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68 results for “conformational dynamics”
Interaction of the inhibitory peptides ShK and HmK with the voltage-gated potassium channel KV1.3: Role of conformational dynamics
<p><strong>ABSTRACT: </strong>Peptide toxins that adopt the ShK fold can inhibit the voltage-gated potassium channel K<sub>V</sub>1.3 with IC<sub>50</sub> values in the pM range, and are therefore potential leads for drugs targeting autoimmune and neuroinflammatory diseases. NMR relaxation measurements and pressure-dependent NMR have shown that, despite being cross-linked by disulfide bonds, ShK itself is flexible in solution. This flexibility affects the local structure around the pharmacophore for K<sub>V</sub>1.3 channel blockade and, in particular, the relative orientation of the key Lys and Tyr side chains (Lys22 and Tyr23 in ShK), and has implications for the design of K<sub>V</sub>1.3 inhibitors. In this study, we have performed molecular dynamics (MD) simulations on ShK and a close homolog, HmK, in order to probe the conformational space occupied by the Lys and Tyr residues, and docked the different conformations with a recently determined cryo-EM structure of the K<sub>V</sub>1.3 channel. Although ShK and HmK have 60% sequence identity, their dynamic behaviors are quite different, with ShK sampling a broad range of conformations over the course of a 5 μs MD simulation, while HmK is relatively rigid. We also investigated the importance of conformational dynamics, in particular the distance between the side chains of the key dyad Lys22 and Tyr23, for binding to K<sub>V</sub>1.3. Although these peptides have quite different dynamics, the dyad in both adopts a similar configuration upon binding, revealing a conformational selection upon binding to K<sub>V</sub>1.3 in the case of ShK. Intriguingly, the more flexible peptide, ShK, binds with nearly 300-fold higher affinity than HmK.</p>
Simulations of PKA RIα Homodimer Reveal cAMP-coupled Conformational Dynamics of Each Protomer and the Dimer Interface with Functional Implications
<p>Protein kinase A (PKA) is a ubiquitous cAMP-dependent enzyme in mammalian tissues. The inactive PKA holoenzyme disassociates into a homodimer of regulatory (R) subunits and two active catalytic (C) subunits upon cAMP binding to two tandem domains (termed <span>CBD-A</span> <span>and</span> <span>CBD</span>-<span>B</span>) in R subunits. The release of cAMP facilitates reassociation of R and C subunits<span>,</span> resetting PKA to its basal state. The cAMP-mediated structural changes in the activation-termination cycle <span>remain</span> <span>partially</span> <span>understood</span>. The multimeric states of PKA complicate the issue and are particularly <span>less</span> <span>studied</span>. Therefore, we computationally investigate the conformational dynamics of PKA <span>RI</span>a homodimer in different cAMP-bound states. The absence of cAMP in two CBDs affect differently the <span>conformational</span> <span>dynamics</span> of protomers. Moreover, <span>such</span> disparate <span>responses</span> are extended to the dimer interface <span>constituted</span> <span>by</span> <span>the</span> <span>N</span>-<span>terminal</span> <span>helical</span> <span>sub-domains</span> termed N3A motifs. T<span>he removal of cAMP from CBD-A induces large-scale structure</span> changes <span>of individual </span>R subunits <span>towards the holoenzyme state,</span> consist with previous simulations of a single R subunit. <span>Meanwhile</span> <span>it</span> <span>keeps the structural heterogeneity of the </span>N3A-N3A' <span>dimer interface observed in the fully bound state.</span> By contrast, the removal of cAMP from CBD-B does not affect <span>individual </span>R subunits but alters the conformational space of the N3A-N3A' dimer interface. The cAMP-coupled s<span>tructural</span> <span>changes</span> <span>of</span> <span>each</span> <span>protomer</span> <span>and</span> conserved conformational space of <span>the</span> N3A-N3A' <span>dimer</span> <span>interface</span> are essential for t<span>he</span> <span>transition</span> <span>between</span> the fully cAMP-bound R<sub>2</sub> homodimer and the R<sub>2</sub>C<sub>2</sub> holoenzyme as suggested by their crystal structures. Our work provides structural insights into <span>the</span> regulatory mechanism of cAMP in PKA signaling. </p>
Phosphorylation regulated conformational diversity and topological dynamics of an intrinsically disordered nuclear receptor
<p>Molecular dynamics simulations of AF1c region of human glucocorticoid receptor and its phosphovariants as described in the below paper: </p> <p><strong>Phosphorylation regulated conformational diversity and topological dynamics of an intrinsically disordered nuclear receptor</strong></p> <p>Vasily Akulov, Alba Jiménez Panizo, Eva Estébanez-Perpiñá, John van Noort, Alireza Mashaghi</p> <p> </p> <p>The data related to this project has been deposited in two repositories. This repository contains the first part of the data; the second part can be found at the DOI: 10.5281/zenodo.13822438</p>
Project files provided as supporting information to the manuscript "How Communication Pathways Bridge Local and Global Conformations in an IgG4 Antibody: a Molecular Dynamics Study"
<p>June 23, 2021</p> <p>Thomas Tarenzi, Marta Rigoli and Raffaello Potestio</p> <p>==================================</p> <p>The dataset contains the following folders:</p> <p>- contact_area_binding_site: files with the computed surface area, used for the calculation of the contact area between PD-1 and the antibody Fab (Fig. S41).</p> <p>- hbonds_ab-pd1: number of hydrogen bonds between the antigen and the antibody, for each holo cluster (Fig. S41).</p> <p>- mutual_information: matrices with the computed mutual information, for each pair of residues (Fig. S34, S35, S46). The folder contains also the generalized correlation coefficients (Fig. S43) and the correlation scores (Fig. 4, S44, S45), computed from the mutual informations.</p> <p>- networks: communities - for each cluster, each residue is assigned to a community within the interaction network (Fig. S32, S33). betweenness - the values of edge betweenness for each cluster (S30, S31).</p> <p>- output_clustering: each frame of the apo and holo simulations is assigned a cluster index, on the basis of the structural similarity (Fig. S4).</p> <p>- PAD: per-residue values of PAD parameter, for apo and holo systems (Fig. 3, S39).</p> <p>- PCA: principal component analysis for each conformational cluster (Section S2.1).</p> <p>- representative_structures: representative structures for each conformational cluster (Fig. 2).</p> <p>- r_gyr_antibody: radii of gyration of the antibody, for each cluster (Fig. 2).</p> <p>- r_gyr_hinge: radii of gyration of the sole hinge segment, for each cluster (Fig. S36).</p> <p>- RMSD_antibody: distributions of the root-mean-square deviation of the antibody, for each cluster (Fig. S5). </p> <p>- RMSD_antigen: root-mean-square deviation of the antigen PD-1, for each cluster (Fig. S42).</p> <p>- RMSD_binding_site: distributions of the root-mean-square deviation of the residues belonging to the paratope, for each cluster (Fig. S40, S47).</p> <p>- RMSD_matrix: root-mean-square deviation between structures belonging to different pairs of clusters (Fig. S9).</p> <p>- rmsf_antigen: root-mean-square fluctuation of the antigen PD-1, for each cluster (Fig. S42).</p> <p>- rmsf_hinge: difference between the total root-mean-square fluctuations of the two hinge segments, for each cluster (Fig. S38).</p> <p>- salt_bridge: distribution of distances between residues R979 and D1377 (Fig. 4).</p> <p>- sasa_domains: contact area between Fab and Fc antibody domains, for each cluster (Fig. S8).</p> <p>- sasa_hinge: solvent accessible surface area of each hinge segment, for each cluster (Fig. S37).</p>
ConforMine Molecular Dynamics Data: Conformational Variability, Secondary Structure Propensities and Molecular Dynamics Simulations
<pre>This dataset contains all the data used to calculate Conformational Variability (ConVa) and Conformational Propensities as well as to train ConforMine. Each directory one level below this document contains another readme for further explanation on the contained data. The following information can be found in this dataset: </pre> <ul> <li>ConforMine_MD_training_sequences.fasta: FASTA file with the amino acid sequences of all used proteins.</li> <li>simulations (directory): Contains all the raw data derived from the MD simulations.</li> <li>ConforMine_training_MD_dihedrals (directory): Contains .xvg files with the dihedral angles of each amino acid at each step of the MD simulation.</li> <li>ConforMine_training_data_conformational_variability (directory): Contains the Conformational Variability values for all amino acids. Each file contains all ConVa values for a whole protein. The data is provided in .csv and .npy format.</li> <li>ConforMine_training_data_conformational_propensities (directory): Contains the Conformational Propensities values for all amino acids. Each file contains all propensities for a whole protein. The data is provided in .csv and .npy format.</li> </ul>
Regulation of T7 gp2.5 Binding Dynamics by its C-terminal tail, Template Conformation and Sequence
<p>Bacteriophage T7 single-stranded DNA-binding protein (gp2.5) binds to and protects transiently exposed regions of single-stranded DNA (ssDNA) while dynamically interacting with other proteins of the replication complex. We directly visualize fluorescently labelled T7 gp2.5 binding to ssDNA at the single-molecule level. Upon binding, T7 gp2.5 reduces the contour length of ssDNA by stacking nucleotides in a force-dependent manner, suggesting T7 gp2.5 suppresses the formation of secondary structure. Next, we investigate the binding dynamics of T7 gp2.5 and a deletion mutant lacking 21 Cterminal residues (gp2.5-Δ21C) under various template tensions. Our results show that the base sequence of the DNA molecule, ssDNA conformation induced by template tension, and the acidic terminal domain from T7 gp2.5 significantly impact on the DNA binding parameters of T7 gp2.5. Moreover, we uncover a unique template-catalyzed recycling behaviour of T7 gp2.5, resulting in an apparent cooperative binding to ssDNA, facilitating efficient spatial redistribution of T7 gp2.5 during the synthesis of successive Okazaki fragments. Overall, our findings reveal an efficient binding mechanism that prevents the formation of secondary structures by enabling T7 gp2.5 to rapidly rebind to nearby exposed ssDNA regions, during lagging strand DNA synthesis.</p>
Regulation of T7 gp2.5 Binding Dynamics by its C-terminal tail, Template Conformation and Sequence
<p><sup>* </sup>To whom correspondence may be addressed: g.j.l.wuite@vu.nl<br> <sup># </sup>The first three authors should be regarded as joint first authors</p> <p><strong>Abstract</strong><br> Bacteriophage T7 single-stranded DNA-binding protein (gp2.5) binds to and protects transiently exposed regions of single-stranded DNA (ssDNA) while dynamically interacting with other proteins of the replication complex. We directly visualize fluorescently labelled T7 gp2.5 binding to ssDNA at the single-molecule level. Upon binding, T7 gp2.5 reduces the contour length of ssDNA by stacking nucleotides in a force-dependent manner, suggesting T7 gp2.5 suppresses the formation of secondary structure. Next, we investigate the binding dynamics of T7 gp2.5 and a deletion mutant lacking 21 C-terminal residues (gp2.5-Δ21C) under various template tensions. Our results show that the base sequence of the DNA molecule, ssDNA conformation induced by template tension, and the acidic terminal domain from T7 gp2.5 significantly impact on the DNA binding parameters of T7 gp2.5. Moreover, we uncover a unique template-catalyzed recycling behaviour of T7 gp2.5, resulting in an apparent cooperative binding to ssDNA, facilitating efficient spatial redistribution of T7 gp2.5 during the synthesis of successive Okazaki fragments. Overall, our findings reveal an efficient binding mechanism that prevents the formation of secondary structures by enabling T7 gp2.5 to rapidly rebind to nearby exposed ssDNA regions, during lagging strand DNA synthesis.</p> <p><strong>Data Set Description</strong><br> This data set contains the raw data and analysis code for the single-molecule study of the binding dynamics of T7 gp2.5 and its interactions with single-stranded DNA (ssDNA). The experiments utilized a custom-built setup combining dual optical trapping, confocal microscopy, and microfluidics.</p> <p><strong>Methodology</strong><br> The single-molecule experiments were performed at room temperature in a 5-channel microfluidic flow cell using a custom-built experimental setup that combines dual optical trapping, confocal microscopy, and microfluidics for single-molecule assays. Data analysis was conducted using Python, Origin, and MATLAB. Detailed methodology and instrument specifications are provided in the associated publication.</p> <p><strong>Data Set Structure</strong><br> - `Raw_Data/`: Contains raw data files in TDMS format.<br> - `Data_Analysis_Code/`: Contains the code used for data analysis and figure generation.</p> <p><strong>File Formats</strong><br> - Raw data files are in TDMS format.</p> <p><strong>Usage Notes</strong><br> Along with the raw data, we also provided the analysis code which generates the figures. Users should be aware of the required citations, license terms, and ethical considerations when using this data set.</p> <p><strong>Acknowledgments</strong><br> This work was financially supported by: ‘Crowd management: The physics of genome processing in complex environments’ of Stichting voor Fundamenteel Onderzoek der Materie, China Scholarship Council (funding No. 201704910912) and the European Union H2020 Marie-Sklowdowska Curie International Training Network AntiHelix, Grant Agreement n. 859853.</p> <p><strong>Author Contributions</strong><br> J.C-D., L X., M.T.J.H., and G.J.L.W. conceptualized the research; J.C-D., and L.X. collected data; I.H. built the combined optical trapping and confocal microscope instrument and developed the MATLAB code for the analysis of kymograph data; S.A.S. and A.v.O. provided purified wild type T7 gp2.5 and tested their biochemical activity; S-J L. provided purified gp2.5-Δ21C and tested their biochemical activity; L X. J.C-D., and M.T.J.H. analyzed the data; J.C-D., L X., M.T.J.H., and G.J.L.W. wrote the manuscript; G.J.L.W. supervised the project; the manuscript is read, revised and confirmed by all the<br> </p>
Data from: Conformational dynamics in TRPV1 channels reported by an encoded coumarin amino acid
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Data from: Conformational dynamics and asymmetry in multimodal inhibition of membrane-bound pyrophosphatases
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Interaction between cytochrome c and DNA: conformation, peroxidase activity and molecular dynamics simulation
A mixed system of cytochrome c (Cyt c, a typical hemoprotein) and DNA was constructed and the interaction between Cyt c and DNA were analyzed by experiment and molecular dynamics (MD) simulation methods, respectively. On the one hand, the experimental results showed: 1. The peroxidase activity of the mixed system was significantly enhanced relative to the Cyt c in 50 mM phosphate buffer solution at 25ºC. 2. UV-Vis spectra study found that, compared with the Cyt c solution, the absorbance of the Cyt c-DNA mixed system increased significantly at 280 nm, while the absorbance decreased at 405 nm, indicating that the overall structure of Cyt c in the mixed system became loose, and the structure around heme group became more compact. 3. Circular Dichroism (CD) studies showed that there was a weak interaction between DNA and Cyt c in the mixed system, which had little effects on the secondary structures of Cyt c. On the other hand, MD simulation results showed: 1. DNA and Cyt c were combined by hydrogen bonding and non-bonding interactions in the mixed system. 2. During the simulation process, N-Terminal α-Helix changed, and Lys13-Cys17 opened, exposing the active center (heme structure) of Cyt c, which may increase the binding of the mixed system to the substrate. 3. The bond length of Fe-N (N in His18 and Fe in heme group) became slightly shorter after equilibrium in the presence of DNA. 4. The Cyt c became loose after binding with DNA. 5. The total binding free energy between Cyt c and DNA was calculated to be -141.9 kJ/mol. The Cyt c-DNA system was in a relatively stable state from energy perspective. The results of the research on the structure and function of the Cyt c-DNA mixed system using experimental method and MD simulation method were consistent. The combination of experimental method and simulation method may provide useful research ideas and effective research methods for further studying the interaction mechanism between hemeprotein and DNA.
Tracking conformational transitions of the gonadotropin hormone receptors in a bilayer of (SDPC) poly-unsaturated lipids from all-atom molecular dynamics simulations.
<p>In the present study, we describe the results from a computational microscopy perspective (also known as molecular dynamics simulation) at the atomistic resolution for the two gonadotropin hormone receptors, the follicle-stimulant hormone receptor and the luteinizing/chorionic gonadotropin hormone receptor, which are essential for reproduction in humans.</p>
Exploring the ligand binding and conformational dynamics of the substrate binding domain 1 of the ABC transporter GlnPQ
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Ligand efficacy modulates conformational dynamics of the µ-opioid receptor
<p>This dataset includes DEER data from:</p> <p>Zhao and Elgeti et al. (2024)</p> <p>doi: 10.1038/s41586-024-07295-2</p>
Dynamic conformational changes of acid-sensing ion channels in different desensitizing conditions
<p>Peer-reviewed manuscript, supplemental file, and source data related to the article 'Dynamic conformational changes of acid-sensing ion channels in different desensitizing conditions'.</p>
In silico investigation of Alsin RLD conformational dynamics and phosphoinositides binding mechanism
<p>Raw data of the publication "In silico investigation of Alsin RLD conformational dynamics and phosphoinositides binding mechanism"</p>
Evolution of the conformational dynamics of the molecular chaperone Hsp90
<p>MD snapshots from: "Evolution of the conformational dynamics of the molecular chaperone Hsp90"</p> <p> </p>
Additional data "Client binding shifts the populations of dynamic Hsp90 conformations through an allosteric network"
<p>Additional data containing chemical shift perturbations and intensity changes of Hsp90 upon client binding, intermolecular PREs, and raw scattering data of Hsp90-client complexes.</p>
Supplementary FIles for Cholesterol Biases the Conformational Landscape of the Chemokine Receptor CCR3: A MAS SSNMR-Filtered Molecular Dynamics Study
<p>This repository contains supplementary files for the initial submission of:</p> <p>Cholesterol Biases the Conformational Landscape of the Chemokine Receptor CCR3: A MAS SSNMR-Filtered Molecular Dynamics Study<br> Evan J. van Aalst, Corey J. McDonald, and Benjamin J. Wylie</p> <p>Files found in this repository include:<br> 1. Raw fids corresponding to the solid-state NMR spectra used in this work.<br> 2. The script, model structures, and predicted chemical shifts used in the COMPASS proof of concept.<br> 3. Input molecular dynamics files derived from CHARMM-GUI including all mdp files, initial model structure files, and the production script.<br> 4. Model structures per ns derived from MD trajectories with corresponding predicted chemical shift lists and associated experimental chemical shift lists.</p>
Interaction between cytochrome c and DNA: conformation, peroxidase activity and molecular dynamics simulation
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Exploring Conformational Landscapes and Binding Mechanisms of Convergent Evolition for the SARS-CoV-2 Spike Omicron Variant Complexes with the ACE2 Receptor Using AlphaFold2-Based Structural Ensembles and Molecular Dynamics Simulations
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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