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34 results for “Subcellular localization”
SLC35A2 deficiency reduces protein levels of core 1 β-1,3-galactosyltransferase 1 (C1GalT1) and its chaperone Cosmc and affects their subcellular localization - yet unpublished supplementary data
<p>The upload contains raw data files used for the article "SLC35A2 deficiency reduces protein levels of core 1 β-1,3-galactosyltransferase 1 (C1GalT1) and its chaperone Cosmc and affects their subcellular localization".</p> <p><strong>Article abstract</strong></p> <p>Nucleotide sugar transporters (NSTs) are multitransmembrane proteins, localized in the Golgi apparatus and/or endoplasmic reticulum, which provide glycosylation enzymes with their substrates. It has been demonstrated that NSTs may form complexes with functionally related glycosyltransferases, especially in the N-glycosylation pathway. However, potential interactions of NSTs with enzymes mediating the biosynthesis of mucin-type O-glycans have not been addressed to date. Here we report that UDP-galactose transporter (UGT; SLC35A2) associates with core 1 β-1,3-galactosyltransferase 1 (C1GalT1; T-synthase). This provides the first example of an interaction between an enzyme that acts exclusively in the O-glycosylation pathway and an NST. We also found that SLC35A2 associated with the C1GalT1-specific chaperone Cosmc, and that the endogenous Cosmc was localized in both the endoplasmic reticulum and Golgi apparatus of wild-type HEK293T cells. Furthermore, in SLC35A2-deficient cells protein levels of C1GalT1 and Cosmc were decreased and their Golgi localization was less pronounced. Finally, we identified SLC35A2 as a novel molecular target for the antifungal agent itraconazole. Based on our findings we propose that NSTs may contribute to the stabilization of their interaction partners and help them to achieve target localization in the cell, most likely by facilitating their assembly into larger functional units. </p> <p>The Word document (Data description.docx) contains a description of each file.</p>
Data from: Expression, subcellular localization, and mechanistic analysis of intellectual disability syndrome protein ABBA
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Protein Subcellular localization prediction data used in the article entitled "MSclassifier: Median-Supplement model-based Classification tool for automated knowledge discovery"
<p>This repository contains data used to obtain results from a 5-fold cross-validation testing of how MSclassifier and other packages accurately predict protein subcellular localization in the software article entitled "MSclassifier: median-supplement model-based classification tool for automated knowledge discovery." The data used in the software article is derived from data generated in "G. K. Acquaah-Mensah, S. M. Leach, and C. Guda, Predicting the subcellular localization of human proteins using machine learning and exploratory data analysis, Genomics Proteomics Bioinformatics, 4(2):120-133, 2006, <a href="https://doi.org/10.1016/S1672-0229(06)60023-5">https://doi.org/10.1016/S1672-0229(06)60023-5</a>"</p>
Figure 4: The Pacer RH domain basic triad controls subcellular localization [FINAL]
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Disorder predictions for all reviewed UniProt proteins with "membrane" subcellular localization
<h1>IDRs in membrane proteins</h1> <h3>About</h3> <p>The writeup below walks through how to answer the question "what fraction of membrane proteins have IDRs".</p> <p>The tl/dr: on average <strong>55% of membrane proteins [as defined here] have IDRs</strong>.</p> <h3>Data</h3> <p>This directory (WITH the associated sequences), which are not included on GitHub, are deposited on Zenodo.</p> <h2>Steps</h2> <h4>Install software</h4> <p>Install metapredict</p> <div> <pre><code>pip install metapredict</code></pre> </div> <p>Done.</p> <h4>Find the data</h4> <p>Using the "Advanced Search", I downloaded all reviewed proteins on <a href="https://uniprot.org/">UniProt</a> tagged as having a "membrane" subcellular localization.</p> <p>There are, of course, much more detailed ways one could get these data (e.g., requiring transmembrane helices, integral membrane protein filtering etc, etc), but I figured for purposes here, I'd just do this...</p> <h4>Download the data</h4> <p>From this, I downloaded all the FASTA sequences. This triggers the following file to be downloaded:</p> <div> <pre><code>uniprotkb_cc_scl_term_SL_0162_AND_revie_2024_11_09.fasta</code></pre> </div> <h4>Preprocessing the data</h4> <p>Next I wanted to fix any sequences with non-standard amino acids by converting those residues to "standard" amino acids. I did this using <code>pfasta</code>, a commandline tool that's part of <a href="https://protfasta.readthedocs.io/en/latest/">protfasta</a> which comes with metapredict.</p> <p>Did this simply with</p> <div> <pre><code>pfasta --invalid-sequence convert-all -o membrane_proteins.fasta uniprotkb_cc_scl_term_SL_0162_AND_revie_2024_11_08.fasta</code></pre> </div> <ul> <li>The <code>--invalid-sequence</code> flag tells pfasta what to with an invalid sequence, and <code>-o</code> tells it what the ouput file should be called.</li> </ul> <p>Running this (~5 seconds) generates the following output:</p> <div> <pre><code>........................ pfasta version 0.1.13+0.gc1786bd.dirty Please report bugs to: https://github.com/holehouse-lab/protfasta ........................ [INFO]: Reading in the file uniprotkb_cc_scl_term_SL_0162_AND_revie_2024_11_08.fasta [INFO]: Read in file with 1004330 lines [INFO]: Parsed file to recover 117691 sequences [INFO]: Converted 697 sequences to valid sequences [INFO]: Writing new sequence file [membrane_proteins.fasta]...</code></pre> </div> <h4>Predicting all IDRs</h4> <p>Run one of metapredict's command-line utilities to predict all IDRs in these proteins.</p> <div> <pre><code>metapredict-predict-idrs membrane_proteins.fasta -o membrane_protein_idrs.fasta</code></pre> </div> <p>This takes ~20 seconds on my desktop with an A4500 GPU or ~5 min with my Macbook Pro. We're then left with a file called <code>membrane_protein_idrs.fasta</code>. This is a FASTA file that the header format:</p> <div> <pre><code>><HEADER FOR ORIGINAL PROTEIN> IDR_START=<start position> IDR_END=<end_position></code></pre> </div> <p>e.g.</p> <div> <pre><code>>sp|B4R1D8|HH_DROSI Protein hedgehog OS=Drosophila simulans OX=7240 GN=hh PE=3 SV=1 IDR_START=0 IDR_END=94 MDNHSSVPWASAASVTCLSLDAKCHSSSSSCSSKSTASSISASPETQTMRHIAHTQRCLS RLTSLVALLLIVLPMMFSPAHSCGPGRGLGRHRA</code></pre> </div> <h3>Hacky way to get number of proteins with IDRs</h3> <p>Get the number of unique proteins in the main file:</p> <div> <pre><code>cat membrane_proteins.fasta | grep ">" | awk -F "|" {' print $2 '} | sort | uniq |wc</code></pre> </div> <p>Which returns</p> <div> <pre><code>117691 117691 829609</code></pre> </div> <p>So we have 117,691 unique proteins (NB: we could also just have gotten this without the <code>awk -F "|" {' print $2 '} | sort | uniq</code> part BUT we do need that for the number of unique proteins with IDRs so good to be consistent.</p> <div> <pre><code>cat membrane_protein_idrs.fasta | grep ">" | awk -F "|" {' print $2 '} | sort | uniq |wc</code></pre> </div> <p>Which returns</p> <div> <pre><code>64971 64971 458829 </code></pre> </div> <p>So on average <strong>55% of membrane proteins [as defined here] have IDRs</strong>.</p> <h3>Bonus!</h3> <p>In addition we can predict all disorder scores rather than discrete IDRs using</p> <div> <pre><code>metapredict-predict-disorder membrane_proteins.fasta -o membrane_protein_disorder_scores.csv</code></pre> </div> <p>This takes ~5 min on my Macbook Pro. The data for these are included here for completeness/convenience.</p>
Data from: Determining the subcellular location of new proteins from microscope images using local features
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Identification of spatiotemporal RNA expression and subcellular RNA localization during Drosophila embryonic neurogenesis
GEO Series GSE106095. Drosophila melanogaster. 60 samples. Type: Expression profiling by high throughput sequencing.
Subcellular localization of coagulation factor II receptor-like 1 in neurons governs angiogenesis
GEO Series GSE58660. Homo sapiens. 1 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Subcellular localization analysis of long non-non coding RNAs (lncRNAs) in skeletal muscle myofibers
GEO Series GSE112768. Mus musculus. 18 samples. Type: Non-coding RNA profiling by array.
Quantification of subcellular RNA localization through direct detection of RNA oxidation
GEO Series GSE279714. Danio rerio; Homo sapiens. 211 samples. Type: Other.
m6A and YTHDF proteins control the subcellular localization of select neuronal mRNAs [RNA-seq and DART-seq]
GEO Series GSE171199. Mus musculus. 32 samples. Type: Expression profiling by high throughput sequencing; Other.
Next Generation Sequencing of MDA-MB-231 cells with variable subcellular localization of Aurora-A Kinase (Wild-type (WT), no localization signal sequence, Cytoplasmic (NES) or Nuclear (NLS)) .
GEO Series GSE154494. Homo sapiens. 26 samples. Type: Expression profiling by high throughput sequencing.
RNA Subcellular CAGE Localization from ENCODE/RIKEN
GEO Series GSE34448. Homo sapiens. 78 samples. Type: Expression profiling by high throughput sequencing.
The highly localized interaction between Neurofascin-186 and Gliomedin promotes subcellular innervation by the chandelier cell
GEO Series GSE310319. Mus musculus. 9 samples. Type: Expression profiling by high throughput sequencing.
Role of Aldoketoreductases and Other Doxorubicin Pharmacokinetic Genes in Doxorubicin Resistance, DNA binding, and Subcellular Localization
GEO Series GSE27254. Homo sapiens. 16 samples. Type: Expression profiling by array.
Alteration in expression and subcellular localization of the androgen receptor-regulated FAM111A
GEO Series GSE286417. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
Atlas of subcellular RNA localization in human embryonic stem cells and its flow during differentiation
GEO Series GSE206328. Homo sapiens. 40 samples. Type: Expression profiling by high throughput sequencing.
Disruption of ligand binding domain of the Epidermal Growth Factor Receptor by sequential knock in CRISPR/Cas9 genome editing alters its subcellular localization and phosphorylation in cervical cancer
GEO Series GSE288741. Homo sapiens. 6 samples. Type: Other.
APEX-seq Maps Transcriptome-wide Subcellular RNA Localization in Living Cells
GEO Series GSE299592. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
Replicative senescence in human embryo bone marrow MSCs is characterized by changes in cell motility and subcellular local-ization of Rac1 and RhoA GTPases
<p>Supplementary files to the article "Replicative senescence in human embryo bone marrow MSCs is characterized by changes in cell motility and subcellular local-ization of Rac1 and RhoA GTPases"</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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