Unlock your biological data

?

Try: RNA sequencing CRISPR Genomic databases DESeq

1 - 25 of 25 results
filter_list Filters
language Programming Language
healing Disease
settings_input_component Operating System
tv Interface
computer Computer Skill
copyright License
1 - 25 of 25 results
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 neutral 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.
PREDITOR
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.
GammaPred
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.
Mocapy++
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.
SHIFTOR
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.
psiphi
Calculates phi and psi torsion angles from protein coordinates. psiphi reads a clean coordinate file of protein structure coordinates and writes a standard EMBOSS report file of psi and phi torsion angles in a specified region of a specified polypeptide chain. The angles are calculated for each alpha carbon (CA) atom from the co-ordinates of the mainchain atoms in the two planes around it. The tool is intended to be used by everyone and everything, from naive users to embedded scripts.
0 - 0 of 0 results
1 - 2 of 2 results
filter_list Filters
thumb_up Fields of Interest
public Country
1 - 2 of 2 results

By using OMICtools you acknowledge that you have read and accepted the terms of the end user license agreement.