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.
Gives access to many free software tools for sequence analysis. EMBOSS aims to serve the molecular biology community. It permits the creation and the release of software in an open source spirit. This tool is useful for sequence analysis into a seamless whole. It is free of charge and is available in open source.
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.
Aims to predict nucleic acid binding proteins (NABPs) and identification of their binding sites. Dr. PIP is an automated tool that comprehensively distinguishes between DNA binding protein (DBPs) and non-DBPs. It can also identify DBPs in cases where homology-based prediction would have failed. This application can be used for de-novo prediction.
Provides an intuitive interface for generating OPLS-AA/1.14*CM1A(-LBCC) force field (FF) parameters for organic ligands. LigParGen is a web server which generates ligand parameters for common simulation software packages such as NAMD, GROMACS, OpenMM, BOSS and MCPRO. The software allows the users to obtain high quality parameters for molecular mechanics (MM) simulations without extensive knowledge about MM force fields or quantum mechanics (QM) methods.
A neural network method. Given the structure of a protein known to bind DNA, the method predicts residues that contact DNA. The inputs to the neural network include position-specific sequence profiles and solvent accessibilities of each residue and its spatial neighbors. The neural network is trained on known structures of protein-DNA complexes.
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-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 the RNA-, DNA-, and protein-binding residues located in the intrinsically disordered regions. DisoRDPbind is implemented using a runtime-efficient multi-layered design that utilizes information extracted from physiochemical properties of amino acids, sequence complexity, putative secondary structure and disorder, and sequence alignment. Its outputs complement predictions of representative methods that were built using structured DNA- and RNA-binding residues. Predictions of disordered protein-binding residues generated by DisoRDPbind are characterized by strong correlations, better predictive performance and higher runtime when compared with the closest ANCHOR method.
Relies solely on the protein’s structure, to predict its complex with B-form DNA. ParaDock is an ab-initio, rigid protein–flexible DNA docking algorithm that detects local rigid shape complementarity based docking solutions. This web application is able to predict the specific location, interface, and the specific shape of the DNA molecule, resulting in good ‘fraction of native contacts’ scores.
Allows users to predict and characterize the effect of a single point missense mutation on protein-nucleic acid binding. mCSM-NA can identify single point missense mutations leading to increased or decreased nucleic acid binding affinity. Moreover, it is able to conduct experimentation, shedding light into the mechanistic effect of mutations on a molecular level.
Permits users to predict DNA-binding residues. DR_bind makes predictions by spotting solvent-accessible residues, a cluster of conserved and electrostatically stabilized. It offers users the possibility to view predicted residues highlighted in the given protein structure across a 3D visualization. Also, users can download a PyMol script to visualize these 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.
Provides a user-friendly web access to a hybrid algorithm of template-based modeling and free docking for protein-protein and protein-DNA/RNA complexes. HDOCK is a docking server that efficiently integrates various components including sequence search, template selection and model building. The predictive power of the HDOCK server can be also improved by ranking first of the template-based model.
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).
Assists users with the analysis of the experimental data generated by hydroxyl-radical footprinting (HRF) of DNA-protein complexes. HYDROID is an application that was also developed for the interpretation of results through comparison to theoretical predictions from molecular models. It provides modules that implement functionalities such as the extraction and quantification of DNA cleavage frequency profiles from gel electrophoresis images or the estimation of theoretical DNA cleavage frequency profiles.