms to predict genome-wide potential interactions of a protein. ADAN database and related web tools are online resources for the predictive analysis of ligand-domain complexes. ADAN database is a collection of different modular protein domains (SH2, SH3, PDZ, WW, etc.). It contains 3505 entries with extensive structural and functional information available, manually integrated, curated and annotated with cross-references to other databases, biochemical and thermodynamical data, simplified coordinate files, sequence files and alignments. Prediadan, a subset of ADAN database, offers position-specific scoring matrices for protein-protein interactions, calculated by FoldX, and predictions of optimum ligands and putative binding partners. Users can also scan a query sequence against selected matrices, or improve a ligand-domain interaction. ADAN is accessible at http://adan-embl.ibmc.umh.es/ or http://adan.crg.es/.

RosettaAntibody: antibody variable region homology modeling server

The RosettaAntibody server (http://antibody.graylab.jhu.edu) predicts the structure of an antibody variable region given the amino-acid sequences of the respective light and heavy chains. In an initial stage, the server identifies and displays the most sequence homologous template structures for the light and heavy framework regions and each of the complementarity determining region (CDR) loops. Subsequently, the most homologous templates are assembled into a side-chain optimized crude model, and the server returns a picture and coordinate file. For users requesting a high-resolution model, the server executes the full RosettaAntibody protocol which additionally models the hyper-variable CDR H3 loop. The high-resolution protocol also relieves steric clashes by optimizing the CDR backbone torsion angles and by simultaneously perturbing the relative orientation of the light and heavy chains. RosettaAntibody generates 2000 independent structures, and the server returns pictures, coordinate files, and detailed scoring information for the 10 top-scoring models. The 10 models enable users to use rational judgment in choosing the best model or to use the set as an ensemble for further studies such as docking. The high-resolution models generated by RosettaAntibody have been used for the successful prediction of antibody–antigen complex structures.

PHEMTO: protein pH-dependent electric moment tools.

PHEMTO (protein pH-dependent electric moment tools) is released in response to the high demand in protein science community for evaluation of electrostatic characteristics in relations to molecular recognition. PHEMTO will serve protein scientists with new advanced features for analysis of protein molecular interactions: Electric/dipole moments, their pH-dependence and in silico charge mutagenesis effects on these properties as well as alternative algorithms for electric/dipole moment computation—Singular value decomposition of electrostatic potential (EP) to account for reaction field. The implementation is based on long-term experience—PHEI mean field electrostatics and PHEPS server for evaluation of global and local pH-dependent properties. However, PHEMTO is not just an update of our PHEPS server. Besides standard electrostatics, we offer new, advanced and useful features for analysis of protein molecular interactions. In addition our algorithms are very fast. Special emphasis is given to the interface—intuitive and user-friendly. The input is comprised of the atomic coordinate file in Protein Data Bank format. The advanced user is provided with a special input section for addition of non-polypeptide charges. The output covers actually full electrostatic characteristics but special emphasis is given to electric/dipole moments and their interactive visualization. PHEMTO server can be accessed at http://phemto.orgchm.bas.bg/.

Computational Docking of Biomolecular Complexes with AutoDock

INTRODUCTIONComputer prediction of the interaction between enzymes and small molecules has now advanced to the point that it allows accurate prediction of bound conformations and binding constants. For instance, the program AutoDock allows consistent computational docking of flexible ligands with about a dozen torsional degrees of freedom, and the empirical free-energy force field provides predicted energies that are accurate to within ~2 kcal/mol, or an ~30-fold difference in binding constants. Thus, these methods can easily separate compounds with micromolar and nanomolar binding constants from those with millimolar binding constants, and can often rank molecules with finer differences in affinity. Computational docking methods can be used to screen a variety of possible compounds, searching for new compounds with specific binding properties or testing a range of modifications of an existing compound. The approach has been successful in numerous cases, most notably, the discovery of human immunodeficiency virus (HIV) protease inhibitors. This protocol presents a detailed outline and advice for use of AutoDock and its graphical interface, AutoDock Tools, to analyze biomolecular complexes using computational docking. The first step is to prepare the coordinate files for the docking molecule and the target molecule. The second step is the calculation of the affinity grid for the target molecule. In the third step, the docking molecule is docked with the affinity grid, and, finally, the results are analyzed.