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Protein-DNA interaction detection software tools

Protein-DNA complexes play vital roles in many cellular processes by the interactions of amino acids with DNA. Several computational methods have been developed for predicting the interacting residues in DNA-binding proteins using sequence and/or structural information.

Source text:
(Nagarajan et al., 2013) Novel approach for selecting the best predictor for identifying the binding sites in DNA binding proteins. Nucleic Acids Res.

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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.
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.
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.
PiDNA / Predicting Protein-DNA Interactions
Assists users to determine protein–DNA interactions with structural models. PiDNA aims to construct reliable position weight matrices (PWMs) by applying an atomic-level knowledge-based scoring function on numerous in silico mutated complex structures. It then determines the interaction between a protein and a single DNA sequence using the PWM suggested by the structure models with small energy changes. Moreover, this tool is able to detect the chain identifiers of double-stranded DNA (dsDNA) molecules present in the structure.
A web server for the identification of nucleotide-binding sites in protein structures. Nucleos compares the structure of a query protein against a set of known template 3D binding sites representing nucleotide modules, namely the nucleobase, carbohydrate and phosphate. Structural features, clustering and conservation are used to filter and score the predictions. The predicted nucleotide modules are then joined to build whole nucleotide-binding sites, which are ranked by their score. The server takes as input either the PDB code of the query protein structure or a user-submitted structure in PDB format. The output of Nucleos is composed of ranked lists of predicted nucleotide-binding sites divided by nucleotide type (e.g. ATP-like). For each ranked prediction, Nucleos provides detailed information about the score, the template structure and the structural match for each nucleotide module composing the nucleotide-binding site.
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.
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).
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.
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 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.
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.
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.
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 predictor for identifying DNA-binding proteins only based on the sequence information of proteins. iDNAPro-PseAAC extends the classic PseAAC approach by incorporating the evolutionary information in the form of profile-based protein representation. Experimental results on an updated benchmark dataset showed that iDNAPro-PseAAC outperformed some state-of-the-art approaches, and it can achieve stable performance on an independent dataset. By using an ensemble learning approach to incorporate more negative samples (non-DNA binding proteins) in the training process, the performance of iDNAPro-PseAAC was further improved.
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.
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.
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.
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.
A web server for predicting DNA-binding proteins from their amino acid sequence. First, we analyzed the amino acid composition of DNA-binding proteins and based on the observation, SVM models have been developed using amino acid, dipeptide and four-part amino acid compositions of proteins. Besides composition, we also developed SVM models using PSSM profiles obtained from PSI-BLAST. We also examined the performance of similarity search (BLAST and PSI-BLAST) and motif-finding (MEME/MAST) approaches.
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