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A normal mode-based geometric simulation approach for exploring biologically relevant conformational transitions in proteins. The approach has been shown to reproduce experimentally observed conformational variabilities in the case of domain and loop motions and is able to generate meaningful pathways of conformational transitions. The generated structures are of good stereochemical quality. Thus, they can serve as input to docking approaches or as starting points for more sophisticated sampling techniques.
Facilitates the exploration of such modes and generates feasible transition pathways between two homologous structures. iMODS supports advanced visualization capabilities for illustrating collective motions, including an improved affine-model-based arrow representation of domain dynamics. Several optimizations have been applied to iMod for supporting online service: (i) the method has been improved, including the implementation of a faster eigenproblem solver, (ii) an affine-modelbased approach has been implemented to facilitate normal mode visualization, and (iii) the simulation of conformational transition trajectories has been extended to address homologous structures.
A tool based on principal component geodesics for conformational analysis using circular data such as bond, torsion and pseudotorsion angles. The web interface of GeoPCA, which implements the principal component geodesics approach, requires as input, a file with angular data. It yields as output: (i) Cartesian coordinates of the data points projected on the first and second principal component geodesics of a sphere (orthogonal great circles on the sphere) and (ii) the values of two angles representing corresponding distances to first and second principal component geodesics for each data point. GeoPCA thus provides a useful way of visualizing, analyzing and predicting conformations of complex macromolecules with many degrees of freedom.
MD-dMD / Maxwell-Demon discrete Molecular Dynamics
A web app for determining pathways for conformational transitions in macromolecules based on the use of discrete molecular dynamics, and biasing techniques based on a combination of essential dynamics and Maxwell-Demon sampling techniques. MD-dMD can work with high efficiency at different levels of resolution, including the atomistic one, and can help to define initial pathways for further exploration by means of more accurate atomistic molecular dynamics simulations.
Pulls interresidue distances of a given conformation towards the distances in the target final conformation using a set of harmonic restraints. In this morphing method, the restraining energy depends linearly on the distance deviation between the current structure in a way that allows a great deal of flexibility to the motion of the protein. Climber moves towards the target by a sufficient amount as measured by the change in coordinate root-mean-square deviation each step to get there in Ncycle steps.
aANM / adaptive Anisotropic Network Model
Elucidates the transition mechanisms in such supramolecular systems. Application to bacterial chaperonin GroEL and comparisons with experimental data, results from action minimization algorithm, and previous simulations support the utility of aANM as a tool for unraveling potential transition pathways sampled by large complexes. The application of aANM to GroEL therefore elucidates not only highly probable pathways or the hierarchic contribution of modes to achieve the transition, but it also provides insights into key interactions that initiate the transition, or those that form/break at the transition state(s).
ANMPathway / Anisotropic Network Model Pathway
Constructs a physically reasonable pathway between two endpoints of a conformational transition. ANMPathway represents a direct application of the “string method” to a two-state coarse-grained system approximated by anisotropic network model (ANM) energy surfaces. It can produce a completely optimized pathway for a 1000 residue protein in about one hour on a single CPU of a standard desktop computer. ANMPathway has been designed to provide a unique solution for the most probable pathway, with a minimal number of parameters and no biased simulations using the ANMs for the two endpoints.
Studies large-scale conformational transitions of proteins between two known structures. PATH-ENM is based on a mathematically simple form of a mixed potential, whose saddle-point and conformation-dependent free energies are easy to compute analytically or numerically. The Mixed Elastic Network Model (MENM) formalism is computationally efficient, as it only requires the construction and inversion of Hessian matrices, and is essentially not limited by the size of proteins. PATH-ENM is useful to explore large-scale conformational transitions with limited computational effort by using a coarse-grained model with a simplified energy function.
iENM / interpolated Elastic Network Model
Generates a transition pathway between the two protein conformations. The key idea of iENM is to solve the saddle points of a general double-well potential function constructed from two elastic network model potential functions based at the beginning and end conformations of a transition. This mathematical problem is converted to the search for the minima of a linear interpolation of the above two Elastic Network Model (ENM) potential functions.
MaxOcc / Maximum Occurrence
Permits users to prepare and run calculations. MaxOcc hides complexity of the interaction with the grid. Its calculations are run on the WeNMR grid, allowing a few hundred conformations to be analyzed simultaneously. This tool accepts as input data both paramagnetic Nuclear Magnetic Resonance (NMR) data (pcs, rdc and/or pre data) and Small-Angle X-ray Scattering (SAXS) profiles. It streamlines preparation of input data and the retrieval and analysis of the calculation results.
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