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HYCUD / HYdrodynamic CoUpling of Domains
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.
motion detection
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.
SPECTRUS / SPECTral-based Rigid Units Subdivision
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.
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