Difference between revisions of "Main Page"

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| [http://pymol.org PyMOL v1.8.2 has been released] on April 20, 2016.
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| [[dssr_block]] is a wrapper for DSSR (3dna) and creates block-shaped nucleic acid cartoons
 
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Revision as of 10:57, 20 April 2016

Welcome to the PyMOL Wiki!
The community-run support site for the PyMOL molecular viewer.
New accounts: email jason (dot) vertrees (@) gmail dot com
Quick Links
Tutorials Table of Contents Commands
Script Library Plugins FAQ
Gallery | Covers PyMOL Cheat Sheet (PDF) Getting Help
News & Updates
Official Release PyMOL v1.8.2 has been released on April 20, 2016.
New Script dssr_block is a wrapper for DSSR (3dna) and creates block-shaped nucleic acid cartoons
New Plugin LiSiCA is a new plugin for 2D and 3D ligand based virtual screening using a fast maximum clique algorithm.
Official Release PyMOL v1.8.0 has been released on Nov 18, 2015.
PyMOL Open-Source Fellowship Schrödinger is now accepting applications for the PyMOL Open-Source Fellowship program! Details on http://pymol.org/fellowship
Official Release PyMOL, AxPyMOL, and JyMOL v1.7.6 have all been released on May 4, 2015.
New Plugin PyANM is a new plugin for easier Anisotropic Network Model (ANM) building and visualising in PyMOL.
New Plugin Bondpack is a collection of PyMOL plugins for easy visualization of atomic bonds.
New Plugin MOLE 2.0 is a new plugin for rapid analysis of biomacromolecular channels in PyMOL.
3D using Geforce PyMOL can now be visualized in 3D using Nvidia GeForce video cards (series 400+) with 120Hz monitors and Nvidia 3D Vision, this was previously only possible with Quadro video cards.
Older News See Older News.
Did you know...

PICv

Type PyMOL Plugin
Download PICv
Author(s) Akshay Uttarkar, Vasanth Kumar Desai, Namitha P, John Berrisford, Sameer Velankar, Vidya Niranjan*
License GNU Free Documentation License 1.2




About PICv

PICv.png

Protein interaction clustering and visualization is an pioneer attempt in understanding protein-protein interaction at a residue level. For any given protein the interaction is purely dependent on its charges and surface-structural modifications. The clustering of proteins based on there preferential amino acid interactions provides a biological insight on both the above mentioned aspects. The clusters such obtained can be used to infer the interaction behavior for a class or family of proteins. Such interpretation can be useful in understanding ..→

A Random PyMOL-generated Cover. See Covers.