Dynamics of p38 MAP kinase inferred from a structural ensemble using PCA is compared to intrinsic dynamics of the protein modeled using ANM. See PCA of X-ray structures or Bioinformatics article for more details.
Results from comparative analysis of residue conservation, conformational mobility, and coevolutionary patterns for uracil-DNA glycosylase. See Mol Biol Evol article or Conservation and Coevolution Analysis for more details.
Comparative analysis of dynamics of drug target proteins and model systems from experiments (PCA) and theory (ANM). See the Protein Science article for details.
Comparative analysis of p38 MAP kinase dynamics from experiments (PCA), simulations (EDA), and theory (ANM). See the Protein Science article for details.
Animation shows HIV-1 reverse transcriptase functional motions calculated using anisotropic network model. Arrows and animations are generated using NMWiz VMD plugin. See NMWiz tutorial for usage examples.
You can make a quick protein representation in interactive sessions using showProtein() function.
NMWiz is designed for picturing normal modes easy. Image shows arrows from slowest three ANM modes for p38 MAP kinase centered at the origin. They indeed align with planes normal to each other.
NMWiz makes depicting elastic network models and protein motions predicted with them easy. Image shows ANM model for p38 MAP kinase and three slow ANM modes (below).
NMWiz can be used to comparative dynamics inferred from experimental datasets and predicted using theory.
ProDy is a free and open-source Python package for protein structural dynamics analysis. It is designed as a flexible and responsive API suitable for interactive usage and application development.
Dynamics from experimental datasets, theoretical models and simulations can be visualized using NMWiz.
Bakan A, Meireles LM, Bahar I ProDy: Protein Dynamics Inferred from Theory and Experiments 2011 Bioinformatics 27(11):1575-1577
Continued development of ProDy is supported by NIH through R01 GM099738 award.
Evol is a suite of powerful and efficient API features and applications for analysis of sequence evolution and its comparison to protein functional dynamics. As part of ProDy, Evol helps bridging sequence evolution and functional dynamics by characterization of .
Evol applications allow you to analyze large MSA files, save or plot numerical results without writing any code. You can read the output files and resume analysis in your favorite software.
Sequence Evolution Correlates with Structural Dynamics Liu Y, Bahar I 2012 Mol Biol Evol 29(9):2253-2263
Role of Hsp70 ATPase domain intrinsic dynamics and sequence evolution in enabling its functional interactions with NEFs Liu Y, Gierasch LM, Bahar 2010 PLoS Comput Biol 6(9)
Normal Mode Wizard (NMWiz) is a VMD plugin designed for visual comparative analysis of normal mode data, i.e. modes may come from principal component, essential dynamics, normal mode analysis or may be any vector describing a molecular motion.
NMWiz, designed as a GUI for ProDy, can be used to
ProDy: Protein Dynamics Inferred from Theory and Experiments Bakan A, Meireles LM, Bahar I 2011 Bioinformatics 27(11):1575-1577
v1.4.10 is a bugfix release. v1.4 series come with Python 3 support and new sequence analysis features. See changes for details.
You can install ProDy and Evol using
pip install -U ProDy, or by following these steps:
Extract tarball contents and run setup.py as follows:
$ tar -xzf ProDy-1.4.10.tar.gz $ cd ProDy-1.4.10 $ python setup.py build $ sudo python setup.py installIf you don’t have root access, see alternate installation schemes in Installing Python Modules.
ProDy-1.4.10.win32-py2.z.exe file and follow installation steps.
To be able use ProDy Applications and Evol Applications in command prompt (
cmd.exe), append Python and scripts folders
PATH environment variable.
Learn how to depict normal modes and generate animations of protein dynamics along them with NMWiz.
Learn how to identify conserved and coevolved residues and characterizing their dynamical properties.
Learn how to perform normal mode analysis and developing customized force constant functions.
Learn how to analyze large and heterogeneous ensembles of protein structures to infer dynamical properties.
Learn how to compare and align structures, identify ligand contacts, and extract ligands from PDB files.
Learn how to analyze simulation trajectories, in particular handling large trajectory files that don't fit in memory.
Learn how to generate alternate protein conformations along ANM modes and to refine them using NAMD.
Let us know any problems you might have by opening an issue at the tracker so that we can make ProDy better.