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Provides rapid, accurate and fully automated calculation of tunnels and channels in static structures. The molecules amendable to analysis of CAVER include proteins, nucleic acids or inorganic materials. CAVER can be used either as PyMol plugin or independent application CAVER Analyst. CAVER Analyst has been designed for easy set-up of calculation, visualization of results and efficient data analysis. It can be used for both static structures and molecular ensembles from molecular dynamic simulations or NMR.
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A web-based interactive application for the analysis of access/egress paths to interior molecular voids. MOLEonline 2.0 enables platform-independent, easy-to-use and interactive analyses of (bio)macromolecular channels, tunnels and pores. Results are presented in a clear manner, making their interpretation easy. For each channel, MOLEonline displays a 3D graphical representation of the channel, its profile accompanied by a list of lining residues and also its basic physicochemical properties.
Finds protein cavities throughout Molecular Dynamics (MD) simulation trajectories. trj_cavity can analyse the solvent occupancy of the cavities identified. This tool is implemented within the GROMACS framework for the rapid identification and characterization of cavities detected within MD trajectories. It is also optimized for usability and computational efficiency and is applicable to the time-dependent analysis of any cavity topology, and optional specialized descriptors can be used to characterize, protein channels.
A fully automated method designed to detect and characterise transmembrane protein channels from their 3D structure. A stepwise procedure is followed in which the pore centre and pore axis are first identified and optimised using geometric criteria, and then the biggest and longest cavity through the channel is detected. Finally, pore features, including diameter profiles, pore-lining residues, size, shape and regularity of the pore are calculated, providing a quantitative and visual characterization of the channel.
Allows one to generate a Brownian dynamics trajectory of ions in a channel system. GCMC/BD is a web-based graphical user interface presented for grand canonical Monte Carlo (GCMC) BD simulations of channel proteins. This is a method used to maintain the desired electrochemical conditions in the boundaries (buffer regions) of the simulation system which are in (equilibrium) contact with the bulk. The webserver is designed to help users avoid most of the technical difficulties and issues encountered in setting up and simulating complex pore systems.
A sequence-based predictor for identifying the subfamilies of voltage-gated potassium channels. iVKC-OTC has been developed by incorporating the optimized tripeptide composition (OTC) generated by feature selection technique into the general form of pseudo-amino acid composition to identify six subfamilies of voltage-gated K+ channels (VKCs). One of the remarkable advantages of introducing the optimized tripeptide composition is being able to avoid the notorious dimension disaster or over fitting problems in statistical predictions.
A toolkit for identifying pockets, cavities and channels of protein structures. The toolkit was developed in PERL programming language and includes “PoreID” for pore identification, “PoreTrace” for pore axes determination and “GateOpen” for opening the gate between neighboring pores. “PoreID” is a grid-based method that avoids orientation dependency of the results. It targets all kinds of pores (pockets, cavities and channels) and is automatic so that only the PDB file of the target protein has to be specified by the user.
PrinCCes / Protein internal Channel & Cavity estimation
A computer program supporting the visualization of voids. PrinCCes includes a novel algorithm for the decomposition of the entire void volume of the protein or protein complex to individual entities. The decomposition is based on continuity. An individual void is defined by uninterrupted extension in space: a spherical probe can freely move between any two internal locations of a continuous void. Continuous voids are detected irrespective of their topological complexity, they may contain any number of holes and bifurcations. The voids of a protein can be visualized one by one or in combinations as triangulated surfaces.
A support vector machine based method was proposed to predict VKC subfamilies using amino acid and dipeptide compositions. In order to remove redundant information, a novel feature selection technique was employed to single out optimized features. In the jackknife cross-validation, the proposed method (VKCPred) achieved an overall accuracy of 93.09% with 93.22% average sensitivity and 98.34% average specificity, which are superior to that of other two state-of-the-art classifiers.
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