Dynamic domain identification software tools | Protein structure data analysis
Structure of most proteins is flexible. Identification and analysis of intramolecular motions is a complex problem. Breaking a structure into relatively rigid parts, so-called dynamic domains, may help comprehend the complexity of protein’s mobility.
A web resource that can be used to subdivide protein structures in quasi-rigid dynamical domains. The latter are groups of amino acids behaving as approximately rigid units in the course of protein equilibrium fluctuations. The PiSQRD server takes as input a biomolecular structure and the desired fraction of protein internal fluctuations that must be accounted for by the relative rigid-body motion of the dynamical domains. Next, the lowest energy modes of fluctuation of the protein (optionally provided by the user) are calculated and used to identify the rigid subunits. The resulting optimal subdivision is returned through a web page containing both interactive graphics and detailed data output.
Calculates geometrical variability for pairs of amino acids to constructs a contact matrix, for which a spectral clustering is carried out. These clusters correspond to dynamic domains: quasi-rigid structural parts of the protein molecule. Dynamic domains found by ResiCon are more compact than those identified by two other popular methods: PiSQRD and GeoStaS.
A program with graphical interface which, based on molecular conformations, divides a molecule into "dynamic domains", i.e., parts that stay relatively rigid in all conformations. This division simplifies the description of differences between the conformations. The algorithm implemented in GeoStaS is based on searching for geometrical similarities between atomic motions. The pairwise atomic movement similarity matrix (AMSM) is constructed and then clustered with the use of either specifically adapted nearest-neighbor clustering algorithm (which suggests an optimal solution) or a hierarchical merging algorithm (which requires inputting the number of desired domains). GeoStaS can analyze conformations of both proteins and nucleic acids.
Predicts the real value of B-factor. PredBF is based on the Support Vector Regression (SVR) model. It was tested on a benchmark dataset and results show that it achieves correlation coefficient values of 0.55 on the testing datasets. The tool appears to be powerful for predicting the B-factor profile directly from the amino acid sequence.