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15 results for “protein secondary structure”
Protein secondary-structure description with a coarse-grained model: code and datasets in ActivePapers format
<p>This file contains the supplementary material for the publication</p> <p><em>Protein secondary-structure description with a coarse-grained model</em><br /> by Gerald R. Kneller and K. Hinsen<br /> http://dx.doi.org/10.1107/S1399004715007191<br /> Acta Cryst. (2015). D<strong>71</strong>, 1411-1422</p> <p><strong>Datasets in this file</strong></p> <p>1) ScrewFit and ScrewFrame parameters for ideal secondary-structure elements</p> <p> Scripts:<br /> /code/import_ideal_structures<br /> /code/analyze_ideal_structures</p> <p>1.1) The PDB files generated with Chimera</p> <p> /data/ideal_structures/3-10.pdb<br /> /data/ideal_structures/alpha.pdb<br /> /data/ideal_structures/beta-antiparallel.pdb<br /> /data/ideal_structures/beta-parallel.pdb<br /> /data/ideal_structures/pi.pdb</p> <p>1.2) The corresponding MOSAIC datasets</p> <p> /data/ideal_structures/3-10<br /> /data/ideal_structures/alpha<br /> /data/ideal_structures/beta-antiparallel<br /> /data/ideal_structures/beta-parallel<br /> /data/ideal_structures/pi</p> <p>1.3) The ScrewFit parameters</p> <p> /data/ideal_structures/screwfit/3-10<br /> /data/ideal_structures/screwfit/alpha<br /> /data/ideal_structures/screwfit/beta-antiparallel<br /> /data/ideal_structures/screwfit/beta-parallel<br /> /data/ideal_structures/screwfit/pi</p> <p>1.4) The ScrewFrame parameters</p> <p> /data/ideal_structures/screwframe/3-10<br /> /data/ideal_structures/screwframe/alpha<br /> /data/ideal_structures/screwframe/beta-antiparallel<br /> /data/ideal_structures/screwframe/beta-parallel<br /> /data/ideal_structures/screwframe/pi</p> <p><br /> 2) Statistics for ScrewFit and ScrewFrame parameters computed<br /> for the ASTRAL SCOPe subset with less than 40% sequence identity.</p> <p> Scripts:<br /> /code/astral_analysis<br /> /code/fit_rho_distributions<br /> /code/plot_histograms</p> <p>2.1) The ASTRAL database (link to published ActivePaper)</p> <p> /data/astral_2.04</p> <p>2.2) The histograms for the ScrewFit and ScrewFrame parameters<br /> for the all-alpha and all-beta subsets</p> <p> /data/histograms/astral_alpha/screwfit<br /> /data/histograms/astral_alpha/screwframe</p> <p> /data/histograms/astral_beta/screwfit<br /> /data/histograms/astral_beta/screwframe</p> <p>2.3) The Gaussians fitted to the peaks in the distributions for rho</p> <p> /data/fitted_rho_distributions/screwfit<br /> /data/fitted_rho_distributions/screwframe</p> <p>2.4) Plots</p> <p> /documentation/delta.pdf<br /> /documentation/delta_q.pdf<br /> /documentation/delta_r.pdf<br /> /documentation/p.pdf<br /> /documentation/rho-detail.pdf<br /> /documentation/rho.pdf<br /> /documentation/sigma.pdf<br /> /documentation/tau.pdf</p> <p><br /> 3) Comparison of secondary-structure identification between ScrewFrame<br /> and DSSP.</p> <p> Script:<br /> /code/compare_secondary_structure_assignments<br /> /code/plot_histograms</p> <p>3.1) The histograms of the lengths of secondary-structure elements</p> <p> /data/histograms/secondary_structure/length-alpha-dssp<br /> /data/histograms/secondary_structure/length-alpha-screwframe<br /> /data/histograms/secondary_structure/length-beta-dssp<br /> /data/histograms/secondary_structure/length-beta-screwframe</p> <p>3.2) The 2D histograms of the number of residues inside identified<br /> secondary-structure elements</p> <p> /data/histograms/secondary_structure/n-alpha<br /> /data/histograms/secondary_structure/n-beta</p> <p>3.3) The distribution of rho inside alpha helices</p> <p> /data/histograms/secondary_structure/rho-alpha-dssp</p> <p>3.3) Plots</p> <p> /documentation/lengths-alpha.pdf<br /> /documentation/lengths-beta.pdf<br /> /documentation/n-alpha.pdf<br /> /documentation/n-beta.pdf<br /> /documentation/rho-alpha-dssp.pdf</p> <p><br /> 4) Illustration for myoglobin and VADC-1</p> <p> Scripts:<br /> /code/import_myoglobin_vdac<br /> /code/analyze_myoglobin<br /> /code/analyze_vdac<br /> /code/perturbation_analysis</p> <p>4.1) Imported structures in MOSAIC format:<br /> PDB code 1A6G for myoglobin<br /> PDB code 2K4T for VDAC-1</p> <p> /data/myoglobin<br /> /data/VDAC-1</p> <p>4.2) Plots showing rho and delta</p> <p> /documentation/rho-myoglobin.pdf<br /> /documentation/delta-myoglobin.pdf</p> <p>4.3) Tube models for visualization with Chimera</p> <p> /documentation/myoglobin-tube.bld<br /> /documentation/VDAC-1-tube.bld</p> <p>4.4) Sensitivity to perturbations in the coordinates</p> <p> /documentation/rho-perturbed-myoglobin.pdf<br /> /documentation/delta-perturbed-VDAC-1.pdf<br /> /documentation/rho-perturbed-myoglobin.pdf<br /> /documentation/delta-perturbed-VDAC-1.pdf<br /> /documentation/myoglobin-perturbation.pdf<br /> /documentation/VDAC-1-perturbation.pdf</p> <p>5) Analysis of CA-only structures in the PDB</p> <p> Scripts:<br /> /code/ca_analysis<br /> /code/import_calpha_structures<br /> /code/plot_histograms</p> <p>5.1) Imported CA-only structures in MOSAIC format</p> <p> /data/pdb_ca_only_structures</p> <p>5.2) Histograms for ScrewFrame parameters</p> <p> /data/histograms/ca_only_structures</p> <p>5.3) Plots</p> <p> /documentation/delta_ca.pdf<br /> /documentation/delta_q_ca.pdf<br /> /documentation/delta_r_ca.pdf<br /> /documentation/p_ca.pdf<br /> /documentation/rho_ca.pdf<br /> /documentation/sigma_ca.pdf<br /> /documentation/tau_ca.pdf</p> <p> </p>
Figure 3. Distribution of Q3 values-Secondary Structure Prediction of Protein using Resilient Back Propagation Learning Algorithm
<p>The estimated accuracy for the α- helices (QH), β- strands (QE), C-coil states (QC), and three<br> state together (Q3) for the system is shown in Figure 3.</p>
Figure 2. PAM250 matrix for the encoded sequence-Secondary Structure Prediction of Protein using Resilient Back Propagation Learning Algorithm
<p>The PAM matrix (Dayhoff et al., 1978) describes the probability that original amino acid<br> will be replaced by another amino acid over a defined evolutionary interval. The unit of<br> evolutionary divergence is defined as the interval in which 1% of the amino acids have been<br> changed between two sequences. The work uses PAM250, which assumes the occurrence of 250-<br> point mutations per 100 amino acids.<br> So, for the given the protein sequence GIVEQCCASVCSLYQLENYCN, A will be replaced<br> by 1 -3 0 1 -3 -1 0 5 -2 -3 -4 -2 -3 -5 0 1 0 -7 -5 -1 as shown in Figure 2.</p>
Figure 1: Snapshot of the CB396 dataset-Secondary Structure Prediction of Protein using Resilient Back Propagation Learning AlgorithmSecondary Structure Prediction of Protein using Resilient Back Propagation Learning Algorithm
<p>The dataset used for this work is CB396. This dataset contains 396 non-redundant sequences<br> derived from the 3Dee database created by Cuff and Barton (Cuff & Barton, 1999). It contains 396<br> proteins with their respective secondary structure as shown in Figure 1.</p>
Nanoscale chemical characterization of secondary protein structure of F-Actin using mid-infrared photoinduced force microscopy (PiF-IR)
<p>Raw data for manuscript for special issue in Spectrochimica Acta related to ECSBM 2022</p> <p> </p>
Annotation and analysis of the secondary structure elements in the Cytochrome P450 protein family
<p>We collected all currently available structures for proteins in the Cytochrome P450 family and annotated their secondary structure elements using SecStrAnnotator software (https://webchem.ncbr.muni.cz/Wiki/SecStrAnnotator). We used 2nnjA as the template domain for the annotation. Based on these annotations, we analysed the occurrence, length distribution, amino acid sequence, and presence of structural irregularities (β-bulges, 3<sub>10</sub>-helices, π-helices) of each secondary structure element class. We also statistically compared the bacterial vs eukaryotic structures. For the secondary structure element classes with sufficient sequence conservation, the most conserved residue is annotated as the reference residue.</p> <p>Main files:</p> <ul> <li><strong>set_ALL.json</strong> - Set-ALL: list of 1012 protein domains belonging to the Cytochrome P450 family (CATH accession 1.10.630.10 + Pfam accession PF00067, accessed on 7 July 2020, one domain per PDB entry)</li> <li><strong>set_NR.json</strong> - Set-NR: non-redundant list of 183 domains (one domain per UniProt ID)</li> <li><strong>domain_lists_table.tsv</strong> - Overview of Set-ALL and Set-NR and separation into subsets Set-NR-Bact (bacterial), Set-NR-Euka (eukaryotic), Set-NR-Arch (archaeal), Set-NR-Viru (viral)</li> <li><strong>structures/template_2NNJ-template.sses.json</strong> - Manually prepared annotation template (domain 2nnjA)</li> <li><strong>structures/template_2NNJ.cif</strong> - Structure of the template domain (2nnjA)</li> <li><strong>annotations_with_reference_residues_ALL.json, annotations_with_reference_residues_ALL.tsv</strong> - Annotation of secondary structure elements for Set-ALL</li> <li><strong>annotations_with_reference_residues_NR.json, annotations_with_reference_residues_NR.tsv</strong> - Annotation of secondary structure elements for Set-NR</li> <li><strong>aligments_NR</strong> - Multiple sequence alignments for each SSE class (Set-NR)</li> <li><strong>logos_NR</strong> - Sequence logos for each SSE class (Set-NR)</li> <li><strong>plots</strong> - Plots of SSE occurrence, length distribution, contained helix types and beta-bulge occurrence (Set-NR), some plots show the comparison between Set-NR-Bact and Set-NR-Euka</li> <li><strong>statistical_tests.ods</strong> - Comparison of SSE occurrence between Set-NR-Bact and Set-NR-Euka by the test of equal proportions and the Fisher test, comparision of the SSE length by the Kolmogorov-Smirnov test and the two-sample Wilcoxon test</li> </ul>
Expansion of the RNAStructuromeDB to include secondary structural data spanning the human protein-coding transcriptome
<p>This dataset includes the -2, -1, and no filter z-score dot bracket files from ScanFold for all protein coding transcript isoforms.</p>
Global Analysis of the RNA-Protein Interaction and RNA Secondary Structure Landscapes Identifies Dynamic Changes During Mammalian Erythropoiesis (mRNA)
GEO Series GSE148421. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
The CGG triplet repeat binding protein 1 counteracts DNA secondary structure formation to prevent transcription-replication stress
GEO Series GSE244415. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing; Other.
Global analysis of the RNA-protein interaction and RNA secondary structure landscapes of the Arabidopsis nucleus
GEO Series GSE58974. Arabidopsis thaliana. 10 samples. Type: Expression profiling by high throughput sequencing; Other.
Global Analysis of the RNA-Protein Interaction and RNA Secondary Structure Landscapes Identifies Dynamic Changes During Mammalian Erythropoiesis
GEO Series GSE142242. Mus musculus. 24 samples. Type: Other.
Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction, 2014
<p>This contains the protein sequence and secondary structure dataset from <a href="https://proceedings.mlr.press/v32/zhou14.html"><strong>Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction</strong></a><strong>, ICML, 2014</strong></p> <p>This dataset was originally hosted at http://www.princeton.edu/~jzthree/datasets/ICML2014/. Since the original URL is no longer available and the dataset is still used by many, I moved the dataset here.</p>
RNA G-quadruplex secondary structure promotes alternative splicing via the RNA binding protein hnRNPF
GEO Series GSE107542. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Global analysis reveals large scale dynamics of protein-RNA interaction and RNA secondary structure characteristics of Physcomitrella patens ABA response (PIP-Seq)
GEO Series GSE148423. Physcomitrium patens. 24 samples. Type: Other.
Global analysis reveals large scale dynamics of protein-RNA interaction and RNA secondary structure characteristics of Physcomitrella patens ABA response (mRNA)
GEO Series GSE148422. Physcomitrium patens. 6 samples. Type: Expression profiling by high throughput sequencing.
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