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NASSAM / Nucleic Acids Search for Substructures and Motifs
A graph theoretical program that can search for 3D patterns of base arrangements by representing the bases as pseudo-atoms. The geometric relationship of the pseudo-atoms to each other as a pattern can be represented as a labeled graph where the pseudo-atoms are the graph's nodes while the edges are the inter-pseudo-atomic distances. The input files for NASSAM are PDB formatted 3D coordinates. This web server can be used to identify matches of base arrangement patterns in a query structure to annotated patterns that have been reported in the literature or that have possible functional and structural stabilization implications.
A program for annotating atomic-resolution RNA 3D structure files and searching them efficiently to locate and compare RNA 3D structural motifs. WebFR3D provides on-line access to the central features of FR3D, including geometric and symbolic search modes, without need for installing programs or downloading and maintaining 3D structure data locally. In geometric search mode, WebFR3D finds all motifs similar to a user-specified query structure. In symbolic search mode, WebFR3D finds all sets of nucleotides making user-specified interactions.
DSSR / Dissecting the Spatial Structure of RNA
An integrated and automated tool for analyzing and annotating RNA tertiary structures. DSSR identifies canonical and noncanonical base pairs, including those with modified nucleotides, in any tautomeric or protonation state. It detects higher-order coplanar base associations, termed multiplets. DSSR finds arrays of stacked pairs, classifies them by base-pair identity and backbone connectivity, and distinguishes a stem of covalently connected canonical pairs from a helix of stacked pairs of arbitrary type/linkage. DSSR identifies coaxial stacking of multiple stems within a single helix and lists isolated canonical pairs that lie outside of a stem. The program characterizes ‘closed’ loops of various types (hairpin, bulge, internal, and junction loops) and pseudoknots of arbitrary complexity. Notably, DSSR employs isolated pairs and the ends of stems, whether pseudoknotted or not, to define junction loops.
A computational server for comparison of RNA 3D models with the reference structure and for discrimination between the correct and incorrect models. Our approach is based on the idea of local neighborhood, defined as a set of atoms included in the sphere centered around a user-defined atom. A unique feature of the RNAssess is the simultaneous visualization of the model-reference structure distance at different precision levels, from the individual residues to the entire molecules.
MINT / Motif Identifier for Nucleic acids Trajectory
An automatic tool for analyzing three-dimensional structures of RNA and DNA, and their full-atom molecular dynamics trajectories or other conformation sets (e.g. X-ray or nuclear magnetic resonance-derived structures). For each RNA or DNA conformation MINT determines the hydrogen bonding network resolving the base pairing patterns, identifies secondary structure motifs (helices, junctions, loops, etc.) and pseudoknots. MINT also estimates the energy of stacking and phosphate anion-base interactions. For many conformations, as in a molecular dynamics trajectory, MINT provides averages of the above structural and energetic features and their evolution.
R3D-2-MSA / RNA 3D Structure-to-Multiple Sequence Alignment
Provides seamless, nucleotide-level access to high-quality, curated sequence alignments for rRNAs of all major phylogenetic domains, using residue numbers taken directly from representative, atomic-resolution 3D structures selected from PDB/NDB for completeness and quality. R3D-2-MSA is extensible to additional classes of structured RNAs. Its programmatic interface facilitates the use of RNA sequence variability data in a wide range of bioinformatic applications, including RNA 3D modeling, evolutionary studies and identification of non-coding RNA genes in genomes. The server also provides an attractive, easy-to-use interface for manual use by bench scientists and students learning about RNA structure and evolution.
COGNAC / COnnection tables Graphs for Nucleic ACids
A graph theoretical algorithm implemented as a web server that is able to search for unbroken networks of hydrogen-bonded base interactions and thus provide an accounting of such interactions in RNA 3D structures. COGNAC is also able to compare the hydrogen bond networks between two structures and from such annotations enable the mapping of atomic level differences that may have resulted from conformational changes due to mutations or binding events.
RNA-MoIP / RNA Motifs over Integer Programming
Uses an integer programming framework to predict RNA 2D-3D structures. RNA-MoIP is an accurate hybrid method that uses RNA secondary structure templates in which it inserts candidate RNA 3D motifs. This server was designed with the goal of making the unique IP-based approach to tertiary structure prediction available to all users. It can be used as a valuable tool for applications requiring both fast and accurate 3D structure predictions, such as RNA structure, function and interaction prediction.
Proposes an RNA fragment assembly method that preserves RNA global secondary structure while sampling conformations. RSIM provides a fully automated application predicting RNA tertiary structures from secondary structure constraints using fragment assembly. These tertiary structures are further refined with Monte Carlo simulations utilizing a sampling method, an expanded statistical potential, and a diverse fragment library. Furthermore, RSIM tracks simulation paths during refinement. This allows representation of the predicted RNA conformational space as a graph with secondary structures as nodes and simulation paths as edges. Graph theoretic analysis can then be applied to predict regions in the conformational space most likely to contain native-like RNA structures.
PRI-Modeler / protein–RNA interaction modeler
Recognizes the secondary and tertiary structures of RNA. PRI-Modeler analyzes the hydrogen bond and van der Waals interactions between protein and RNA from the three-dimensional atomic coordinates of protein-RNA complexes in the protein data bank. It identifies base pairs and classifies base pairs into 28 types to extract secondary and tertiary structures. It can analyze multiple PDB files at once and provides the analysis report on protein-RNA interactions in all the PDB files analyzed.
Provides a user friendly tool for the prediction of RNA structure and stability. Vfold offers a web interface to predict (a) RNA two-dimensional structure from the nucleotide sequence, (b) three-dimensional structure from the two-dimensional structure and the sequence, and (c) folding thermodynamics (heat capacity melting curve) from the sequence. To predict the two-dimensional structure (base pairs), the server generates an ensemble of structures, including loop structures with the different intra-loop mismatches, and evaluates the free energies using the experimental parameters for the base stacks and the loop entropy parameters given by a coarse-grained RNA folding model (the Vfold model) for the loops. To predict the three-dimensional structure, the server assembles the motif scaffolds using structure templates extracted from the known PDB structures and refines the structure using all-atom energy minimization.
GARN / Game Algorithm for RNa sampling
A software to compute a sampling of RNA 3D structure from a secondary structure. GARN is a coarse-grained method for sampling, based on game theory and knowledge-based potentials. Game theory is a suitable tool for understanding systems in which the players have preferences for certain solutions. It favors local, egotistical choices rather than searching for a global optimum. GARN is often much faster than previously described techniques and generates large sets of solutions closely resembling the native structure. GARN is thus a suitable starting point for the molecular modeling of large RNAs, particularly those with experimental constraints.
Using primary and secondary structure information of an RNA molecule, the program automatically and rapidly produces a first-order approximation of a 3-dimensional conformation consistent with this information. Applicable to structures of arbitrary branching complexity and pseudoknot content, it features efficient interactive graphical editing for the removal of any overlaps introduced by the initial generating procedure and for making conformational changes favorable to targeted features and subsequent refinement.
RNAdualPF / RNAdual Partition Function
Computes the dual partition function Z of all RNA nucleotide sequences a compatible with target structure s0. RNAdualPF generates sequences which have low free energy with respect to a user-specified target structure s0 – i.e. the inherent bias of RNAdualPF is known, unlike the situation of other inverse folding algorithms. RNAdualPF additionally allows the user to specify IUPAC codes to constrain certain nucleotide positions as well as to control the GC-content of all generated sequences.
PTRNAmark / Precision Training RNA Mark
An algorithm to evaluate 3D RNA structure. PTRNAmark doesn’t only combine nucleotides’ mutual and self energies but also fully considers the specificity of every RNA. It is based on all-heavy-atom knowledge-based statistical potential. PTRNAmark can fully consider the characteristic of every RNA model and the specificity of physical interaction. It turns out that PTRNAmark performs better than 3DRNAScore, RASP, KB potentials and ROSETTA in ranking a tremendous amount of near-native RNA tertiary structures as well as recognizing native state from a pool of near-native states of RNAs.
Automates design of RNA molecules with complex 3D folds. RNAMake is a toolkit contains a library of hundreds of unique motifs and a path-finding algorithm to connect any two points in 3D space at a desired relative orientation using an RNA segment. These motifs are small modular fragments of RNA that are believed to fold independently, thus attaching them together with helix flanking both sides allows users of RNAMake to build large segments of RNA with a high success rate of forming the predicted structure in vitro.
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