1 - 50 of 89 results

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.

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.


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.


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.

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.


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.


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.


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.

LPI-ETSLP / LPI eigenvalue transformation-based semi-supervised link prediction

Predicts lncRNA-protein interaction. LPI-ETSLP integrates lncRNA-lncRNA similarity network and protein-protein similarity network to predict potential lncRNA-protein interactions. This method is based on the semi supervised learning strategy, which mainly uses the similarity between known data to infer the unknown data. Compared to others machine learning models, LPI-ETSLP does not require negative samples.

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.


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.

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.

MORDOR / MOlecular Recognition with a Driven dynamics OptimizeR

Allows creation of binding site and docking. MORDOR is a software which contains all of the tools for virtual screening. The software can perform the main docking using its own energetic and minimization routines, but it is also interfaced with ANTECHAMBER for building ligand databases and CHARMM for some state-of-the-art molecular simulation such as calculating the solvation energy. It can also find the binding sites at the surface of the receptor to initially place the ligands.


Calculates amino acid contact distances in proteins at different distance threshold from the 3 Dimensional (3D)-structure of the protein. ContPro calculates the distance between selected protein chain residue atoms and interacting partner atoms, and when this distance falls below or equal to the selected distance threshold, this residue is considered as binding residue. It parses the multi model Protein Data Bank (PDB) file, sequence of selected protein chain from the 3D-structure of protein and gives the atomic details of contacts.

PRBR / Prediction of RNA-binding Residues

A method for identifying RNA-binding residues from amino acid sequences. PRBR combines a hybrid feature with the enriched random forest (ERF) algorithm. The hybrid feature is composed of predicted secondary structure information and three novel features: evolutionary information combined with conservation information of the physicochemical properties of amino acids and the information about dependency of amino acids with regards to polarity-charge and hydrophobicity in the protein sequences.


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.


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.

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.