3D Slicer is: A software platform for the analysis (including registration and interactive segmentation) and visualization (including volume rendering) of medical images and for research in image guided therapy. A free, open source software available on multiple operating systems: Linux, MacOSX and Windows Extensible, with powerful plug-in capabilities for adding algorithms and applications. Features include: Multi organ: from head to toe. Support for multi-modality imaging including, MRI, CT, US, nuclear medicine, and microscopy. Bidirectional interface for devices. There is no restriction on use, but Slicer is not approved for clinical use and intended for research. Permissions and compliance with applicable rules are the responsibility of the user.
The first database of 218 allosteric modulators of one synaptic receptor with structural annotation.
The Glycine Receptor Allosteric Ligand Library (GRALL) is the first database of allosteric modulators of a synaptic receptor with structural annotation.
- GRALL offers a collection of 218 unique chemical entities with documented modulatory activities at homomeric glycine receptors α1 and α3.
- The collection includes agonists, antagonists, positive and negative allosteric modulators and a number of experimentally inactive compounds.
- For each molecular entry, GRALL provides information on the chemical structure, the direction of modulation, the potency, the 3D molecular structure and quantum-mechanical charges.
- A large fraction of modulators comes with a structural annotation of their ligand-binding site on the receptor, which provides a stringent benchmark to develop in silico strategies for allosteric drug design.
This type of annotation, which is currently missing in other drug banks, is expected to improve the predictivity of in-silico methodologies for allosteric drug design and boost the development of conformation-based pharmacological approaches.
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A MATLAB® toolbox that given a three-dimensional spine reconstruction computes a set of characteristic morphological measures that unequivocally determine the spine shape.
Dendritic spines of pyramidal neurons are the targets of most excitatory synapses in the cerebral cortex and their morphology appears to be critical from the functional point of view. Thus, characterizing this morphology is necessary to link structural and functional spine data and thus interpret and make them more meaningful. We have used a large database of more than 7,000 individually 3D reconstructed dendritic spines from human cortical pyramidal neurons that is first transformed into a set of 54 quantitative features characterizing spine geometry mathematically. The resulting data set is grouped into spine clusters based on a probabilistic model with Gaussian finite mixtures. We uncover six groups of spines whose discriminative characteristics are identified with machine learning methods as a set of rules. The clustering model allows us to simulate accurate spines from human pyramidal neurons to suggest new hypotheses of the functional organization of these cells.