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30 results for “protein chain”
Ligand binding remodels protein side chain conformational heterogeneity
<p>While protein conformational heterogeneity plays an important role in many aspects of biological function, including ligand binding, its impact has been difficult to quantify. Macromolecular X-ray diffraction is commonly interpreted with a static structure, but it can provide information on both the anharmonic and harmonic contributions to conformational heterogeneity. Here, through multiconformer modeling of time- and space-averaged electron density, we measure conformational heterogeneity of 743 stringently matched pairs of crystallographic datasets that reflect unbound/apo and ligand-bound/holo states. When comparing the conformational heterogeneity of side chains, we observe that when binding site residues become more rigid upon ligand binding, distant residues tend to become more flexible, especially in non-solvent exposed regions. Among ligand properties, we observe increased protein flexibility as the number of hydrogen bonds decrease and relative hydrophobicity increases. Across a series of 13 inhibitor bound structures of CDK2, we find that conformational heterogeneity is correlated with inhibitor features and identify how conformational changes propagate differences in conformational heterogeneity away from the binding site. Collectively, our findings agree with models emerging from NMR studies suggesting that residual side chain entropy can modulate affinity and point to the need to integrate both static conformational changes and conformational heterogeneity in models of ligand binding.</p>
Elongation of very long chain fatty acids protein 7 (ELOVL7); A Target Enabling Package
<p>The long-chain fatty acid elongases (ELOVL) catalyse the first rate-limiting step in the two carbon elongation of the acyl chains of fatty acids (FAs) greater than 12 carbons in length. Defects in these ELOVL elongases cause severe genetic diseases, such as Stargardt disease-3 and several ataxias, and knockout studies suggest roles in insulin resistance and hepatic steatosis. This TEP provides the first structural information for this family of enzymes which, coupled with mutagenesis and biophysical studies, demonstrates how substrates and products bind within the active site.</p>
Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks
<p>Residue-residue distance information is useful for predicting tertiary structures of protein monomers or quaternary structures of protein complexes. Many deep learning methods have been developed to predict intra-chain residue-residue distances of monomers accurately, but few methods can accurately predict inter-chain residue-residue distances of complexes. We develop a deep learning method CDPred (i.e., Complex Distance Prediction) based on the 2D attention-powered residual network to address the gap. Tested on two homodimer datasets, CDPred achieves the precision of 60.94% and 42.93% for top L/5 inter-chain contact predictions (L: length of the monomer in homodimer), respectively, substantially higher than DeepHomo’s 37.40% and 23.08% and GLINTER’s 48.09% and 36.74%. Tested on the two heterodimer datasets, the top Ls/5 inter-chain contact prediction precision (Ls: length of the shorter monomer in heterodimer) of CDPred is 47.59% and 22.87% respectively, surpassing GLINTER’s 23.24% and 13.49%. Moreover, the prediction of CDPred is complementary with that of AlphaFold2-multimer.</p>
Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks
<p>Residue-residue distance information is useful for predicting tertiary structures of protein monomers or quaternary structures of protein complexes. Many deep learning methods have been developed to predict intra-chain residue-residue distances of monomers accurately, but few methods can accurately predict inter-chain residue-residue distances of complexes. We develop a deep learning method CDPred (i.e., Complex Distance Prediction) based on the 2D attention-powered residual network to address the gap. Tested on two homodimer datasets, CDPred achieves the precision of 60.94% and 42.93% for top L/5 inter-chain contact predictions (L: length of the monomer in homodimer), respectively, substantially higher than DeepHomo’s 37.40% and 23.08% and GLINTER’s 48.09% and 36.74%. Tested on the two heterodimer datasets, the top Ls/5 inter-chain contact prediction precision (Ls: length of the shorter monomer in heterodimer) of CDPred is 47.59% and 22.87% respectively, surpassing GLINTER’s 23.24% and 13.49%. Moreover, the prediction of CDPred is complementary with that of AlphaFold2-multimer.</p>
A structural database of chain-chain and domain-domain interfaces of proteins
<p>Library of protein-protein and domain-domain interfaces from the protein data bank. The data also contains the structural clusters of protein-protein and domain-domain interfaces.</p>
A combined NMR and Deep Neural Network approach for enhancing the spectral resolution of aromatic side chains in proteins
<p><span><span>Nuclear magnetic resonance (NMR) spectroscopy has become an important technique in structural biology for characterising the </span><span>structure, </span><span>dynamics</span> <span>and interactions </span><span>of </span><span>macromolecules. Wh</span><span>ile</span><span> a plethora of </span><span>NMR </span><span>methods are </span><span>now </span><span>available to inform on backbone and methyl-bearing </span><span>side-chains</span><span> of proteins, </span><span>a </span><span>characterisation</span><span> of</span><span> aromatic side chains is more challenging and often require</span><span>s</span><span> specific labelling or </span></span><span><span>13</span></span><span><span>C-detection. Here we </span><span>present</span><span> a deep neural network (DNN)</span> <span>named FID-Net-2</span><span>, which transforms NMR spectra recorded on simple uniformly </span></span><span><span>13</span></span><span><span>C labelled samples to yield high-quality </span></span><span><span>1</span></span><span><span>H</span><span>-</span></span><span><span>13</span></span><span><span>C correlation spectra of the aromatic side chains. </span><span>Key to the success of the DNN is the design of a </span><span>complementary</span><span> set of</span> <span>NMR experiment</span><span>s</span><span> that </span><span>produce</span> <span>spectra with </span><span>unique </span><span>features</span><span> to aid the</span> <span>DNN </span><span>prod</span><span>uce</span><span> high-resolution aromatic </span></span><span><span>1</span></span><span><span>H-</span></span><span><span>13</span></span><span><span>C correlation spectra with </span><span>accurate</span><span> intensities. </span><span>The </span><span>reconstructed spectra can be used for quantitative </span><span>purposes as FID-Net-2</span><span> predicts uncertainties </span><span>in </span><span>the </span><span>resulting</span> <span>spectra</span><span>. </span><span>We </span><span>have </span><span>validated</span> <span>the new </span><span>methodology</span><span> experimentally</span> <span>on protein</span><span> samples</span><span> ranging from 7 to 40 </span><span>kDa</span><span> in size. We </span><span>demonstrate</span><span> that the method can</span> <span>accurately reconstruct</span> <span>high resolution </span><span>two-dimensional </span><span>aromatic </span></span><span><span>1</span></span><span><span>H-</span></span><span><span>13</span></span><span><span>C correlation </span><span>maps</span><span>,</span> <span>high resolution </span><span>three-dimensional aromatic</span><span>-</span><span>methyl NOESY spectra to </span><span>facilitate</span><span> aromatic </span></span><span><span>1</span></span><span><span>H-</span></span><span><span>13</span></span><span><span>C assignments</span><span>,</span><span> and </span><span>that the intensities of peaks from the reconstructed</span> <span>aromatic </span></span><span><span>1</span></span><span><span>H-</span></span><span><span>13</span></span><span><span>C correlation maps </span><span>can be used to </span><span>quantitatively </span><span>characterise</span><span> the kinetics of protein folding</span><span>. </span><span>More generally, w</span><span>e believe that this strategy of</span><span> devising new</span><span> NMR</span><span> experiments</span><span> specifically</span> <span>for analysis</span> <span>using customised </span><span>DNN</span><span>s </span><span>represents</span><span> a </span><span>substantial</span><span> advance </span><span>that</span> <span>will</span><span> have a major impact on the study of molecules </span><span>using NMR </span><span>in the years to come.</span></span><span> </span></p>
Supplements for "Solvent Accessibility Promotes Rotamer Errors During Protein Modelling with Major Side-Chain Prediction Programs"
<p><strong>Supplements for "Solvent Accessibility Promotes Rotamer Errors During Protein Modelling with Major Side-Chain Prediction Programs"</strong></p> <p>This supplement includes the following files:</p> <ul> <li>Main_v02.R --- Script in R language to process files (in "PDBs.zip") and produce the dataset ("Dataset_v02.csv")</li> <li>PDBs.zip --- PDB files include filtered structures processed by three programs</li> <li>Dataset_v02.csv --- final filtered version of dataset produced in R language (by "Main_v02.R"). </li> <li>Dataset key.txt --- key to column names in Dataset_v02.csv </li> </ul> <p>The article featuring this dataset is published in:</p> <p>Journal: Journal of Chemical Information and Modeling<br>Title: "Solvent Accessibility Promotes Rotamer Errors During Protein Modelling with Major Side-Chain Prediction Programs"<br>Author(s): Hameduh, Tareq; Mokry, Michal ; Miller, Andrew ; Heger, Zbynek; Haddad, Yazan</p> <p><a href="https://emea01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fpubs.acs.org%2Fdoi%2F10.1021%2Facs.jcim.3c00134&data=05%7C01%7C%7C4876a5c33e5f4bd8daf008db7e55a28d%7C84df9e7fe9f640afb435aaaaaaaaaaaa%7C1%7C0%7C638242678496577396%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=NE%2F0dYvc4m5%2B8T0YQtqxY21yk1pA9ZpiADqzx3vEJ4Q%3D&reserved=0">https://pubs.acs.org/doi/10.1021/acs.jcim.3c00134</a></p> <p> </p>
Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks
<p>Benchmark data sets of CDPred as described in</p> <p><strong>Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks</strong></p> <p>Zhiye Guo<sup>1</sup>, Jian Liu<sup>1</sup>, Jeffrey Skolnick<sup>2</sup>, Jianlin Cheng<sup>1*</sup></p> <p><sup>1 </sup>Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211</p> <p><sup>2 </sup>School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332-2000</p> <p>*Corresponding author (chengji@missouri.edu)</p> <p>There is four test dataset in this package, each test dataset contains four different folders and one list file. The <strong>afpred_pdb</strong> includes all the corresponding monomer structures predicted by alphafold. The <strong>cdpred_output </strong>includes the prediction results of our tool CDPred for each dataset. The <strong>pre_gen_a3m </strong>includes the multiple sequence alignments file used by CDPred to generate prediction results. And the <strong>true_pdb </strong>includes the fasta file for the test dataset and its heavy atom distance map (h_dist) and carbon alpha distance map (real_dist) that extract from the native structure.</p> <p>HomoTest1: The homodimer test dataset contains 28 targets collect from CASP_CAPRI 10-13</p> <p>HomoTest2: The homodimer test dataset contains 23 targets collect from CASP_CAPRI 13-14</p> <p>HeteroTest1: The heterodimer test dataset contains 9 targets collect from CASP_CAPRI13-14</p> <p>HeteroTest2: The heterodimer test dataset contains 55 targets collect from PDB bank 09-2021 to 11-2021</p>
Bacterial-Type Long-Chain Polyphosphates Bind Human Proteins in the Phosphatidylinositol Signaling Pathway
<p>This dataset consists of 2 tabs.<br> Tab 1: Processed full data<br> This contains the full array data (all proteins), with brightest signals sorted to the top. The data was filtered according to the criteria laid out in the report (see column headers, cells formatted in red if criteria not met).<br> The right side of the table displays the raw data of the duplicate spots on the sample array (incubated with polyP-bio + Strept-532) and the control array (incubated with Strept-532 only). "</p> <p>Tab 2: Histone Controls<br> This contains the individual fluorescence values for the histone control spots (which are repeated in duplicate on each of the 48 subarrays, i.e. 96 values total), as well as their averages, side by side for sample and control array.</p> <p> </p>
Supplementary materials used for the research to predict phase separation in protein chains
<p>This is a collection of all the data, codes and metadata that were used for the research titled: 'Sequence based prediction of protein phase separation into disordered condensates using machine learning'.</p>
Simulations of membrane proteins in a mixture of lipids with varying levels of chain unsaturation
<p>Four structurally distinct membrane proteins were simulated in a mixture of phospholipids. The membranes contained one copy of one of the protein types with equimolar concentrations of DPPC, DOPC, DLiPC, SDPC, and cholesterol. The CHARMM36 force field [1] was employed, and the systems were built using the CHARMM-GUI web portal [2]. The 4-microsecond long simulations were performed using GROMACS [3]. Further details on the setup of the systems as well as on the simulation protocol can be found in the related paper at DOI: 10.1371/journal.pcbi.1007033.</p> <p>The files are named based on the PDB codes of the proteins (1AFO, 2M0B, 3EML, and 3EMN). For each system, the original simulation run input file (tpr) and the corresponding outputs (xtc, edr) are provided. Additionally, the simulations can be extended using the continue point (cpt) file. For each system the files required to generate the run input file are also provided: the topology file (top), index file (ndx), and a common run parameter file (mdp). The CHARMM36 force field can be downloaded from http://mackerell.umaryland.edu/charmm_ff.shtml</p> <p>Notably, the protein definition files (itp) were re-generated. After performing these simulations, the atom ordering of cholesterol in the CHARMM36 force field was changed. The provided gro files, as well as the lipid definition files (itp) correspond to this new atom order. While they can be used to generate a run input file, yet this run input file will not be identical to the one provided. Therefore: 1) for analysis, the provided xtc+tpr files are fine; 2) for extending the simulation, the tpr+cpt files are fine; 3) for new simulations, the tpr file can be generated based on the provided files, yet it cannot be used together with the provided xtc file.</p> <p>[1] <strong>DOI: </strong>10.1021/jp101759q</p> <p>[2] <strong>DOI: </strong>10.1021/acs.jctc.5b00935</p> <p>[3] <strong>DOI: </strong>10.1016/j.softx.2015.06.001</p>
Ligand binding remodels protein side chain conformational heterogeneity
<p>While protein conformational heterogeneity plays an important role in many aspects of biological function, including ligand binding, its impact has been difficult to quantify. Macromolecular X-ray diffraction is commonly interpreted with a static structure, but it can provide information on both the anharmonic and harmonic contributions to conformational heterogeneity. Here, through multiconformer modeling of time- and space-averaged electron density, we measure conformational heterogeneity of 743 stringently matched pairs of crystallographic datasets that reflect unbound/apo and ligand-bound/holo states. When comparing the conformational heterogeneity of side chains, we observe that when binding site residues become more rigid upon ligand binding, distant residues tend to become more flexible, especially in non-solvent exposed regions. Among ligand properties, we observe increased protein flexibility as the number of hydrogen bonds decrease and relative hydrophobicity increases. Across a series of 13 inhibitor bound structures of CDK2, we find that conformational heterogeneity is correlated with inhibitor features and identify how conformational changes propagate differences in conformational heterogeneity away from the binding site. Collectively, our findings agree with models emerging from NMR studies suggesting that residual side chain entropy can modulate affinity and point to the need to integrate both static conformational changes and conformational heterogeneity in models of ligand binding.</p>
Medium Chain Triglycerides (MCT) and Whey Protein Isolate (WPI) for Type 2 Diabetes Patients (Combine)
ClinicalTrials.gov study NCT04905589. IPD Sharing: NO. Countries: 1. Publications: 1.
Source Data for: VCP/p97-Associated Proteins are Binders and Debranching Enzymes of K48-K63-Branched Ubiquitin Chains
Open the record for dataset details and reuse information.
Neurofilament Light Chain ,Chitinase-3 Like-1 Proteins and Plasmacytoid Dendritic Cells in Multiple Sclerosis
ClinicalTrials.gov study NCT05451069. IPD Sharing: UNDECIDED. Countries: 0. Publications: 14.
Chromobox protein homolog 7 suppresses the stem-like phenotype of glioblastoma cells by regulating the myosin heavy chain 9-NF-κB signaling pathway
GEO Series GSE283387. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Murine deficiency of peroxisomal L-bifunctional protein (EHHADH) causes medium-chain 3-hydroxydicarboxylic aciduria and perturbs hepatic cholesterol homeostasis
GEO Series GSE171292. Mus musculus. 23 samples. Type: Expression profiling by high throughput sequencing.
Transcriptome changes in the retinal pigment epithelial cells of Microtubule-Associated Protein 1 Light Chain 3B gene knockout mouse (LC3b-/-)
GEO Series GSE225344. Mus musculus. 7 samples. Type: Expression profiling by high throughput sequencing.
A viral ADP-ribosyltransferase attaches RNA chains to host proteins
GEO Series GSE214431. Escherichia coli B. 11 samples. Type: Expression profiling by high throughput sequencing.
Loss of Mitochondrial Fatty Acid Beta-Oxidation Protein Short Chain Enoyl-CoA Hydratase Disrupts Oxidative Phosphorylation Protein Complex Stability and Function
GEO Series GSE200252. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.