Gorgon is an interactive molecular modeling system specifically geared towards cryo-EM
and other low resolution structures of macromolecular complexes. The long term goal of
the gorgon project is to be able to address to every part of the molecular modeling pipeline
starting from the initial volumetric reconstruction of the complex all the way to the final placement of
each individual atom.
Gorgon currently provides the following feature categories:
Gorgon is being developed as a collaboration between Washington University in St. Louis and Baylor
College of Medicine.
A comprehensive visualization framework for volumetric data (iso-contours,
cross-sections and solid rendering), geometric skeletons, secondary structure elements and atomic
Many geometric operations such as skeletonization (binary / grayscale and interactive), smoothing,
resampling, cropping, etc. are provided.
Secondary structure identification:
SSEHunter is now integrated into Gorgon, which lets you to find the locations of Alpha Helices and Beta Sheets
given a low-resolution density map of a molecule.
Protein structure prediction:
We provide tools which allow users to find the correspondence between
predicted secondary structure elements in the sequence and the observed secondary structure elements in the
Protein backbone tracing:
The c-alpha backbone of a protein can be easily traced manually or semi-automatically
using the many supporting elements offered by Gorgon.
Take the survey
We are currently conducting a survey of Gorgon usage, and future functionality for funding purposes. We would appreciate it
if you could take the survey and help us understand how we can improve Gorgon in the future. The survey consists of 10 questions and
can be found here.
Whats New in Version 2
- Improved correspondence engine which now includes beta sheets for protein structure prediction.
- SSEHunter for Secondary Structure Identification, and alignment of these SSEs to the density.
- Interactive placement of CAlpha Loops.
- Plugin framework allowing plugins to be written in Python.
- Session support, to allow the saving and loading of work-in-progress.
- Better visualization routines.
- Support for many more file formats.
We would like to thank
Media and Machines Lab at
Washington University in St. Louis and
National Center for Macromolecular Imaging at
Baylor College of Medicine .
This work is supported by the NSF grants IIS-0705474, IIS-0705538 and the NCMI grant through the NIH RR002250.