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Dihedral angle detection software tools | Protein structure data analysis

The prediction of the secondary structure of a protein is a critical step in the prediction of its tertiary structure and, potentially, its function. Moreover, the backbone dihedral angles, highly correlated with secondary structures, provide crucial information about the local three-dimensional structure. Source text: Kountouris and Hirst, 2009.

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SPIDER / Sequence-based Prediction of Local and Nonlocal Structural Features for Proteins
Predicts different sets of structural protein properties. SPIDER is an iterative deep-learning neural network. It obtains secondary structure, torsion angles, Cα−atom based angles and dihedral angles, and solvent accessible surface area. It utilises both local and nonlocal structural information in iterations. At each iteration, SPIDER employs a deep-learning neural network to predict a structural property based on structural properties predicted in the previous iteration.
A web server for predicting protein torsion angle restraints. PREDITOR accepts sequence and/or chemical shift data as input and generates torsion angle predictions (with predicted errors) for phi, psi, omega and chi-1 angles. PREDITOR combines sequence alignment methods with advanced chemical shift analysis techniques to generate its torsion angle predictions. The method is fast (<40 s per protein) and accurate, with 88% of phi/psi predictions being within 30 degrees of the correct values, 84% of chi-1 predictions being correct and 99.97% of omega angles being correct. PREDITOR is 35 times faster and up to 20% more accurate than any existing method.
PDMS / Protein Dipole Moments Server
Performs calculation of the net charge and dipole moment. PDMS is a web server that also incorporates calculation of a protein’s mass moments and mean radius (the geometric average of its three mass moments) for addressing the question of correlation of a protein’s dipole moment with its overall shape. Moreover, it can display the angles between the dipole vector. The software can be used to screen proteins rapidly for the presence of interesting electrostatic properties.
TNM / Torsion Network Model
Allows computation of the normal modes of the structure-based model of a protein of known structure in the space of torsion angles. TNM is an elastic network model (ENM) that uses the torsion angles of the protein backbone as degrees of freedom, combining the topology of the native structure with the constraints imposed by the covalent geometry of proteins. The software normal modes enable the reconstruction of the positions of atoms and the definition of different inter-residues interactions.
CONFECT / Conformations from an Expert Collection of Torsion patterns
Provides a conformer generator. CONFECT is an approach dedicated for computational modeling such as structural superimposition, docking or manual analysis of conformational space. It combines a torsion pattern hierarchy with an incremental construction-based sampling algorithm, suited for small conformational ensembles. The application is available as part of the TorsionAnalyzer software package or as a standalone software on demand.
Predicts the gamma turn residues in the given protein sequence. The method is based on the neural network training on PSI-BLAST generated position specific matrices and PSIPRED predicted secondary structure. Two neural networks with a single hidden layer have been used where the first sequence-to-structure network is trained on PSI-BLAST obtained position specific matrices. The filtering has been done by second structure-to-structure network trained on output of first net and PSIPRED predicted secondary structure. The training has been carried out using error backpropagation with a sum of square error function (SSE).
DISSPred / Dihedral angles and Secondary Structure Prediction
An accurate predictor of backbone dihedral angles and secondary structure. Using predicted secondary structure and dihedral angles, our method improves the predictive accuracy of both secondary structure and dihedral angle prediction in an iterative process using SVMs. The achieved secondary structure Q3 accuracy of 80% on a set of 513 non-redundant proteins shows that our method is more accurate than other secondary structure prediction methods.
Constructs probabilistic models of biomolecular structure, due to its support for directional statistics. Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distributions, including distributions from directional statistics (the statistics of angles, directions and orientations). The tool is suitable for the Kent distribution on the sphere and the bivariate von Mises distribution on the torus.
Predicts a large number of protein torsion angles (phi, psi, omega, chi1) using only 1H, 13C and 15N chemical shift assignments as input. SHIFTOR program is capable of predicting chi1 angles with 81% accuracy and omega angles with 100% accuracy. SHIFTOR exploits many developments and observations regarding chemical shift dependencies as well as using information in the Protein Databank (PDB) to improve the quality of its shift-derived torsion angle predictions. SHIFTOR is available as a freely accessible web server.
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