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33 results for “Conformational analysis”
Figure 4. Canonical Variates Analysis head conformation diagram for 136 in Head geometric morphometrics of two Chagas disease vectors from Venezuela
Figure 4. Canonical Variates Analysis head conformation diagram for 136 Triatoma maculata specimens and thin-plate deformation grids. A. V instar–Adults. B. I instar–Adults. C. II instar–III instar.
Figure 3. Canonical Variates Analysis head conformation diagram for 140 in Head geometric morphometrics of two Chagas disease vectors from Venezuela
Figure 3. Canonical Variates Analysis head conformation diagram for 140 Rhodnius prolixus specimens and thin-plate deformation grids. A. V instar–Adults. B. I instar–Adults. C. II instar–III instar.
Data for: Analysis of Conformational Exchange Processes using Methyl-TROSY-Based Hahn Echo Measurements of Quadruple-Quantum Relaxation
<p>Raw experimental data used in associated publication. A full list of experiments is provided in the README.md file.</p>
Snapshots, frequency contact maps analysis, Poisson Boltzmann calculations, and data scripts for characterization of structural and energetic differences between conformations of the SARS-CoV-2 spike protein
<p><strong>Molecular dynamics simulation</strong> trajectories, which have been performed using the Amber ff14SB force field running with the Amber18 package at the NSF-funded (OAC-1826915, OAC-1828163) ELSA high performance computing cluster at The College of New Jersey. Simulation methodology and further details are described in [1] and [2]. For further details on the trajectories, please contact Joseph Baker (bakerj@tcnj.edu).</p> <p>The <strong>Poisson Boltzmann </strong>energy calculations have been achieved by using the input_files.tar.xz found here and solving the Poisson Boltzmann equation with pygbe. A more detailed example and tutorial can be found at [4]. For further details contact Horacio V Guzman.</p> <p><strong>The dataset contains </strong></p> <ul> <li><strong>A total of 30 snapshots of the three trajectories (10 snapshots each system = two per replica x 5 replicas/system):</strong></li> </ul> <ol> <li>SARS-CoV-2002 spike protein with three RBD in the down positions: "COV2-DDD/PDB/" .</li> <li>SARS-CoV-2002 spike protein with one RBD in the up and two RBD in the down positions: "COV2-UDD/PDB/".</li> <li>SARS-CoV-2002 spike protein with two RBD in the up and one RBD in the down positions: "COV2-DUU/PDB/".</li> </ol> <ul> <li><strong>Input files for Poisson-Boltzmann analysis</strong>:</li> </ul> <ol> <li>PoissonBoltzmann/input_files.tar.xz</li> </ol> <ul> <li><strong>Data for the frequency contact map and processing scripts</strong>:</li> </ul> <ol> <li>cov2-ddd.pdb, cov2-udd.pdb, cov2-duu.pdb reference PDB files.</li> <li>Contact maps [3] at "COV2-DDD/CONTACT_MAP/", "COV2-UDD/CONTACT_MAP/", "COV2-DUU/CONTACT_MAP/".</li> <li>frequency.lua: get frequency of contacts from a set of contacts map files.</li> <li>diff_frequency.lua: get differential frequency of contacts from a set of frequency files.</li> <li>Frequency of contacts listed in frequency.data files at "COV2-DDD/", "COV2-UDD/" and "COV2-DUU/" directories.</li> </ol> <p>Read the "INFO" files for further informations.</p> <p>This dataset and the code is part of a collaboration between:</p> <ul> <li>The Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland (supported by the National Science Centre, Poland, under grant No. 2017/26/D/NZ1/0046)</li> <li>Department of Chemistry, The College of New Jersey, New Jersey, United States (supported by National Science Foundation under grant numbers OAC-1826915 and OAC-1828163).</li> <li>Jozef Stefan Institute, Ljubljana, Slovenia (supported by the Slovenian Research Agency (Funding No. P1-0055)).</li> <li>School of engineering in bioinformatics, University of Talca, Talca, Chile.</li> </ul> <p>[1] Rodrigo A. Moreira, Mateusz Chwastyk, Joseph L. Baker, Horacio V Guzman, & Adolfo B. Poma. (2020). All-atom simulations snapshots and contact maps analysis scripts for SARS-CoV-2002 and SARS-CoV-2 spike proteins with and without ACE2 enzyme (Version 0.1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3817447</p> <p>[2] Chad W. Hopkins, Scott Le Grand, Ross C. Walker, and Adrian E. Roitberg. Long-Time-Step Molecular Dynamics through Hydrogen Mass Repartitioning. Journal of Chemical Theory and Computation 2015 11 (4), 1864-1874. http://doi.org/10.1021/ct5010406</p> <p>[3] Rodrigo A. Moreira, Mateusz Chwastyk, Joseph L. Baker, Horacio V Guzman, & Adolfo B. Poma. Quantitative determination of mechanical stability in the novel coronavirus spike protein. Nanoscale, 2020,12, 16409-16413. <a href="https://doi.org/10.1039/D0NR03969A">https://doi.org/10.1039/D0NR03969A</a></p> <p>[4] https://github.com/pyF4all</p>
Ligand-induced Conformational Selection Predicts the Selectivity of Cysteine Protease Inhibitors - Inputs and Analysis
<p>Supplementary data of "Ligand-induced Conformational Selection Predicts the Selectivity of Cysteine Protease Inhibitors" paper.</p> <p>This dataset consists of the parametrized ligand (covalent and noncovalent form of ICR, ICK, ICL, IKR) and complexes files, sample of input files used for Molecular dynamics simulations and analysis procedures, and the raw data of results. </p>
Simulation and analysis data set for apo-conformational kinetics and gated ligand binding to HIV-1 protease
<p>The data set provided here accompanies a study described in the manuscript:</p> <p>S. Kashif Sadiq, Abraham Muñiz Chicharro, Patrick Friedrich, Rebecca Wade, A multiscale approach for computing gated ligand binding from molecular dynamics and Brownian dynamics simulations. (2021) Preprint available: https://doi.org/10.1101/2021.06.22.449380</p> <p>This study combines molecular dynamics MD simulations and associated conformational analyses and Markov state models (MSMs) with Brownian dynamics (BD) simulations to compute conformation gated ligand association kinetics to HIV-1 protease.</p> <p>To download the data, go to a directory where you would like to download the files. Then for each of the provided tar files enter the following command:</p> <p>tar xvf $X.tar</p> <p>where $X is the name prefix of the corresponding tar file.</p> <p>The unpacked data set creates a ./data sub-directory which itself contains two further sub-directories: MD and BD. Please see README.txt files within these sub directories for further instructions on the software tools and scripts that have been provided therein for using and reproducing the data set. The MD README.txt is found within: data_MD_MSM_analysis.tar, the BD README.txt is found within: data_BD_examples.tar.</p> <p>Please note, the python Jupyter notebook and associated module for further analysis of the MSM from the pre-defined feature set calculated in the study as well as other analyses can also be found at:</p> <p>https://github.com/kashifsadiq/hiv1pr-msm/</p> <p>MD trajectory files are provided for further analysis but are not required to reproduce the MSM and conformational analyses reported in the study. To facilitate overview, MSM analysis has been stored in several object files. To exactly reproduce the reported MD/MSM analyses, untar only the 1) data_MD_MSM_analysis.tar and 2) data_MD_MSMobj.tar files and work through the python Jupyter notebook.</p> <p> </p>
Internal Normal Mode Analysis applied to RNA flexibility and conformational changes
<p>We investigated the capability of internal normal modes to reproduce RNA dynamics and predict observed RNA conformational changes, and, notably, those induced by the formation of RNA-protein and RNA-ligand complexes. Here, we extended our iNMA approach developed for proteins to study RNA molecules using a simplified rep- resentation of RNA structure and its potential energy. In this study, we considered three main data sets to investigate different aspects : i) one based on single-stranded RNA molecules for which all-atom MD simulations were computed; ii) one based on the available structures belonged to a specific Rfam family; iii) one based on the transition from unbound to bound RNA.</p> <p><strong>In each folder</strong></p> <p><em>modes.dat</em>: results obtained by iNMA (frequency and normal modes)</p> <p><em>das1.dat</em>: conversion from internal to cartesian normal modes</p> <p>Each file <em>name_enm.pdb</em> refers to a PDB structure with a CG representation (RNA three-bead model).</p> <p><strong>Dataset 1</strong>: d1.zip</p> <p>For the first dataset, we provide MD simulations converted into CG representation (RNA three-bead model), PCA analysis, the results obtained by iNMA for different values of distance cut-off <em>R</em><sub><em>c</em> </sub> and some scripts.</p> <p>Matlab and python scripts: </p> <p><em>analysis_pca.py</em>: to extract the different principal components</p> <p><em>analysis_PCA.m</em>: to compute overlap and cumative overlap in each folder</p> <p><em>analysis_complete_new.m</em>: to summarize the results</p> <p><strong>Dataset 2</strong>: d2.zip</p> <p>For this dataset, we provide the structure ensemble for Rfam family and the results obtained by iNMA for different values of distance cut-off <em>R</em><sub><em>c</em> </sub>and some scripts.</p> <p>PDB files:</p> <p><em>allensemble.pdb</em>: ensemble of PDB structures for a given Rfam family</p> <p><em>allensemble_enm.pdb</em>: ensemble of PDB structures for a given Rfam family converted to CG representation (RNA three-bead model)</p> <p><em>allensemble_enm_new.pdb</em>: ensemble of PDB structures for a given Rfam family with the same number of atoms for each model converted to CG representation (RNA three-bead model)</p> <p><em>model.pdb</em>: reference PDB structure</p> <p><em>model_enm.pdb</em>: reference PDB structure converted to CG representation (RNA three-bead model)</p> <p>Matlab script: </p> <p><em>pca_xray_anal.m</em>: PCA analysis, overlap, cumulative overlap, rmsip and plots</p> <p><strong>Dataset 3</strong>: d3.zip</p> <p>PDB structure:</p> <p><em>bound.pdb</em>: bound structure</p> <p><em>unbound.pdb</em>: unbound structure</p> <p><em>diff.dat</em>: difference between bound and unbound structure after superimposition </p> <p>RMSD<em>n </em>with n a number: the first column represents <span class="math-tex">\(\sqrt{\beta/2}\)</span></p> <p>Matlab script:</p> <p><em>rmsd_anal.m</em>: analysis best mode based on RMSD</p> <p><strong>Application to the CrPV-IRES</strong>: IRES.zip</p> <p>PDB structures:</p> <p> <em>IRES_cg.pdb</em>: Coarse-grain structure based on the PDB ID 5IT9</p> <p><em>b_end001_01_70.pdb</em>, <em>b_end001_01_80.pdb, b_end001_01_90.pdb</em>: Example of modified structures using the first lowest modes and different amplitudes <span class="math-tex">\(\beta\)</span></p> <p><em>b_end002_03_50.pdb</em>, <em>b_end002_03_60.pdb, b_end002_03_70.pdb</em>: Example of modified structures using the third lowest modes and different amplitudes <span class="math-tex">\(\beta\)</span></p>
Quantum chemical investigation of the predominant conformation of the antibiotic azithromycin in water and DMSO solutions: an integrated thermodynamic and NMR analysis
<p><span>Azithromycin (AZM) is a macrolide-type antibiotic used to prevent and treat serious infection</span><span>s (mycobacteria or MAC) that significantly inhibit bacterial growth. Knowledge of the predominant conformation in solution is of fundamental importance for advancing our understanding of the intermolecular interactions of AZM with biological targets. We report an extensive density functional theory (DFT) study of plausible AZM structures in solution considering implicit and explicit solvent effects. The best match between the experimental and theoretical nuclear magnetic resonance (NMR) profiles was used to assign the preferred conformer in solution, which was supported by the thermodynamic analysis. Among the 15 distinct AZM structures, conformer M14, having a short intramolecular C6-OH…N H-bond, is predicted to be dominant in water and DMSO solutions. The results indicated that the X-ray structure backbone is mostly conserved in solution, showing that large flexible molecules with several possible conformations may assume a preferential spatial orientation in solution, which is the molecular structure that ultimately interacts with biological targets.</span></p>
Quantum chemical investigation of the predominant conformation of the antibiotic azithromycin in water and DMSO solutions: an integrated thermodynamic and NMR analysis
Open the record for dataset details and reuse information.
Conformity of E-Learning for Teaching and Learning in Ethiopian Higher Education: Analysis on User-Friendliness of E-Resources
<p>The study investigated the practicability of e-learning for teaching and learning in Ethiopian higher education in terms of availability, clarity, accessibility in terms of accommodation and economy. The research looked into the roles of e-provisions based on the country’s Information and Communication Technology (ICT) in education policy guidelines as benchmark. Descriptive survey research design was used in the research since the study focused on indicating status than in-depth institution-based analysis of technology-use in education. Two higher institutions were selected for their relative proximity and viability for data collection. Data for the research were collected from 150 students, 4 technical support renderers and 30 teachers. Instruments of data collection were binary mode questionnaires and semi-structured interviews. Findings indicated shortage in a purpose-orientation, weak cross-institutional interchange and low mainstreaming of e-resources for course-provision. Though initiatives were high to use e-resources across lessons, shortage in internet access and prevailing digital divides were common barriers. Selective use of e-learning was witnessed on the part of technical support providers. multiplier effects in sharing experiences were not practiced among the teachers. Purpose-conformity was met on highly individualized bases. inter-institutional experiential exchange was insufficient though there was high emphasis on supporting selected instructional strings.</p>
Atomistic Predictions and Network-Based Allosteric Analysis of Conformational Ensembles for the State-Switching ABL Kinase Mutants Using Combination of Alanine Sequence Scanning and Shallow Subsampling in AlphaFold2
Open the record for dataset details and reuse information.
Conformity of Conjunctival Hyperemia Assessment in Soft Contact Lens Wearers Using Image J Analysis and Efron Degree System
ClinicalTrials.gov study NCT07290829. IPD Sharing: NO. Countries: 1. Publications: 0.
Obtention of viable cell suspensions from breast cancer tumor biopsies for 3D chromatin conformation and single cell transcriptome analysis [scRNA-seq 2]
GEO Series GSE270362. Homo sapiens. 1 samples. Type: Expression profiling by high throughput sequencing.
Twins: A deep learning method for replicate-based conformation contact map analysis.
GEO Series GSE233377. Mus musculus. 0 samples. Type: Third-party reanalysis; Other.
Chromosome conformation capture-on-chip analysis of long-range cis interactions of the SOX9 promoter
GEO Series GSE50449. Homo sapiens. 6 samples. Type: Other.
Multiplexed analysis of chromosome conformation at vastly improved sensitivity
GEO Series GSE67959. Mus musculus. 14 samples. Type: Other.
Genomic analysis of chromatin conformation and DNA damage distribution in ovarian cancer cell lines derived following acquired resistance to chemotherapy [ATAC-Seq]
GEO Series GSE149145. Homo sapiens. 18 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Genomic analysis of chromatin conformation and DNA damage distribution in ovarian cancer cell lines derived following acquired resistance to chemotherapy [Exo-Seq]
GEO Series GSE147645. Homo sapiens. 15 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Genomic analysis of chromatin conformation and DNA damage distribution in ovarian cancer cell lines derived following acquired resistance to chemotherapy [RNA-Seq]
GEO Series GSE149146. Homo sapiens. 18 samples. Type: Expression profiling by high throughput sequencing.
Next Generation Sequencing Facilitates Quantitative Analysis of chromatin conformational interaction between MYC and PVT1 in MCF7
GEO Series GSE106153. Homo sapiens. 16 samples. Type: Other.
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