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1.  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
2.  The utility of comparative models and the local model quality for protein crystal structure determination by Molecular Replacement 
BMC Bioinformatics  2012;13:289.
Background
Computational models of protein structures were proved to be useful as search models in Molecular Replacement (MR), a common method to solve the phase problem faced by macromolecular crystallography. The success of MR depends on the accuracy of a search model. Unfortunately, this parameter remains unknown until the final structure of the target protein is determined. During the last few years, several Model Quality Assessment Programs (MQAPs) that predict the local accuracy of theoretical models have been developed. In this article, we analyze whether the application of MQAPs improves the utility of theoretical models in MR.
Results
For our dataset of 615 search models, the real local accuracy of a model increases the MR success ratio by 101% compared to corresponding polyalanine templates. On the contrary, when local model quality is not utilized in MR, the computational models solved only 4.5% more MR searches than polyalanine templates. For the same dataset of the 615 models, a workflow combining MR with predicted local accuracy of a model found 45% more correct solution than polyalanine templates. To predict such accuracy MetaMQAPclust, a “clustering MQAP” was used.
Conclusions
Using comparative models only marginally increases the MR success ratio in comparison to polyalanine structures of templates. However, the situation changes dramatically once comparative models are used together with their predicted local accuracy. A new functionality was added to the GeneSilico Fold Prediction Metaserver in order to build models that are more useful for MR searches. Additionally, we have developed a simple method, AmIgoMR (Am I good for MR?), to predict if an MR search with a template-based model for a given template is likely to find the correct solution.
doi:10.1186/1471-2105-13-289
PMCID: PMC3534383  PMID: 23126528
Molecular replacement; MR; MQAP; Model quality assessment; Protein structure prediction
3.  Molecular evolution of dihydrouridine synthases 
BMC Bioinformatics  2012;13:153.
Background
Dihydrouridine (D) is a modified base found in conserved positions in the D-loop of tRNA in Bacteria, Eukaryota, and some Archaea. Despite the abundant occurrence of D, little is known about its biochemical roles in mediating tRNA function. It is assumed that D may destabilize the structure of tRNA and thus enhance its conformational flexibility. D is generated post-transcriptionally by the reduction of the 5,6-double bond of a uridine residue in RNA transcripts. The reaction is carried out by dihydrouridine synthases (DUS). DUS constitute a conserved family of enzymes encoded by the orthologous gene family COG0042. In protein sequence databases, members of COG0042 are typically annotated as “predicted TIM-barrel enzymes, possibly dehydrogenases, nifR3 family”.
Results
To elucidate sequence-structure-function relationships in the DUS family, a comprehensive bioinformatic analysis was carried out. We performed extensive database searches to identify all members of the currently known DUS family, followed by clustering analysis to subdivide it into subfamilies of closely related sequences. We analyzed phylogenetic distributions of all members of the DUS family and inferred the evolutionary tree, which suggested a scenario for the evolutionary origin of dihydrouridine-forming enzymes. For a human representative of the DUS family, the hDus2 protein suggested as a potential drug target in cancer, we generated a homology model. While this article was under review, a crystal structure of a DUS representative has been published, giving us an opportunity to validate the model.
Conclusions
We compared sequences and phylogenetic distributions of all members of the DUS family and inferred the phylogenetic tree, which provides a framework to study the functional differences among these proteins and suggests a scenario for the evolutionary origin of dihydrouridine formation. Our evolutionary and structural classification of the DUS family provides a background to study functional differences among these proteins that will guide experimental analyses.
doi:10.1186/1471-2105-13-153
PMCID: PMC3674756  PMID: 22741570
Dihydrouridine synthases; Protein structure prediction; Fold recognition; Remote homology; RNA modification; Molecular evolution; Enzymes acting on RNA
4.  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
5.  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
6.  MetaMQAP: A meta-server for the quality assessment of protein models 
BMC Bioinformatics  2008;9:403.
Background
Computational models of protein structure are usually inaccurate and exhibit significant deviations from the true structure. The utility of models depends on the degree of these deviations. A number of predictive methods have been developed to discriminate between the globally incorrect and approximately correct models. However, only a few methods predict correctness of different parts of computational models. Several Model Quality Assessment Programs (MQAPs) have been developed to detect local inaccuracies in unrefined crystallographic models, but it is not known if they are useful for computational models, which usually exhibit different and much more severe errors.
Results
The ability to identify local errors in models was tested for eight MQAPs: VERIFY3D, PROSA, BALA, ANOLEA, PROVE, TUNE, REFINER, PROQRES on 8251 models from the CASP-5 and CASP-6 experiments, by calculating the Spearman's rank correlation coefficients between per-residue scores of these methods and local deviations between C-alpha atoms in the models vs. experimental structures. As a reference, we calculated the value of correlation between the local deviations and trivial features that can be calculated for each residue directly from the models, i.e. solvent accessibility, depth in the structure, and the number of local and non-local neighbours. We found that absolute correlations of scores returned by the MQAPs and local deviations were poor for all methods. In addition, scores of PROQRES and several other MQAPs strongly correlate with 'trivial' features. Therefore, we developed MetaMQAP, a meta-predictor based on a multivariate regression model, which uses scores of the above-mentioned methods, but in which trivial parameters are controlled. MetaMQAP predicts the absolute deviation (in Ångströms) of individual C-alpha atoms between the model and the unknown true structure as well as global deviations (expressed as root mean square deviation and GDT_TS scores). Local model accuracy predicted by MetaMQAP shows an impressive correlation coefficient of 0.7 with true deviations from native structures, a significant improvement over all constituent primary MQAP scores. The global MetaMQAP score is correlated with model GDT_TS on the level of 0.89.
Conclusion
Finally, we compared our method with the MQAPs that scored best in the 7th edition of CASP, using CASP7 server models (not included in the MetaMQAP training set) as the test data. In our benchmark, MetaMQAP is outperformed only by PCONS6 and method QA_556 – methods that require comparison of multiple alternative models and score each of them depending on its similarity to other models. MetaMQAP is however the best among methods capable of evaluating just single models.
We implemented the MetaMQAP as a web server available for free use by all academic users at the URL
doi:10.1186/1471-2105-9-403
PMCID: PMC2573893  PMID: 18823532
7.  Structural and evolutionary bioinformatics of the SPOUT superfamily of methyltransferases 
BMC Bioinformatics  2007;8:73.
Background
SPOUT methyltransferases (MTases) are a large class of S-adenosyl-L-methionine-dependent enzymes that exhibit an unusual alpha/beta fold with a very deep topological knot. In 2001, when no crystal structures were available for any of these proteins, Anantharaman, Koonin, and Aravind identified homology between SpoU and TrmD MTases and defined the SPOUT superfamily. Since then, multiple crystal structures of knotted MTases have been solved and numerous new homologous sequences appeared in the databases. However, no comprehensive comparative analysis of these proteins has been carried out to classify them based on structural and evolutionary criteria and to guide functional predictions.
Results
We carried out extensive searches of databases of protein structures and sequences to collect all members of previously identified SPOUT MTases, and to identify previously unknown homologs. Based on sequence clustering, characterization of domain architecture, structure predictions and sequence/structure comparisons, we re-defined families within the SPOUT superfamily and predicted putative active sites and biochemical functions for the so far uncharacterized members. We have also delineated the common core of SPOUT MTases and inferred a multiple sequence alignment for the conserved knot region, from which we calculated the phylogenetic tree of the superfamily. We have also studied phylogenetic distribution of different families, and used this information to infer the evolutionary history of the SPOUT superfamily.
Conclusion
We present the first phylogenetic tree of the SPOUT superfamily since it was defined, together with a new scheme for its classification, and discussion about conservation of sequence and structure in different families, and their functional implications. We identified four protein families as new members of the SPOUT superfamily. Three of these families are functionally uncharacterized (COG1772, COG1901, and COG4080), and one (COG1756 represented by Nep1p) has been already implicated in RNA metabolism, but its biochemical function has been unknown. Based on the inference of orthologous and paralogous relationships between all SPOUT families we propose that the Last Universal Common Ancestor (LUCA) of all extant organisms contained at least three SPOUT members, ancestors of contemporary RNA MTases that carry out m1G, m3U, and 2'O-ribose methylation, respectively. In this work we also speculate on the origin of the knot and propose possible 'unknotted' ancestors. The results of our analysis provide a comprehensive 'roadmap' for experimental characterization of SPOUT MTases and interpretation of functional studies in the light of sequence-structure relationships.
doi:10.1186/1471-2105-8-73
PMCID: PMC1829167  PMID: 17338813
8.  The PD-(D/E)XK superfamily revisited: identification of new members among proteins involved in DNA metabolism and functional predictions for domains of (hitherto) unknown function 
BMC Bioinformatics  2005;6:172.
Background
The PD-(D/E)XK nuclease superfamily, initially identified in type II restriction endonucleases and later in many enzymes involved in DNA recombination and repair, is one of the most challenging targets for protein sequence analysis and structure prediction. Typically, the sequence similarity between these proteins is so low, that most of the relationships between known members of the PD-(D/E)XK superfamily were identified only after the corresponding structures were determined experimentally. Thus, it is tempting to speculate that among the uncharacterized protein families, there are potential nucleases that remain to be discovered, but their identification requires more sensitive tools than traditional PSI-BLAST searches.
Results
The low degree of amino acid conservation hampers the possibility of identification of new members of the PD-(D/E)XK superfamily based solely on sequence comparisons to known members. Therefore, we used a recently developed method HHsearch for sensitive detection of remote similarities between protein families represented as profile Hidden Markov Models enhanced by secondary structure. We carried out a comparison of known families of PD-(D/E)XK nucleases to the database comprising the COG and PFAM profiles corresponding to both functionally characterized as well as uncharacterized protein families to detect significant similarities. The initial candidates for new nucleases were subsequently verified by sequence-structure threading, comparative modeling, and identification of potential active site residues.
Conclusion
In this article, we report identification of the PD-(D/E)XK nuclease domain in numerous proteins implicated in interactions with DNA but with unknown structure and mechanism of action (such as putative recombinase RmuC, DNA competence factor CoiA, a DNA-binding protein SfsA, a large human protein predicted to be a DNA repair enzyme, predicted archaeal transcription regulators, and the head completion protein of phage T4) and in proteins for which no function was assigned to date (such as YhcG, various phage proteins, novel candidates for restriction enzymes). Our results contributes to the reduction of "white spaces" on the sequence-structure-function map of the protein universe and will help to jump-start the experimental characterization of new nucleases, of which many may be of importance for the complete understanding of mechanisms that govern the evolution and stability of the genome.
doi:10.1186/1471-2105-6-172
PMCID: PMC1189080  PMID: 16011798
9.  Characterization of the cofactor-binding site in the SPOUT-fold methyltransferases by computational docking of S-adenosylmethionine to three crystal structures 
BMC Bioinformatics  2003;4:9.
Background
There are several evolutionarily unrelated and structurally dissimilar superfamilies of S-adenosylmethionine (AdoMet)-dependent methyltransferases (MTases). A new superfamily (SPOUT) has been recently characterized on a sequence level and three structures of its members (1gz0, 1ipa, and 1k3r) have been solved. However, none of these structures include the cofactor or the substrate. Due to the strong evolutionary divergence and the paucity of experimental information, no confident predictions of protein-ligand and protein-substrate interactions could be made, which hampered the study of sequence-structure-function relationships in the SPOUT superfamily.
Results
We used the computational docking program AutoDock to identify the AdoMet-binding site on the surface of three MTase structures. We analyzed the sequence divergence in two distinct lineages of the SPOUT superfamily in the context of surface features and preferred cofactor binding mode to propose specific function for the conserved residues.
Conclusion
Our docking analysis has confidently predicted the common AdoMet-binding site in three remotely related proteins structures. In the vicinity of the cofactor-binding site, subfamily-conserved grooves were identified on the protein surface, suggesting location of the target-binding/catalytic site. Functionally important residues were inferred and a general reaction mechanism, involving conformational change of a glycine-rich loop, was proposed.
doi:10.1186/1471-2105-4-9
PMCID: PMC153507  PMID: 12689347
10.  RNA:(guanine-N2) methyltransferases RsmC/RsmD and their homologs revisited – bioinformatic analysis and prediction of the active site based on the uncharacterized Mj0882 protein structure 
BMC Bioinformatics  2002;3:10.
Background
Escherichia coli guanine-N2 (m2G) methyltransferases (MTases) RsmC and RsmD modify nucleosides G1207 and G966 of 16S rRNA. They possess a common MTase domain in the C-terminus and a variable region in the N-terminus. Their C-terminal domain is related to the YbiN family of hypothetical MTases, but nothing is known about the structure or function of the N-terminal domain.
Results
Using a combination of sequence database searches and fold recognition methods it has been demonstrated that the N-termini of RsmC and RsmD are related to each other and that they represent a "degenerated" version of the C-terminal MTase domain. Novel members of the YbiN family from Archaea and Eukaryota were also indentified. It is inferred that YbiN and both domains of RsmC and RsmD are closely related to a family of putative MTases from Gram-positive bacteria and Archaea, typified by the Mj0882 protein from M. jannaschii (1dus in PDB). Based on the results of sequence analysis and structure prediction, the residues involved in cofactor binding, target recognition and catalysis were identified, and the mechanism of the guanine-N2 methyltransfer reaction was proposed.
Conclusions
Using the known Mj0882 structure, a comprehensive analysis of sequence-structure-function relationships in the family of genuine and putative m2G MTases was performed. The results provide novel insight into the mechanism of m2G methylation and will serve as a platform for experimental analysis of numerous uncharacterized N-MTases.
doi:10.1186/1471-2105-3-10
PMCID: PMC102759  PMID: 11929612
11.  mRNA:guanine-N7 cap methyltransferases: identification of novel members of the family, evolutionary analysis, homology modeling, and analysis of sequence-structure-function relationships 
BMC Bioinformatics  2001;2:2.
Background
The 5'-terminal cap structure plays an important role in many aspects of mRNA metabolism. Capping enzymes encoded by viruses and pathogenic fungi are attractive targets for specific inhibitors. There is a large body of experimental data on viral and cellular methyltransferases (MTases) that carry out guanine-N7 (cap 0) methylation, including results of extensive mutagenesis. However, a crystal structure is not available and cap 0 MTases are too diverged from other MTases of known structure to allow straightforward homology-based interpretation of these data.
Results
We report a 3D model of cap 0 MTase, developed using sequence-to-structure threading and comparative modeling based on coordinates of the glycine N-methyltransferase. Analysis of the predicted structural features in the phylogenetic context of the cap 0 MTase family allows us to rationalize most of the experimental data available and to propose potential binding sites. We identified a case of correlated mutations in the cofactor-binding site of viral MTases that may be important for the rational drug design. Furthermore, database searches and phylogenetic analysis revealed a novel subfamily of hypothetical MTases from plants, distinct from "orthodox" cap 0 MTases.
Conclusions
Computational methods were used to infer the evolutionary relationships and predict the structure of Eukaryotic cap MTase. Identification of novel cap MTase homologs suggests candidates for cloning and biochemical characterization, while the structural model will be useful in designing new experiments to better understand the molecular function of cap MTases.
doi:10.1186/1471-2105-2-2
PMCID: PMC35267  PMID: 11472630

Results 1-11 (11)