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Global Score
Predicts protein interactions with transcripts > 1000nt. Global Score is an algorithm that integrates the information coming from protein and RNA fragments into an overall binding propensity value. It was applied to all RBP-RNA pairs studied by eCLIP, and the number of predicted interactions significantly increases with the read counts, while pairs that are predicted to not interact show the opposite trend. It was also used to predict physical interactions between Xist and RNA-binding proteins and identified 5 interactions with Spen, Hnrnpk, Hrnnpu/Saf-A, Lbr and Ptbp1.
HADDOCK / High Ambiguity Driven protein-protein DOCKing
An information-driven flexible docking approach for the modeling of biomolecular complexes. HADDOCK distinguishes itself from ab-initio docking methods in the fact that it encodes information from identified or predicted protein interfaces in ambiguous interaction restraints (AIRs) to drive the docking process. HADDOCK can deal with a large class of modeling problems including protein-protein, protein-nucleic acids and protein-ligand complexes.
Finds docking transformations that yield good molecular shape complementarity. A wide interface is ensured to include several matched local features of the docked molecules. PatchDock divides the Connolly dot surface representation of the molecules into concave, convex and flat patches. Then, complementary patches are matched to generate candidate transformations. Each candidate is further evaluated by a scoring function that considers both geometric fit and atomic desolvation. An root mean square deviation clustering is applied to the candidate solutions to discard redundant solutions. PatchDock performs structure prediction of protein–protein and protein–small molecule complexes.
NPDock / Nucleic acid-Protein Dock
A web server for modeling of RNA-protein and DNA-protein complex structures. NPDock combines (1) GRAMM for global macromolecular docking, (2) scoring with a statistical potential, (3) clustering of best-scored structures, and (4) local refinement. The NPDock server provides a user-friendly interface and 3D visualization of the results. The smallest set of input data consists of a protein structure and a DNA or RNA structure in PDB format. Advanced options are available to control specific details of the docking process and obtain intermediate results.
A RNA-protein interaction prediction server based on Support Vector Machine (SVM). The RPI-Pred uses primary sequence and predicted structural fragments information of given protein and RNA. The RPI-Pred perform interaction prediction for two different cases; (1) Single RNA with multiple protein entries and (2) Multiple RNA entries with single protein. Usage of RPI-Pred is user friendly, users are requested to submit "one or multiple RNA sequence in fasta format" and upload "one or multiple, predicted protein block information in single file". The final outcome of RPI-Pred shows, whether given RNAs and proteins can possibly interact or not.
Predicts DNA and RNA binding proteins using a non-homology-based approach. BindUP is based on the electrostatic features of the protein surface and other general properties of the protein. BindUP predicts nucleic acid binding function given the proteins three-dimensional structure or a structural model. Additionally, BindUP provides information on the largest electrostatic surface patches, visualized on the server. The server was tested on several datasets of DNA and RNA binding proteins, including proteins which do not possess DNA or RNA binding domains and have no similarity to known nucleic acid binding proteins, achieving very high accuracy.
Permits sequence-based identification and characterization of protein classes. APRICOT is a computational pipeline which identifies functional motifs in protein sequences using Position Specific Scoring Matrices (PSSM) and Hidden Markov Models (HMM) of the functional domains and statistically scores them based on a series of sequence-based features. The software subsequently identifies putative RNA-binding proteins (RBPs) and characterizes them by several biological properties.
Assists users in analyzing non-coding RNA sequences. ARNhAck provides a software that focuses on the sole biochemical and evolutionary properties of the RNA. It gathers biochemical signal from structure probing experiments on RNA mutants with evolutionary information, collected in multiple sequence alignments (MSAs). The method intends to help in the identification, without prior knowledge of potential partners and hot-spots in RNA, involved in RNA–RNA, RNA–Protein, RNA–DNA and RNA–ligand interfaces.
PRince / Protein-RNA interface
Analyzes the structural features and physicochemical properties of the protein-RNA interface. Users need to submit a PDB file containing the atomic coordinates of both the protein and the RNA molecules in complex form (in '.pdb' format). They should also mention the chain identifiers of interacting protein and RNA molecules. The size of the protein-RNA interface is estimated by measuring the solvent accessible surface area buried in contact. For a given protein-RNA complex, PRince calculates structural, physicochemical and hydration properties of the interacting surfaces. All these parameters generated by the server are presented in a tabular format. The interacting surfaces can also be visualized with software plug-in like Jmol. In addition, the output files containing the list of the atomic coordinates of the interacting protein, RNA and interface water molecules can be downloaded.
TCP-seq / translation complex profile sequencing
Uses fast covalent fixation of translation complexes in live cells, followed by RNase footprinting of translation intermediates and their separation into complexes involving either the full ribosome or the small ribosomal subunit (SSU). TCP-seq is a method that is uniquely capable of recording positions of any type of ribosome–mRNA complex transcriptome-wide. This package provides the full TCP-seq protocol for Saccharomyces cerevisiae liquid suspension culture, including a data analysis pipeline.
Predicts protein-protein, protein-peptide, protein-DNA and protein-RNA binding sites. Multi-VORFFIP utilizes a wide range of structural, evolutionary, experimental and energy-based information that is integrated into a common probabilistic framework by means of a Random Forest (RF) ensemble classifier. It is a centralized resource for the prediction of functional sites and is interfaced by a powerful web application tailored to facilitate the use of the method and analysis of predictions to non-expert end-users.
Identifies the RNA binding sites in proteins by combining interaction propensity with other sequence and structure-based features. PRNA predicts RNA interacting residues in proteins by implementing a well-built random forest classifier. The experiments show that this method is able to detect the annotated protein-RNA interaction sites in a high accuracy. It achieves an accuracy of 84.5%, F-measure of 0.85 and area under the curve (AUC) of 0.92 prediction of the RNA binding residues for a dataset containing 205 non-homologous RNA binding proteins, and also outperforms several existing RNA binding residue predictors, such as RNABindR, BindN, RNAProB and PPRint, and some alternative machine learning methods, such as support vector machine, naive Bayes and neural network in the comparison study.
COCOMAPS / bioCOmplexes COntact MAPS
A web application to easily and effectively analyse and visualize the interface in biological complexes (such as protein-protein, protein-DNA and protein-RNA complexes), by making use of intermolecular contact maps. The user only needs to download input files directly from the wwPDB or upload her/his own PDB formatted files and to specify the chain identifiers for the molecules involved in the interaction to be analysed. Please note that more chains can be selected for each interacting partner.
catRAPID signature
Predicts ribonucleoproteins and RNA-binding regions using physico-chemical properties instead of sequence similarity searches. The algorithm, trained on human proteins and tested on model organisms, calculates the overall RNA-binding propensity followed by the prediction of RNA-binding regions. catRAPID signature outperforms other algorithms in the identification of RNA-binding proteins and detection of non-classical RNA-binding regions. Results are visualized on a webpage and can be downloaded or forwarded to catRAPID omics for predictions of RNA targets.
A tool for genome-wide recommendation of RNA-protein interactions. RNAcommender is a recommender system capable of suggesting RNA targets to unexplored RNA binding proteins, by propagating the available interaction information, taking into account the protein domain composition and the RNA predicted secondary structure. RNAcommender can be a valid tool to assist researchers in identifying potential interacting candidates for the majority of RBPs with uncharacterised binding preferences.
Predicts whether a protein binds RNAs, based on the support vector machine (SVM) and on a comprehensive feature representation. By integrating the physicochemical properties with the evolutionary information of protein sequences, RBPPred performed better than state-of-the-art methods. RBPPred correctly predicted 83% of 2780 RNA-binding proteins (RBPs) and 96% out of 7093 non-RBPs with Matthews correlation coefficient (MCC) of 0.808 using the 10-fold cross validation. Furthermore, it was achieved a sensitivity of 84%, specificity of 97% and MCC of 0.788 on the testing set of human proteome. The capability of RBPPred was tested to identify new RBPs, which further confirmed the practicability and predictability of the method.
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A family of classifiers for predicting RNA-protein interactions using only sequence information. Given the sequences of an RNA and a protein as input, RPIseq predicts whether or not the RNA-protein pair interact. The RNA sequence is encoded as a normalized vector of its ribonucleotide 4-mer composition, and the protein sequence is encoded as a normalized vector of its 3-mer composition, based on a 7-letter reduced alphabet representation. RPISeq offers an inexpensive method for computational construction of RNA-protein interaction networks, and should provide useful insights into the function of non-coding RNAs.
Maps the functional networks of long or circular forms of non-coding RNAs (ncRNA). circlncRNAnet allows in-depth analyses of ncRNA biology. The software provides three features: (1) a framework for processing of user-defined next-generation sequencing (NGS)-based expression data, (2) assigning and assessing the regulatory networks of ncRNAs selected by users and (3) a workflow suitable for all types of ncRNAs. It can be used to get multiple lines of functionally relevant information on the circular RNA/ long non-coding RNA (circRNAs/lncRNAs) of users’ interest.
FTDock / Fourier Transform rigid-body Docking
Discretises two molecules onto orthogonal grids and performs a global scan of translational and rotational space. In order to scan rotational space it is necessary to rediscretise one of the molecules (for speed the smaller) for each rotation. The scoring method is primarily a surface complementarity score between the two grids. To speed up the surface complementarity calculations, which are convolutions of two grids, Fourier Transforms are used.
JET / Joint Evolutionary Trees
Detects very different types of interactions of a protein with another protein, ligands, DNA, and RNA. JET, based on the Evolutionary Trace (ET) method, introduces a novel way to treat evolutionary information. It uses carefully designed sampling method, making sequence analysis more sensitive to the functional and structural importance of individual residues. JET also uses a clustering method parametrized on the target structure for the detection of patches on protein surfaces and their extension into predicted interaction sites.
A web app for predicting the interaction between long noncoding RNAs and proteins. By coding RNA and protein sequences into vectors, a matrix multiplication is used to give score to each RNA-protein pair. This score can be the measurement of interactions between the RNA-protein pair. Comparing to existing approaches, this method shortens the time for training matrix. It also theoretically guarantees the results to be the best solution. The method has shown good ability to discriminate interacting/non-interacting RNA-protein pairs and to predict the RNA-protein interaction within a given complex.
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