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1.  FILTREST3D: discrimination of structural models using restraints from experimental data 
Bioinformatics  2010;26(23):2986-2987.
Summary: Automatic methods for macromolecular structure prediction (fold recognition, de novo folding and docking programs) produce large sets of alternative models. These large model sets often include many native-like structures, which are often scored as false positives. Such native-like models can be more easily identified based on data from experimental analyses used as structural restraints (e.g. identification of nearby residues by cross-linking, chemical modification, site-directed mutagenesis, deuterium exchange coupled with mass spectrometry, etc.). We present a simple server for scoring and ranking of models according to their agreement with user-defined restraints.
Availability: FILTREST3D is freely available for users as a web server and standalone software at: http://filtrest3d.genesilico.pl/
Contact: iamb@genesilico.pl
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btq582
PMCID: PMC2982159  PMID: 20956242
2.  GeneSilico protein structure prediction meta-server 
Nucleic Acids Research  2003;31(13):3305-3307.
Rigorous assessments of protein structure prediction have demonstrated that fold recognition methods can identify remote similarities between proteins when standard sequence search methods fail. It has been shown that the accuracy of predictions is improved when refined multiple sequence alignments are used instead of single sequences and if different methods are combined to generate a consensus model. There are several meta-servers available that integrate protein structure predictions performed by various methods, but they do not allow for submission of user-defined multiple sequence alignments and they seldom offer confidentiality of the results. We developed a novel WWW gateway for protein structure prediction, which combines the useful features of other meta-servers available, but with much greater flexibility of the input. The user may submit an amino acid sequence or a multiple sequence alignment to a set of methods for primary, secondary and tertiary structure prediction. Fold-recognition results (target-template alignments) are converted into full-atom 3D models and the quality of these models is uniformly assessed. A consensus between different FR methods is also inferred. The results are conveniently presented on-line on a single web page over a secure, password-protected connection. The GeneSilico protein structure prediction meta-server is freely available for academic users at http://genesilico.pl/meta.
PMCID: PMC168964  PMID: 12824313
3.  MetalionRNA: computational predictor of metal-binding sites in RNA structures 
Bioinformatics  2011;28(2):198-205.
Motivation: Metal ions are essential for the folding of RNA molecules into stable tertiary structures and are often involved in the catalytic activity of ribozymes. However, the positions of metal ions in RNA 3D structures are difficult to determine experimentally. This motivated us to develop a computational predictor of metal ion sites for RNA structures.
Results: We developed a statistical potential for predicting positions of metal ions (magnesium, sodium and potassium), based on the analysis of binding sites in experimentally solved RNA structures. The MetalionRNA program is available as a web server that predicts metal ions for RNA structures submitted by the user.
Availability: The MetalionRNA web server is accessible at http://metalionrna.genesilico.pl/.
Contact: iamb@genesilico.pl
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btr636
PMCID: PMC3259437  PMID: 22110243
4.  Thomas Milton Ribers 1888–1962 
Journal of Bacteriology  1962;84(3):385-388.
Images
PMCID: PMC277887  PMID: 13988655
5.  Orange Riber Colony 
British Medical Journal  1908;2(2492):1043.
PMCID: PMC2437528  PMID: 20764069
6.  MetaDisorder: a meta-server for the prediction of intrinsic disorder in proteins 
BMC Bioinformatics  2012;13:111.
Background
Intrinsically unstructured proteins (IUPs) lack a well-defined three-dimensional structure. Some of them may assume a locally stable structure under specific conditions, e.g. upon interaction with another molecule, while others function in a permanently unstructured state. The discovery of IUPs challenged the traditional protein structure paradigm, which stated that a specific well-defined structure defines the function of the protein. As of December 2011, approximately 60 methods for computational prediction of protein disorder from sequence have been made publicly available. They are based on different approaches, such as utilizing evolutionary information, energy functions, and various statistical and machine learning methods.
Results
Given the diversity of existing intrinsic disorder prediction methods, we decided to test whether it is possible to combine them into a more accurate meta-prediction method. We developed a method based on arbitrarily chosen 13 disorder predictors, in which the final consensus was weighted by the accuracy of the methods. We have also developed a disorder predictor GSmetaDisorder3D that used no third-party disorder predictors, but alignments to known protein structures, reported by the protein fold-recognition methods, to infer the potentially structured and unstructured regions. Following the success of our disorder predictors in the CASP8 benchmark, we combined them into a meta-meta predictor called GSmetaDisorderMD, which was the top scoring method in the subsequent CASP9 benchmark.
Conclusions
A series of disorder predictors described in this article is available as a MetaDisorder web server at http://iimcb.genesilico.pl/metadisorder/. Results are presented both in an easily interpretable, interactive mode and in a simple text format suitable for machine processing.
doi:10.1186/1471-2105-13-111
PMCID: PMC3465245  PMID: 22624656
7.  RNAmap2D – calculation, visualization and analysis of contact and distance maps for RNA and protein-RNA complex structures 
BMC Bioinformatics  2012;13:333.
Background
The structures of biological macromolecules provide a framework for studying their biological functions. Three-dimensional structures of proteins, nucleic acids, or their complexes, are difficult to visualize in detail on flat surfaces, and algorithms for their spatial superposition and comparison are computationally costly. Molecular structures, however, can be represented as 2D maps of interactions between the individual residues, which are easier to visualize and compare, and which can be reconverted to 3D structures with reasonable precision. There are many visualization tools for maps of protein structures, but few for nucleic acids.
Results
We developed RNAmap2D, a platform-independent software tool for calculation, visualization and analysis of contact and distance maps for nucleic acid molecules and their complexes with proteins or ligands. The program addresses the problem of paucity of bioinformatics tools dedicated to analyzing RNA 2D maps, given the growing number of experimentally solved RNA structures in the Protein Data Bank (PDB) repository, as well as the growing number of tools for RNA 2D and 3D structure prediction. RNAmap2D allows for calculation and analysis of contacts and distances between various classes of atoms in nucleic acid, protein, and small ligand molecules. It also discriminates between different types of base pairing and stacking.
Conclusions
RNAmap2D is an easy to use method to visualize, analyze and compare structures of nucleic acid molecules and their complexes with other molecules, such as proteins or ligands and metal ions. Its special features make it a very useful tool for analysis of tertiary structures of RNAs. RNAmap2D for Windows/Linux/MacOSX is freely available for academic users at http://iimcb.genesilico.pl/rnamap2d.html
doi:10.1186/1471-2105-13-333
PMCID: PMC3556492  PMID: 23259794
Contact maps; Distance maps; RNA secondary structure; RNA base pairing; RNA stacking; Protein-RNA complex; Docking
8.  lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests 
Bioinformatics  2013;29(21):2722-2728.
Motivation: The assessment of protein structure prediction techniques requires objective criteria to measure the similarity between a computational model and the experimentally determined reference structure. Conventional similarity measures based on a global superposition of carbon α atoms are strongly influenced by domain motions and do not assess the accuracy of local atomic details in the model.
Results: The Local Distance Difference Test (lDDT) is a superposition-free score that evaluates local distance differences of all atoms in a model, including validation of stereochemical plausibility. The reference can be a single structure, or an ensemble of equivalent structures. We demonstrate that lDDT is well suited to assess local model quality, even in the presence of domain movements, while maintaining good correlation with global measures. These properties make lDDT a robust tool for the automated assessment of structure prediction servers without manual intervention.
Availability and implementation: Source code, binaries for Linux and MacOSX, and an interactive web server are available at http://swissmodel.expasy.org/lddt
Contact: torsten.schwede@unibas.ch
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btt473
PMCID: PMC3799472  PMID: 23986568
9.  QA-RecombineIt: a server for quality assessment and recombination of protein models 
Nucleic Acids Research  2013;41(Web Server issue):W389-W397.
QA-RecombineIt provides a web interface to assess the quality of protein 3D structure models and to improve the accuracy of models by merging fragments of multiple input models. QA-RecombineIt has been developed for protein modelers who are working on difficult problems, have a set of different homology models and/or de novo models (from methods such as I-TASSER or ROSETTA) and would like to obtain one consensus model that incorporates the best parts into one structure that is internally coherent. An advanced mode is also available, in which one can modify the operation of the fragment recombination algorithm by manually identifying individual fragments or entire models to recombine. Our method produces up to 100 models that are expected to be on the average more accurate than the starting models. Therefore, our server may be useful for crystallographic protein structure determination, where protein models are used for Molecular Replacement to solve the phase problem. To address the latter possibility, a special feature was added to the QA-RecombineIt server. The QA-RecombineIt server can be freely accessed at http://iimcb.genesilico.pl/qarecombineit/.
doi:10.1093/nar/gkt408
PMCID: PMC3692112  PMID: 23700309
10.  Transmembrane protein topology prediction using support vector machines 
BMC Bioinformatics  2009;10:159.
Background
Alpha-helical transmembrane (TM) proteins are involved in a wide range of important biological processes such as cell signaling, transport of membrane-impermeable molecules, cell-cell communication, cell recognition and cell adhesion. Many are also prime drug targets, and it has been estimated that more than half of all drugs currently on the market target membrane proteins. However, due to the experimental difficulties involved in obtaining high quality crystals, this class of protein is severely under-represented in structural databases. In the absence of structural data, sequence-based prediction methods allow TM protein topology to be investigated.
Results
We present a support vector machine-based (SVM) TM protein topology predictor that integrates both signal peptide and re-entrant helix prediction, benchmarked with full cross-validation on a novel data set of 131 sequences with known crystal structures. The method achieves topology prediction accuracy of 89%, while signal peptides and re-entrant helices are predicted with 93% and 44% accuracy respectively. An additional SVM trained to discriminate between globular and TM proteins detected zero false positives, with a low false negative rate of 0.4%. We present the results of applying these tools to a number of complete genomes. Source code, data sets and a web server are freely available from .
Conclusion
The high accuracy of TM topology prediction which includes detection of both signal peptides and re-entrant helices, combined with the ability to effectively discriminate between TM and globular proteins, make this method ideally suited to whole genome annotation of alpha-helical transmembrane proteins.
doi:10.1186/1471-2105-10-159
PMCID: PMC2700806  PMID: 19470175
11.  Sequence-based prediction of protein crystallization, purification and production propensity 
Bioinformatics  2011;27(13):i24-i33.
Motivation: X-ray crystallography-based protein structure determination, which accounts for majority of solved structures, is characterized by relatively low success rates. One solution is to build tools which support selection of targets that are more likely to crystallize. Several in silico methods that predict propensity of diffraction-quality crystallization from protein chains were developed. We show that the quality of their predictions drops when applied to more recent crystallization trails, which calls for new solutions. We propose a novel approach that alleviates drawbacks of the existing methods by using a recent dataset and improved protocol to annotate progress along the crystallization process, by predicting the success of the entire process and steps which result in the failed attempts, and by utilizing a compact and comprehensive set of sequence-derived inputs to generate accurate predictions.
Results: The proposed PPCpred (predictor of protein Production, Purification and Crystallization) predict propensity for production of diffraction-quality crystals, production of crystals, purification and production of the protein material. PPCpred utilizes comprehensive set of inputs based on energy and hydrophobicity indices, composition of certain amino acid types, predicted disorder, secondary structure and solvent accessibility, and content of certain buried and exposed residues. Our method significantly outperforms alignment-based predictions and several modern crystallization propensity predictors. Receiver operating characteristic (ROC) curves show that PPCpred is particularly useful for users who desire high true positive (TP) rates, i.e. low rate of mispredictions for solvable chains. Our model reveals several intuitive factors that influence the success of individual steps and the entire crystallization process, including the content of Cys, buried His and Ser, hydrophobic/hydrophilic segments and the number of predicted disordered segments.
Availability: http://biomine.ece.ualberta.ca/PPCpred/.
Contact: lkurgan@ece.ualberta.ca
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btr229
PMCID: PMC3117383  PMID: 21685077
12.  COLORADO3D, a web server for the visual analysis of protein structures 
Nucleic Acids Research  2004;32(Web Server issue):W586-W589.
COLORADO3D is a World Wide Web server for the visual presentation of three-dimensional (3D) protein structures. COLORADO3D indicates the presence of potential errors (detected by ANOLEA, PROSAII, PROVE or VERIFY3D), identifies buried residues and depicts sequence conservations. As input, the server takes a file of Protein Data Bank (PDB) coordinates and, optionally, a multiple sequence alignment. As output, the server returns a PDB-formatted file, replacing the B-factor column with values of the chosen parameter (structure quality, residue burial or conservation). Thus, the coordinates of the analyzed protein ‘colored’ by COLORADO3D can be conveniently displayed with structure viewers such as RASMOL in order to visualize the 3D clusters of regions with common features, which may not necessarily be adjacent to each other at the amino acid sequence level. In particular, COLORADO3D may serve as a tool to judge a structure's quality at various stages of the modeling and refinement (during both experimental structure determination and homology modeling). The GeneSilico group used COLORADO3D in the fifth Critical Assessment of Techniques for Protein Structure Prediction (CASP5) to successfully identify well-folded parts of preliminary homology models and to guide the refinement of misthreaded protein sequences. COLORADO3D is freely available for academic use at http://asia.genesilico.pl/colorado3d/.
doi:10.1093/nar/gkh440
PMCID: PMC441578  PMID: 15215456
13.  Rigidity analysis of protein biological assemblies and periodic crystal structures 
BMC Bioinformatics  2013;14(Suppl 18):S2.
Background
We initiate in silico rigidity-theoretical studies of biological assemblies and small crystals for protein structures. The goal is to determine if, and how, the interactions among neighboring cells and subchains affect the flexibility of a molecule in its crystallized state. We use experimental X-ray crystallography data from the Protein Data Bank (PDB). The analysis relies on an effcient graph-based algorithm. Computational experiments were performed using new protein rigidity analysis tools available in the new release of our KINARI-Web server http://kinari.cs.umass.edu.
Results
We provide two types of results: on biological assemblies and on crystals. We found that when only isolated subchains are considered, structural and functional information may be missed. Indeed, the rigidity of biological assemblies is sometimes dependent on the count and placement of hydrogen bonds and other interactions among the individual subchains of the biological unit. Similarly, the rigidity of small crystals may be affected by the interactions between atoms belonging to different unit cells.
We have analyzed a dataset of approximately 300 proteins, from which we generated 982 crystals (some of which are biological assemblies). We identified two types of behaviors. (a) Some crystals and/or biological assemblies will aggregate into rigid bodies that span multiple unit cells/asymmetric units. Some of them create substantially larger rigid cluster in the crystal/biological assembly form, while in other cases, the aggregation has a smaller effect just at the interface between the units. (b) In other cases, the rigidity properties of the asymmetric units are retained, because the rigid bodies did not combine.
We also identified two interesting cases where rigidity analysis may be correlated with the functional behavior of the protein. This type of information, identified here for the first time, depends critically on the ability to create crystals and biological assemblies, and would not have been observed only from the asymmetric unit.
For the Ribonuclease A protein (PDB file 5RSA), which is functionally active in the crystallized form, we found that the individual protein and its crystal form retain the flexibility parameters between the two states. In contrast, a derivative of Ribonuclease A (PDB file 9RSA), has no functional activity, and the protein in both the asymmetric and crystalline forms, is very rigid.
For the vaccinia virus D13 scaffolding protein (PDB file 3SAQ), which has two biological assemblies, we observed a striking asymmetry in the rigidity cluster decomposition of one of them, which seems implausible, given its symmetry. Upon careful investigation, we tracked the cause to a placement decision by the Reduce software concerning the hydrogen atoms, thus affecting the distribution of certain hydrogen bonds. The surprising result is that the presence or lack of a very few, but critical, hydrogen bonds, can drastically affect the rigid cluster decomposition of the biological assembly.
Conclusion
The rigidity analysis of a single asymmetric unit may not accurately reflect the protein's behavior in the tightly packed crystal environment. Using our KINARI software, we demonstrated that additional functional and rigidity information can be gained by analyzing a protein's biological assembly and/or crystal structure. However, performing a larger scale study would be computationally expensive (due to the size of the molecules involved). Overcoming this limitation will require novel mathematical and computational extensions to our software.
doi:10.1186/1471-2105-14-S18-S2
PMCID: PMC3817814  PMID: 24564201
14.  FFAS server: novel features and applications 
Nucleic Acids Research  2011;39(Web Server issue):W38-W44.
The Fold and Function Assignment System (FFAS) server [Jaroszewski et al. (2005) FFAS03: a server for profile–profile sequence alignments. Nucleic Acids Research, 33, W284–W288] implements the algorithm for protein profile–profile alignment introduced originally in [Rychlewski et al. (2000) Comparison of sequence profiles. Strategies for structural predictions using sequence information. Protein Science: a Publication of the Protein Society, 9, 232–241]. Here, we present updates, changes and novel functionality added to the server since 2005 and discuss its new applications. The sequence database used to calculate sequence profiles was enriched by adding sets of publicly available metagenomic sequences. The profile of a user’s protein can now be compared with ∼20 additional profile databases, including several complete proteomes, human proteins involved in genetic diseases and a database of microbial virulence factors. A newly developed interface uses a system of tabs, allowing the user to navigate multiple results pages, and also includes novel functionality, such as a dotplot graph viewer, modeling tools, an improved 3D alignment viewer and links to the database of structural similarities. The FFAS server was also optimized for speed: running times were reduced by an order of magnitude. The FFAS server, http://ffas.godziklab.org, has no log-in requirement, albeit there is an option to register and store results in individual, password-protected directories. Source code and Linux executables for the FFAS program are available for download from the FFAS server.
doi:10.1093/nar/gkr441
PMCID: PMC3125803  PMID: 21715387
15.  DARS-RNP and QUASI-RNP: New statistical potentials for protein-RNA docking 
BMC Bioinformatics  2011;12:348.
Background
Protein-RNA interactions play fundamental roles in many biological processes. Understanding the molecular mechanism of protein-RNA recognition and formation of protein-RNA complexes is a major challenge in structural biology. Unfortunately, the experimental determination of protein-RNA complexes is tedious and difficult, both by X-ray crystallography and NMR. For many interacting proteins and RNAs the individual structures are available, enabling computational prediction of complex structures by computational docking. However, methods for protein-RNA docking remain scarce, in particular in comparison to the numerous methods for protein-protein docking.
Results
We developed two medium-resolution, knowledge-based potentials for scoring protein-RNA models obtained by docking: the quasi-chemical potential (QUASI-RNP) and the Decoys As the Reference State potential (DARS-RNP). Both potentials use a coarse-grained representation for both RNA and protein molecules and are capable of dealing with RNA structures with posttranscriptionally modified residues. We compared the discriminative power of DARS-RNP and QUASI-RNP for selecting rigid-body docking poses with the potentials previously developed by the Varani and Fernandez groups.
Conclusions
In both bound and unbound docking tests, DARS-RNP showed the highest ability to identify native-like structures. Python implementations of DARS-RNP and QUASI-RNP are freely available for download at http://iimcb.genesilico.pl/RNP/
doi:10.1186/1471-2105-12-348
PMCID: PMC3179970  PMID: 21851628
RNA; protein; RNP; macromolecular docking; complex modeling; structural bioinformatics
16.  A partition function algorithm for interacting nucleic acid strands 
Bioinformatics  2009;25(12):i365-i373.
Recent interests, such as RNA interference and antisense RNA regulation, strongly motivate the problem of predicting whether two nucleic acid strands interact.
Motivation: Regulatory non-coding RNAs (ncRNAs) such as microRNAs play an important role in gene regulation. Studies on both prokaryotic and eukaryotic cells show that such ncRNAs usually bind to their target mRNA to regulate the translation of corresponding genes. The specificity of these interactions depends on the stability of intermolecular and intramolecular base pairing. While methods like deep sequencing allow to discover an ever increasing set of ncRNAs, there are no high-throughput methods available to detect their associated targets. Hence, there is an increasing need for precise computational target prediction. In order to predict base-pairing probability of any two bases in interacting nucleic acids, it is necessary to compute the interaction partition function over the whole ensemble. The partition function is a scalar value from which various thermodynamic quantities can be derived. For example, the equilibrium concentration of each complex nucleic acid species and also the melting temperature of interacting nucleic acids can be calculated based on the partition function of the complex.
Results: We present a model for analyzing the thermodynamics of two interacting nucleic acid strands considering the most general type of interactions studied in the literature. We also present a corresponding dynamic programming algorithm that computes the partition function over (almost) all physically possible joint secondary structures formed by two interacting nucleic acids in O(n6) time. We verify the predictive power of our algorithm by computing (i) the melting temperature for interacting RNA pairs studied in the literature and (ii) the equilibrium concentration for several variants of the OxyS–fhlA complex. In both experiments, our algorithm shows high accuracy and outperforms competitors.
Availability: Software and web server is available at http://compbio.cs.sfu.ca/taverna/pirna/
Contact: cenk@cs.sfu.ca; backofen@informatik.uni-freiburg.de
Supplementary information: Supplementary data are avaliable at Bioinformatics online.
doi:10.1093/bioinformatics/btp212
PMCID: PMC2687966  PMID: 19478011
17.  A suite of software for processing MicroED data of extremely small protein crystals 
Journal of Applied Crystallography  2014;47(Pt 3):1140-1145.
Electron diffraction of extremely small three-dimensional crystals (MicroED) allows for structure determination from crystals orders of magnitude smaller than those used for X-ray crystallography. The MicroED suite was developed to accomplish the tasks of unit-cell determination, indexing, background subtraction, intensity measurement and merging, resulting in data that can be carried forward to molecular replacement and structure determination.
Electron diffraction of extremely small three-dimensional crystals (MicroED) allows for structure determination from crystals orders of magnitude smaller than those used for X-ray crystallography. MicroED patterns, which are collected in a transmission electron microscope, were initially not amenable to indexing and intensity extraction by standard software, which necessitated the development of a suite of programs for data processing. The MicroED suite was developed to accomplish the tasks of unit-cell determination, indexing, background subtraction, intensity measurement and merging, resulting in data that can be carried forward to molecular replacement and structure determination. This ad hoc solution has been modified for more general use to provide a means for processing MicroED data until the technique can be fully implemented into existing crystallographic software packages. The suite is written in Python and the source code is available under a GNU General Public License.
doi:10.1107/S1600576714008073
PMCID: PMC4038802  PMID: 24904248
electron diffraction; structure determination; computer programs
18.  Statistical analysis of interface similarity in crystals of homologous proteins 
Journal of molecular biology  2008;381(2):487-507.
Many proteins function as homooligomers and are regulated via their oligomeric state. For some proteins, the stoichiometry of homooligomeric states under various conditions has been studied using gel filtration or analytical ultracentrifugation experiments. The interfaces involved in these assemblies may be identified using crosslinking and mass spectrometry, solution-state NMR, and other experiments. But for most proteins, the actual interfaces that are involved in oligomerization are inferred from X-ray crystallographic structures using assumptions about interface surface areas and physical properties. Examination of interfaces across different PDB entries in a protein family reveals several important features. First, similarity of space group, asymmetric unit size, and cell dimensions and angles (within 1%) does not guarantee that two crystals are actually the same crystal form, that is containing similar relative orientations and interactions within the crystal. Conversely, two crystals in different space groups may be quite similar in terms of all of the interfaces within each crystal. Second, NMR structures and an existing benchmark of PDB crystallographic entries consisting of 126 dimers and larger structures and 132 monomers was used to determine whether the existence or lack of existence of common interfaces across multiple crystal forms can be used to predict whether a protein is an oligomer or not. Monomeric proteins tend to have common interfaces across only a minority of crystal forms, while higher order structures exhibit common interfaces across a majority of available crystal forms. The data can be used to estimate the probability that an interface is biological if two or more crystal forms are available. Finally, the PISA database available from the EBI is more consistent in identifying interfaces observed in many crystal forms than is the PDB or EBI’s Protein Quaternary Server (PQS). The PDB in particular is missing highly likely biological interfaces in its biological unit files for about 10% of PDB entries.
doi:10.1016/j.jmb.2008.06.002
PMCID: PMC2573399  PMID: 18599072
19.  2dx_merge – Data management and merging for 2D crystal images 
Journal of structural biology  2007;160(3):375-384.
Summary
Electron crystallography of membrane proteins determines the structure of membrane-reconstituted and two-dimensionally (2D) crystallized membrane proteins by low-dose imaging with the transmission electron microscope, and computer image processing. We have previously presented the software system 2dx, for user-friendly image processing of 2D crystal images. Its central component 2dx_image is based on the MRC program suite, and allows the optionally fully automatic processing of one 2D crystal image. We present here the program 2dx_merge, which assists the user in the management of a 2D crystal image-processing project, and facilitates the merging of the data from multiple images. The merged dataset can be used as a reference to re-process all images, which usually improves the resolution of the final reconstruction. Image processing and merging can be applied iteratively, until convergence is reached. 2dx is available under the GNU General Public License at http://2dx.org.
doi:10.1016/j.jsb.2007.09.011
PMCID: PMC2157552  PMID: 17967545
2dx software; Electron crystallography; 2D crystals; membrane protein; structure determination; computer image processing; MRC software; Merging
20.  MODOMICS: a database of RNA modification pathways. 2008 update 
Nucleic Acids Research  2008;37(Database issue):D118-D121.
MODOMICS, a database devoted to the systems biology of RNA modification, has been subjected to substantial improvements. It provides comprehensive information on the chemical structure of modified nucleosides, pathways of their biosynthesis, sequences of RNAs containing these modifications and RNA-modifying enzymes. MODOMICS also provides cross-references to other databases and to literature. In addition to the previously available manually curated tRNA sequences from a few model organisms, we have now included additional tRNAs and rRNAs, and all RNAs with 3D structures in the Nucleic Acid Database, in which modified nucleosides are present. In total, 3460 modified bases in RNA sequences of different organisms have been annotated. New RNA-modifying enzymes have been also added. The current collection of enzymes includes mainly proteins for the model organisms Escherichia coli and Saccharomyces cerevisiae, and is currently being expanded to include proteins from other organisms, in particular Archaea and Homo sapiens. For enzymes with known structures, links are provided to the corresponding Protein Data Bank entries, while for many others homology models have been created. Many new options for database searching and querying have been included. MODOMICS can be accessed at http://genesilico.pl/modomics.
doi:10.1093/nar/gkn710
PMCID: PMC2686465  PMID: 18854352
21.  R3D Align: global pairwise alignment of RNA 3D structures using local superpositions 
Bioinformatics  2010;26(21):2689-2697.
Motivation: Comparing 3D structures of homologous RNA molecules yields information about sequence and structural variability. To compare large RNA 3D structures, accurate automatic comparison tools are needed. In this article, we introduce a new algorithm and web server to align large homologous RNA structures nucleotide by nucleotide using local superpositions that accommodate the flexibility of RNA molecules. Local alignments are merged to form a global alignment by employing a maximum clique algorithm on a specially defined graph that we call the ‘local alignment’ graph.
Results: The algorithm is implemented in a program suite and web server called ‘R3D Align’. The R3D Align alignment of homologous 3D structures of 5S, 16S and 23S rRNA was compared to a high-quality hand alignment. A full comparison of the 16S alignment with the other state-of-the-art methods is also provided. The R3D Align program suite includes new diagnostic tools for the structural evaluation of RNA alignments. The R3D Align alignments were compared to those produced by other programs and were found to be the most accurate, in comparison with a high quality hand-crafted alignment and in conjunction with a series of other diagnostics presented. The number of aligned base pairs as well as measures of geometric similarity are used to evaluate the accuracy of the alignments.
Availability: R3D Align is freely available through a web server http://rna.bgsu.edu/R3DAlign. The MATLAB source code of the program suite is also freely available for download at that location.
Supplementary information: Supplementary data are available at Bioinformatics online.
Contact: r-rahrig@onu.edu
doi:10.1093/bioinformatics/btq506
PMCID: PMC3465099  PMID: 20929913
22.  Gaia: automated quality assessment of protein structure models 
Bioinformatics  2011;27(16):2209-2215.
Motivation: Increasing use of structural modeling for understanding structure–function relationships in proteins has led to the need to ensure that the protein models being used are of acceptable quality. Quality of a given protein structure can be assessed by comparing various intrinsic structural properties of the protein to those observed in high-resolution protein structures.
Results: In this study, we present tools to compare a given structure to high-resolution crystal structures. We assess packing by calculating the total void volume, the percentage of unsatisfied hydrogen bonds, the number of steric clashes and the scaling of the accessible surface area. We assess covalent geometry by determining bond lengths, angles, dihedrals and rotamers. The statistical parameters for the above measures, obtained from high-resolution crystal structures enable us to provide a quality-score that points to specific areas where a given protein structural model needs improvement.
Availability and Implementation: We provide these tools that appraise protein structures in the form of a web server Gaia (http://chiron.dokhlab.org). Gaia evaluates the packing and covalent geometry of a given protein structure and provides quantitative comparison of the given structure to high-resolution crystal structures.
Contact: dokh@unc.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btr374
PMCID: PMC3150034  PMID: 21700672
23.  PatMaN: rapid alignment of short sequences to large databases 
Bioinformatics  2008;24(13):1530-1531.
Summary: We present a tool suited for searching for many short nucleotide sequences in large databases, allowing for a predefined number of gaps and mismatches. The commandline-driven program implements a non-deterministic automata matching algorithm on a keyword tree of the search strings. Both queries with and without ambiguity codes can be searched. Search time is short for perfect matches, and retrieval time rises exponentially with the number of edits allowed.
Availability: The C++ source code for PatMaN is distributed under the GNU General Public License and has been tested on the GNU/Linux operating system. It is available from http://bioinf.eva.mpg.de/patman.
Contact: pruefer@eva.mpg.de
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btn223
PMCID: PMC2718670  PMID: 18467344
24.  Empowering radiologie education on the internet: A new virtual website technology for hosting interactive educational content on the world wide web 
Journal of Digital Imaging  2001;14(Suppl 1):113-116.
Objective: We describe a virtual web site hosting technology that enables educators in radiology to emblazon and make available for delivery on the world wide web their own interactive educational content, free from dependencies on in-house re-sources and policies.Materials/Methods: Ibis suite of technologies includes a graphically oriented software application, designed for the computer novice, to facilitate the input, storage, and management of domain expertise within a database system. The database stores this expertise as choreographed and interlinked multimedia entities including text, imagery, interactive questions, and audio. Case-based presentations or thematic lectures can be authored locally, previewed locally within a web browser, then uploaded at will as packaged knowledge objects to an educator’s (or department’s) personal web site housed within a virtual server architecture. This architecture can host an unlimited number of unique educational web sites for individuals or departments in need of such service. Each virtual site’s content is stored within that site’s protected back-end database connected to Internet Information Server (Microsoft Corp, Redmond WA) using a suite of Active Server Page (ASP) modules that incorporate Microsoft’s Active Data Objects (ADO) technology. Each person’s or department’s electronic teaching material appears as an independent web site with different levels of access—controlled by a username-password strategy—for teachers and students. There is essentially no static hypertext markup language (HTML). Rather, all pages displayed for a given site are rendered dynamically from case-based or thematic content that is fetched from that virtual site’s database. The dynamically rendered HTML is displayed within a web browser in a Socratic fashion that can assess the recipient’s current fund of knowledge while providing instantaneous user-specific feedback. Each site is emblazoned with the logo and identification of the participating institution. Individuals with teacher-level access can use a web browser to upload new content as well as manage content already stored on their virtual site. Each virtual site stores, collates, and scores participants’ responses to the interactive questions posed on line.Conclusion: This virtual web site strategy empowers the educator with an end-to-end solution for creating interactive educational content and hosting that content within theeducator’s personalized and protected educational site on the world wide web, thus providing a valuable outlet that can magnify the impact of his or her talents and contributions.
doi:10.1007/BF03190311
PMCID: PMC3452700  PMID: 11442067
25.  Three-dimensional electron crystallography of protein microcrystals 
eLife  2013;2:e01345.
We demonstrate that it is feasible to determine high-resolution protein structures by electron crystallography of three-dimensional crystals in an electron cryo-microscope (CryoEM). Lysozyme microcrystals were frozen on an electron microscopy grid, and electron diffraction data collected to 1.7 Å resolution. We developed a data collection protocol to collect a full-tilt series in electron diffraction to atomic resolution. A single tilt series contains up to 90 individual diffraction patterns collected from a single crystal with tilt angle increment of 0.1–1° and a total accumulated electron dose less than 10 electrons per angstrom squared. We indexed the data from three crystals and used them for structure determination of lysozyme by molecular replacement followed by crystallographic refinement to 2.9 Å resolution. This proof of principle paves the way for the implementation of a new technique, which we name ‘MicroED’, that may have wide applicability in structural biology.
DOI: http://dx.doi.org/10.7554/eLife.01345.001
eLife digest
X-ray crystallography has been used to work out the atomic structure of a large number of proteins. In a typical X-ray crystallography experiment, a beam of X-rays is directed at a protein crystal, which scatters some of the X-ray photons to produce a diffraction pattern. The crystal is then rotated through a small angle and another diffraction pattern is recorded. Finally, after this process has been repeated enough times, it is possible to work backwards from the diffraction patterns to figure out the structure of the protein.
The crystals used for X-ray crystallography must be large to withstand the damage caused by repeated exposure to the X-ray beam. However, some proteins do not form crystals at all, and others only form small crystals. It is possible to overcome this problem by using extremely short pulses of X-rays, but this requires a very large number of small crystals and ultrashort X-ray pulses are only available at a handful of research centers around the world. There is, therefore, a need for other approaches that can determine the structure of proteins that only form small crystals.
Electron crystallography is similar to X-ray crystallography in that a protein crystal scatters a beam to produce a diffraction pattern. However, the interactions between the electrons in the beam and the crystal are much stronger than those between the X-ray photons and the crystal. This means that meaningful amounts of data can be collected from much smaller crystals. However, it is normally only possible to collect one diffraction pattern from each crystal because of beam induced damage. Researchers have developed methods to merge the diffraction patterns produced by hundreds of small crystals, but to date these techniques have only worked with very thin two-dimensional crystals that contain only one layer of the protein of interest.
Now Shi et al. report a new approach to electron crystallography that works with very small three-dimensional crystals. Called MicroED, this technique involves placing the crystal in a transmission electron cryo-microscope, which is a fairly standard piece of equipment in many laboratories. The normal ‘low-dose’ electron beam in one of these microscopes would normally damage the crystal after a single diffraction pattern had been collected. However, Shi et al. realized that it was possible to obtain diffraction patterns without severely damaging the crystal if they dramatically reduced the normal low-dose electron beam. By reducing the electron dose by a factor of 200, it was possible to collect up to 90 diffraction patterns from the same, very small, three-dimensional crystal, and then—similar to what happens in X-ray crystallography—work backwards to figure out the structure of the protein. Shi et al. demonstrated the feasibility of the MicroED approach by using it to determine the structure of lysozyme, which is widely used as a test protein in crystallography, with a resolution of 2.9 Å. This proof-of principle study paves the way for crystallographers to study protein that cannot be studied with existing techniques.
DOI: http://dx.doi.org/10.7554/eLife.01345.002
doi:10.7554/eLife.01345
PMCID: PMC3831942  PMID: 24252878
electron crystallography; electron diffraction; electron cryomicroscopy (cryo-EM); microED; protein structure; microcrystals; None

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