Domain movement detection software tools | Protein structure data analysis
From a structural perspective domains in proteins can be regarded as quasi-globular regions. The connections between domains allow their relative movement and consequently domain movements are often engaged in protein function.
A Python-based program for prediction of rotational correlation time of folded protein domains in the context of flexible multidomain proteins and protein complexes with long disordered tails and/or interdomain linkers as well as intrinsically disordered proteins. HYCUD starts from a structural ensemble of the protein of interest together with a definition of its modular architecture, calculates the effective viscosity experienced locally by each of its modules and correspondingly scales the reorientational correlation time of isolated domains obtained through experiment or standard hydrodynamic calculations, and finally averages the scaled correlation times over the structural ensemble. The use of HYCUD has been validated in several protein systems differing in size, domain architecture, assembly state and disorder level.
Detects rigid-body movements in protein structures. Our model aims to approximate alternative conformational states by a few structural parts that are rigidly transformed under the action of a rotation and a translation. By using Bayesian inference and Markov chain Monte Carlo sampling, we estimate all parameters of the model, including a segmentation of the protein into rigid domains, the structures of the domains themselves, and the rigid transformations that generate the observed structures. We find that our Gibbs sampling algorithm can also estimate the optimal number of rigid domains with high efficiency and accuracy.
Identifies dynamical domains in proteins. SPECTRUS uses a dimensional reduction of the inter-residue distance fluctuations and exploits its properties to single out the intrinsic number and type of domains. SPECTRUS takes as input the matrix of pairwise distance fluctuations of amino acids that can be obtained from various sources: it can be computed either from a limited number of available crystal structures or from conformations sampled with extensive molecular dynamics (MD) trajectories, or derived from elastic network models (ENMs), when a single conformation of the molecule of interest is available. The main requirement for reliable subdivisions is that the input structures should be heterogeneous enough that their difference captures the biologically relevant rearrangements of the molecule.
Intends to enable the general applicability of elastic network models (ENM). ImcENM aims to predict function-related protein motions and capture ligand-coupled localized functional transitions. The algorithm permits to reduce complexity of the deformation space which depends of capture function-related movements. The model was experimented with a set of 90 proteins covering functional transitions.
Identifies rigid blocks from two known conformations of a large macromolecular complex. RigidFinder can detect rigid blocks as small as four residues in size.The server is interactively linked to a multi-chain morphing server with the option of using superposition by any calculated rigid block to generate a morph.
Performs optimization by imitating the attraction and repulsion of anions and cations. IMO is a population-based algorithm inspired from properties of ions in nature. This algorithm divides the population of candidate solutions into two sets of negative charged ions and positive charged ions, and improves them according to the important characteristics of the ions: “ions with the same charges repel each other, but with opposite charges attract each other”. It also mimics liquid state and solid state to perform diversification and intensification.