Dihedral angle detection software tools | Protein structure data analysis
A dihedral angle of a protein is the internal angle of polypeptide backbone at which two adjacent planes meet. The conformation of the backbone can be described by two dihedral angles per residue, because the backbone residing between two juxtaposing Cα atoms are all in a single plane. These angles are called φ (phi) which involves the backbone atoms C-N-Cα-C, and ψ (psi) which involves the backbone atoms N-Cα-C-N. Dihedral angle detection software tools are used to predict different sets of structural protein properties, including calculation of dihedral angles.
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.
Provides a software tool for secondary structure assignment from atomic resolution protein structures. STRIDE implements a knowledge-based algorithm that makes combined use of hydrogen bond energy and statistically derived backbone torsional angle information and is optimized to return resulting assignments in maximal agreement with crystallographers' designations. The STRIDE web server provides access to this tool and allows visualization of the secondary structure, as well as contact and Ramachandran maps for any file uploaded by the user with atomic coordinates in the Protein Data Bank (PDB) format. A searchable database of STRIDE assignments for the latest PDB release is also provided as well as a link to the source code to use STRIDE as a stand-alone program.
Allows prediction of secondary structure, accessible surface area and dihedral angles. SPINE-X is a secondary structure prediction method consisting of six steps of iterative prediction of secondary structure (SS), real-value residue solvent accessibility (RSA), and dihedral angles. The software can produce a distribution of three secondary structure states that is very close to the native distribution.
Two automated methods in protein backbone conformational state prediction: one method is based on support vector machines (SVMs); the other method combines a standard feed-forward back-propagation artificial neural network (NN) with a local structure-based sequence profile database (LSBSP1). LSBSP1 and the NN algorithm have been implemented in PrISM.1.
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.
An integrated system of neural networks for real-value prediction and apply the method to predict residue-solvent accessibility and backbone psi dihedral angles of proteins based on information derived from sequences only. Real-SPINE is trained with a large data set of 2640 protein chains, sequence profiles generated from multiple sequence alignment, representative amino-acid properties, a slow learning rate, overfitting protection, and predicted secondary structures.