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7 results for “conformational variability”
Synthetic cryo electron subtomograms containing biomolecular complexes with continuous conformational variability, used for validating TomoFlow method
<p>Two datasets used for validating TomoFlow method, an optical-flow based approach for analyzing continuous conformational variability of biomolecular complexes in cryo electron subtomograms. The TomoFlow method and the methods used to synthesize the two test datasets have been fully described in the following article: "M. Harastani, M. Eltsov, A. Leforestier, S. Jonic, TomoFlow: Analysis of continuous conformational variability of macromolecules in cryogenic subtomograms based on 3D dense optical flow, Journal of Molecular Biology (2021), doi: https://doi.org/10.1016/j.jmb.2021.167381". Additionally, this article describes a test of TomoFlow using one experimental cryo electron tomography dataset (available in EMPIAR and EMDB databases under the accession codes EMPIAR-10679 and EMD-12699). </p>
Prediction of Conformational Variability for RRM proteins in inter3m data base
<p>Predictions for protein Conformational Variability for the entries in InteR3M (<a href="https://inter3mdb.loria.fr/">https://inter3mdb.loria.fr/</a>), performed with the software ConforMine (in preparation).</p>
Synthetic cryo electron microscopy single particle images containing biomolecular complexes with continuous conformational variability used for validating DeepHEMNMA method and validation results
<p>This archive contains a synthetic dataset used for validating DeepHEMNMA method and the validation results. DeepHEMNMA is a deep learning extension of HEMNMA approach for analyzing continuous conformational variability of biomolecular complexes in cryo electron (cryo-EM) microscopy single particle images. We provide a training set of 20,000 images and an inference set of 50,000 images. The training images were used (1) to estimate the conformational and rigid-body parameters with HEMNMA and (2) to train the neural network using the parameters previously estimated with HEMNMA (the file with the HEMNMA-estimated parameters is provided). The inference images were used to infer the parameters with the trained neural network. Also, we provide (1) the input PDB structure, its normal modes, and the conformational and rigid-body parameters used to synthesize the 20,000 training images (ground-truth parameters) and (2) the conformational and rigid-body parameters inferred from the set of 50,000 inference images.</p> <p>The DeepHEMNMA method and the method for synthesizing images have been fully described in the following article: "Hamitouche I and Jonic S (2022), DeepHEMNMA: ResNet-based hybrid analysis of continuous conformational heterogeneity in cryo-EM single particle images. Front Mol Biosci 9, 965645. <a href="https://doi.org/10.3389/fmolb.2022.965645">https://doi.org/10.3389/fmolb.2022.965645</a> (in press)". Additionally, this article describes a test of DeepHEMNMA using one experimental cryo-EM dataset (available in EMPIAR database under the accession code EMPIAR-10016). </p>
Synthetic dataset used for validating MDSPACE method for analyzing continuous conformational variability of biomolecules in cryo-EM single particle images
<p>Synthetic dataset used for validating MDSPACE method for analyzing continuous conformational variability of biomolecules in cryo-EM single particle images. A README file with the contents of the dataset is included. </p>
Rereferenced Chemical Shift files used to select dihedrals for the 5 secondary structure conformations used to calculate Conformational Variability (ConVa).
<p>These Chemical Shifts in this repository were processed with ShiftCrypt (1) and then processed as described in the manuscript. </p> <p> </p> <p>1- Gabriele Orlando, Daniele Raimondi, Luciano Porto Kagami, Wim F Vranken, ShiftCrypt: a web server to understand and biophysically align proteins through their NMR chemical shift values, <em>Nucleic Acids Research</em>, Volume 48, Issue W1, 02 July 2020, Pages W36–W40, <a href="https://doi.org/10.1093/nar/gkaa391">https://doi.org/10.1093/nar/gkaa391</a></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>
Multiresolution molecular dynamics simulations reveal the interplay between conformational variability and functional interactions in membrane-bound cytochrome 2B4
<p>This dataset contains coordinate files, scripts and input parameters to run and analyse molecular dynamics simulations of membrane bound CYP 2B4.</p> <p> </p> <p>The associated study is described in: </p> <p>data - containes input data & last simulation frames for all of the described MD simulations and analyses described in the manuscript</p> <p>Structures_used_for_figures - contains .pdb and .pse files of all protein structures depicted in the manuscript</p>
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