Journal: Biophysical Journal

Article Title: Structural Disorder of Folded Proteins: Isotope-Edited 2D IR Spectroscopy and Markov State Modeling

doi: 10.1016/j.bpj.2014.12.061

Figure Lengend Snippet: (Left) Network plot of the Markov state model for the native ensemble of NTL91-39. The states are color-coded by RMSD to the crystal structure. The node size encodes the degree, or number of connections to other states, of each state. (Dashed circle) Registry-shifted states (34). (Center) Structure overlay of the four highest populated states. The main differences are observed in the β-turn region. (Right) Nodes color-coded by the pseudo free energy of native states F = −ln(P), where P denotes the respective populations in the full MSM. To see this figure in color, go online.

Article Snippet: As a basis for characterizing structural heterogeneity, we make use of a Markov state model (MSM) that groups conformers from a molecular dynamics (MD) simulation based on exchange kinetics, forming a convenient basis for comparison between MD simulations and experiments ( 28–30 ).

Techniques:

Journal: Biophysical Journal

Article Title: Structural Disorder of Folded Proteins: Isotope-Edited 2D IR Spectroscopy and Markov State Modeling

doi: 10.1016/j.bpj.2014.12.061

Figure Lengend Snippet: (a) Representative structures of the folded β-turn and disordered turn conformations represented in the MSM of NTL91-39 overlaid onto a cartoon structure of the Markov state with lowest RMSD to the crystal structure. (Spheres) Carbonyls corresponding to the V9 and G13 residues. (Yellow dashes) The two folded hydrogen bonds. (Orange double-headed arrow) The V9-M12 hydrogen bond used as the order parameter to distinguish between folded and disordered turn structures. (b) V9-, G13-, and V9G13-label spectra calculated for folded (solid) and disordered (dashed) structures, respectively. (c) Scatter plot of the V9-G13 coupling constant in wavenumbers as a function of rVM distance for the 140 states in the MSM. (Circles are color-coded by the overall RMSD of each state to the crystal structure.) To see this figure in color, go online.

Article Snippet: As a basis for characterizing structural heterogeneity, we make use of a Markov state model (MSM) that groups conformers from a molecular dynamics (MD) simulation based on exchange kinetics, forming a convenient basis for comparison between MD simulations and experiments ( 28–30 ).

Techniques:

Journal: Communications Biology

Article Title: Computational modeling of the olfactory receptor Olfr73 suggests a molecular basis for low potency of olfactory receptor-activating compounds

doi: 10.1038/s42003-019-0384-8

Figure Lengend Snippet: Workflow of virtual screening for new agonists of Olfr73. a Homology modelling of Olfr73 based on 3D structure-sequence alignment of Olfr73 to β 2 AR and Rho. b Refinement of ECL2 loop. c Molecular dynamics (MD) simulations of agonist bound Olfr73. d Docking agonist molecule into the 3D structural model of Olfr73. e Interaction fingerprint analysis by docking 25 reported compounds. This information was subsequently used for PH4 model building in virtual screening. f Virtual screening for Olfr73 agonists in the ZINC compound library composed of 1.58 million drug candidates. Applying stepwise selection filtering based on shape volume, ionization penalty and polarity, downscaled the chemical library successively to 204 compounds. The shape volume features are deduced from the results of MD simulations. Finally, quantitative structure–activity relationship evaluations reduced the chemical library to 64 compounds with predicted potential to activate Olfr73. Out of this final list, agonist binding modes were verified manually based on the activation mechanism deduced from MD simulations, and 25 available compounds were tested by cellular functional assays yielding 17 active compounds

Article Snippet: The PH4 models were built according to the results obtained from both IFP analysis and molecular dynamics simulations.

Techniques: Sequencing, Drug discovery, Selection, Activity Assay, Binding Assay, Activation Assay, Functional Assay

Journal: Communications Biology

Article Title: Computational modeling of the olfactory receptor Olfr73 suggests a molecular basis for low potency of olfactory receptor-activating compounds

doi: 10.1038/s42003-019-0384-8

Figure Lengend Snippet: Hierarchical clustering of Olfr73 agonist molecules. Six different classes of agonists are identified (distinguished by a color code) according to their PH4 features. In the Hierarchical diagram, the links between the chemical compounds are represented as branched vertical lines. The height of the lines, coupled with merging distance (numbers showed in each node), indicate the normalized dissimilarity distance between the adjacent compounds. A higher line or a larger merging distance denotes a larger dissimilarity. A typical representative molecular structure of each class is shown below the dendrogram together with their molecular surfaces indicating hydrophobic moieties in grey and polar moieties in red. The commonly shared atoms within a certain class of molecules are labeled with colored dots accordingly. The molecular structures of the six classes of agonists are grouped in boxes. The 17 newly found agonists are represented as A1-A17 in blue. The 25 previously reported agonists are represented as B1-B25 in black. The agonist isoeugenol is B3 and p-isobutylphenol is A1. In all cases the corresponding micromolar EC 50 values are indicated in brackets. Names of A- and B-compounds are listed in Supplementary Tables and

Article Snippet: The PH4 models were built according to the results obtained from both IFP analysis and molecular dynamics simulations.

Techniques: Labeling