1 - 50 of 80 results


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.

DBSI / DNA Binding Site Identifier

A powerful structure-based SVM model for the prediction and visualization of DNA binding sites on protein structures. DBSI is a machine learning approach to classify surface residues as binders or non-binders of DNA. DBSI employs sequence- and structure-based features encompassing a range of physical, chemical, geometric and evolutionary properties of the protein surface. DBSI also implements microenvironment features that allow for small-scale structural perturbation and the role of non-local cooperative effects. DBSI has been shown to be a top-performing model to predict DNA binding sites on the surface of a protein or peptide and shows promise in predicting RNA binding sites.


A web server for predicting DNA-binding sites in a DNA-binding protein from its amino acid sequence. The web server implements three machine learning methods: support vector machine, kernel logistic regression and penalized logistic regression. Prediction can be performed using either the input sequence alone or an automatically generated profile of evolutionary conservation of the input sequence in the form of PSI-BLAST position-specific scoring matrix (PSSM). PSSM-based kernel logistic regression achieves the accuracy of 77.2%, sensitivity of 76.4% and specificity of 76.6%. The outputs of all three individual methods are combined into a consensus prediction to help identify positions predicted with high level of confidence.


Calculates protein residue Interaction Energy Matrix of amino acids between themselves and with deoxyribonucleotides. INTAA permits to make analysis of the interfaces in protein–DNA complexes. It provides a 3D structure viewer in order to visualize pairwise and net interaction energies of individual amino acids, side chains and backbones. The tool provides a way to examine the relative abundance and interaction energies in various binding configurations of biomolecular building blocks.

fABMACS / fast Adaptively Biased MAchine for Chemical Simulations

An implementation of adaptive biasing that greatly improves the speed of free energy computation in molecular dynamics simulations. fABMACS will be useful in identification of false positives, creation of chemical hypotheses, scoring ligands, and guiding nuclear magnetic resonance (NMR) based fragment screening. The efficiency gains of fABMACS will enable more efficient use of computational resources and make new, ambitious applications more affordable and tractable.

PNImodeler / Protein-Nucleic acid Interaction modeler

Predicts protein binding nucleotides in DNA. PNImodeler is a web server which consists of two models: one model uses DNA sequence data alone and predicts all potential binding sites with unknown protein partners and the other uses both DNA and protein sequences to predict protein binding nucleotides with the specific protein. The two models were tested on independent data set which has different DNA sequences from the model with sequence similarity of 80%.

PSFM-DBT / Position Specific Frequency Matrix-Distance-Bigram Transformation

Recognizes DNA binding proteins (iDBPs) by using Distance-Bigram Transformation (DBT) method. PSFM-DBT calculates the occurrence frequency of a combination of two amino acids separated by a certain distance along the protein sequence. It can transform Position Specific Frequency Matrix (PSFM) into fixed length feature vector. The tool can be useful for protein sequence analysis, especially for studying the structure and function of proteins.


An ensemble classification algorithm to improve the identification of DNA-binding proteins. nDNA-Prot framework is comprised of two stages: a 188-dimension feature extraction method to obtain the protein structure and an ensemble classifier designated as imDC. Experiments using different datasets showed that our method is more successful than the traditional methods in identifying DNA-binding proteins. The identification was conducted using a feature that selected the minimum Redundancy and Maximum Relevance (mRMR). An accuracy rate of 95.80% and an Area Under the Curve (AUC) value of 0.986 were obtained in a cross validation. A test dataset was tested in our method and resulted in an 86% accuracy, versus a 76% using iDNA-Prot and a 68% accuracy using DNA-Prot.


Identifies transcription factors (TFs), predicts their structural superclass, and detects their DNA-binding domains given a protein sequence of interest. TFpredict is implemented in a Java application which was designed as an upstream tool for SABINE, and provides all structural characteristics required by the SABINE algorithm for the prediction of TF binding specificities. It provides complementary and accurate methods for the identification, structural annotation and DNA motif prediction of TFs.


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.

SAMPDI / Single Amino acid Mutation binding free energy change of Protein-DNA Interaction

Performs fast predictions of binding free energy changes of protein-DNA complexes caused by single mutations on the proteins. SAMPDI combines modified Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) based energy terms with additional knowledge based terms. It employs the so-called rigid body approach which is based on the assumption that the structures do not undergo conformational changes upon binding.


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.

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 highly reliable computational technique for predicting DNA-binding function at the level of protein-DNA complex structures, rather than low-resolution two-state prediction of DNA-binding as most existing techniques do. The method first predicts protein-DNA complex structure by utilizing the template-based structure prediction technique HHblits, followed by binding affinity prediction based on a knowledge-based energy function (Distance-scaled finite ideal-gas reference state for protein-DNA interactions).

PFplus / Patch Finder Plus

A web-based tool for extracting and displaying continuous electrostatic positive patches on protein surfaces. The input required for PFplus is either a four letter PDB code or a protein coordinate file in PDB format, provided by the user. PFplus computes the continuum electrostatics potential and extracts the largest positive patch for each protein chain in the PDB file. The server provides an output file in PDB format including a list of the patch residues. In addition, the largest positive patch is displayed on the server by a graphical viewer (Jmol), using a simple color coding.


forum (1)
A sequence-based predictor for identifying the sequence-specific binding residues in a transcription factor (TF). Concerning protein-DNA interactions, there are two types of binding mechanisms involved, namely sequence-specific binding and nonspecific binding. Sequence-specific bindings occur between protein sidechains and nucleotide bases and correspond to sequence-specific recognition of genes. Therefore, sequence-specific bindings are essential for correct gene regulation. In this respect, ProteDNA is distinctive since it has been designed to identify sequence-specific binding residues. In order to accommodate users with different application needs, ProteDNA has been designed to operate under two modes, namely, the high-precision mode and the balanced mode.


Predicts protein-DNA binding affinity from high-throughput assays that measure the binding affinity. DeeperBind is a long short term recurrent convolutional network for prediction of protein binding specificities with respect to DNA probes. It can model the positional dynamics of probe sequences and hence reckons with the contributions made by individual sub-regions in DNA sequences, in an effective way. This package is an extension of DeepBind which adds a layer of Deep LSTM to model positional information.


Predicts DNA-binding proteins. gDNA-Prot is a DNA-binding predictor that combines the support vector machine classifier and a novel kind of feature called graphical representation. The method is compared with the DNAbinder, iDNA-Prot and DNA-Prot. gDNA-Prot integrates a set of feature descriptors, including the probabilities of 20 AAs and graphical representation features of 23 physicochemical properties indices. The results suggest that gDNA-Prot outperforms the DNAbinder and iDNA-Prot.


A HMM-based method for accurate prediction of RNA and pentatricopeptide repeat protein binding. aPPRove takes as input a PPR protein (primary structure), one or more RNA transcripts or binding footprints, and outputs the binding sites that have highest statistical significance, and how the nucleic acids in the RNA aligned to the amino acid pairs (defined by positions 6 and 1') in the PPR sequence for each binding. The statistical significance is based on the significance of the alignment in comparison to random alignments of the PPR to a database of transcripts.


With the avalanche of protein sequences generated in the postgenomic age, it is a critical challenge to develop automated methods for accurate and rapidly identifying DNA-binding proteins based on their sequence information alone. Here, a predictor, called "iDNA-Prot|dis", was established by incorporating the amino acid distance-pair coupling information and the amino acid reduced alphabet profile into the general pseudo amino acid composition (PseAAC) vector. The former can capture the characteristics of DNA-binding proteins so as to enhance its prediction quality, while the latter can reduce the dimension of PseAAC vector so as to speed up its prediction process.