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.
Kinesin Eg5 druggable sites, including allosteric inhibitor binding site and and tubulin binding site, identified by simulations are shown. See our publication for details.
Sampling of the functional substates (inward-facing (IF) or outward-facing (OF), in closed (c) or open (o) forms) of LeuT using coMD simulations. See publication for details.
Energy landscape in the space of principal coordinates.
Outward-facing (OF) and inward-facing (IF) structures of GltPh show a large displacement of the core domains. See publication for details.
The second mode of the OF structure moves all three transport domains simultaneously through the membrane in a ‘lift-like’ motion. See publication for details.
The second mode of the IF structure moves all three transport domains simultaneously through the membrane in a ‘lift-like’ motion. See publication for details.
Deformability profile of ubiquitin (PDB code: 1UBI). Structure is automatically uploaded to VMD program where blue color shows regions which are mechanically more resistant to the external force.
Mean value of effective spring constant (calculated from mechanical stiffness matrix) with secondary structure of ubiquitin. Blue color indicates mechanically strong regions.
Mechanical Stiffness Map with effective force constant in a color bar (blue - strong regions, red - weak regions) for ubiquitin.
Workflow for GNM analysis of chromatin dynamics. See publication for details.
Covariance matrix of chromosome 17 of human B cells. Structural domains and CCDDs are identified and outlined. See publication for details.
3D Laplacian embedding of chromosome 17 loci using the first three principal modes. See ChromD tutorial for details.
Perturbation response scanning of the Hsp70 chaperone reveals interdomain allostery. See publication for details.
Perturbation response scanning of the AMPA-type glutamate receptor reveals sensors and effectors for allosteric signaling. See publication for details.
A more in-depth analysis of the PRS matrix reveals interdomain signaling in the AMPA receptor. See publication for details.
The left panels show the three softest GNM modes (blue lines) and their standard deviations (faint blue bands). Red and blue regions in the corresponding ribbon diagrams show regions moving in opposite directions. The right panel has the average cross-correlation matrix from the first 20 global modes (top) and its standard deviation (bottom).
Square fluctuations calculated from the top 5 global modes are shown for a number of LeuT fold family members, revealing similarities and subfamily- or conformation-dependent differences.
Type-I periplasmic binding protein domains are mapped onto the first two signature ANM modes. These domains, found in a range of proteins including bacterial solute carriers and eukaryotic receptors, have two lobes that undergo well-characterised conserved motions that are evident from comparison of structures. SignDy reveals such conserved dynamics.
SignDy analysis allows a comparison of the frequency dispersion of family members. The distribution of inverse eigenvalues is shown for the softest five modes for TIM barrel fold family.
Pharmmaker (center) includes four main programs (steps 2 to 5 of the pipeline) that bridge druggability simulations from DruGUI (left) with pharmacophore-based virtual screening (right). The names of the programs are given under each step in blue.
(Left) A snapshot extracted from druggability simulations for an AMPAR LBD dimer using Pharmmaker including probe poses and target conformation. Dominant binding interactions between probe and residues are shown. (Right) A pharmacophore model built based on the snapshot. One hydrogen acceptor, one donor, and two hydrophobic features were used to represent the probes.
Each residue is given a binding value for each probe type, based on an inverse square distance potential. This is shown in the two graphs for the two subunits of an AMPAR LBD dimer. The dotted lines indicate a cutoff of 500, above which residues are defined as high affinity residues for a particular probe.
coMD is a hybrid method with a VMD plugin designed for efficient simulations of conformational changes. ENM modes selected by a Monte Carlo/Metropolis algorithm are used for accelerating the MD trajectories (Gur et. al, 2013). CoMD can be used for estimating the conformational energy landscape (Gur et. al, 2015).
coMD can be used for
coMD plugin files can be obtained from Getting coMD.
Gur M, Madura DJ, Bahar I Global Transitions of Proteins Explored by a Multiscale Hybrid Methodology: Application to Adenylate Kinase 2013 Biophys J 105(7):1643-1652.
Gur M, Zomot E, Cheng MH, Bahar I. Energy Landscape of LeuT from Molecular Simulations. 2015 J Chem Phys 143(24):243134.
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