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8 results for “protein sequence database”
PSSH2 - database of protein sequence-to-structure homologies (including Sars-CoV-2 structures)
<p><strong>Protein sequence and structure data</strong></p> <p>This data set contains data from Uniprot (in the files called protein_sequence, protein_synonyms, protein_names, organism_synonyms) and PDB (in the files called PDB and PDB_chain) as used by the <a href="https://github.com/ODonoghueLab/Aquaria">Aquaria web resource</a> at the time of download (2022-02-08).</p> <p> </p> <p><strong>The PSSH2 data set</strong><br> <br> PSSH2 is a database of protein sequence-to-structure homologies based on HHblits, an alignment method employing iterative comparisons of hidden Markov models (HMMs). To ensure the highest possible final alignment quality for matches in Aquaria using HHblits, we first calculate HMM profiles for each unique PDB sequence (PDB_full) and also for each unique Swiss-Prot sequence. We generated PSSH2 using HHblits to find similarities between HMMs from PDB and HMMs from UniProt sequences.</p> <p> </p> <p><strong>Calculating PSSH2</strong></p> <p>The Swissprot and PDB data was downloaded in November 2021.<br> Generating PSSH2: We used <a href="https://gwdu111.gwdg.de/~compbiol/uniclust/2021_03/UniRef30_2021_03.tar.gz">UniRef30_2021_03</a> (originally called UniRef30_2021_06) from HH-suite, a database of non-redundant UniProt sequence clusters in which the highest pairwise sequence identity between clusters was 30%. The HHblits code and the code for running the calculations was retrieved from git (https://github.com/soedinglab/hh-suite.git and https://github.com/aschafu/PSSH2.git respectively) at the respective time of calculation in the timeframe until December 2021. <br> </p> <p><strong>PDB based sequence-to-structure alignments</strong></p> <p>In addition to the PSSH2 data, new PDB structures were retrieved based on the primary accession of the proteins, by querying for all chains in all PDB entries with exact matches using the sequence cross references records given in PDB. Sequence-to-structure alignments were then created, again based on information provided in each PDB entry. These are contained in the PDBchain data.</p> <p>This data covers sequences and PDB structures in the timeframe until February 2022. </p> <p> </p> <p><strong>Evaluating PSSH2</strong></p> <p>The resulting alignment data was analysed using CATH domain assignments downloaded from /cath/releases/all-releases/v4_2_0/cath-classification-data/ to define correct hits and false hits: </p> <ul> <li>The set of query sequences is defined by the CATH non-redundant S40_overlap_60 dataset (ftp://orengoftp.biochem.ucl.ac.uk/cath/releases/all-releases/v4_2_0/non-redundant-data-sets/)</li> <li>The set of all expected hits are all pdb structures containing a domain with the same CATH code if contained in the set of processed sequences (-> all) or only if also contained in the set of non redundant sequences (-> nr40).</li> <li>The set of true positives is defined by sharing the same CATH code up to the level of homology ("CATH") or up to the level of topology ("CAT").</li> </ul> <p>The data was evaluated with respect to false discovery rate (FDR) and recall (true positive rate TPR) by cumulatively considering all hits with an E-value below the threshold ("C") or in bins with an E-value between the threshold and one tenth of the threshold ("B"). This evaluation was carried out for the data obtained in November 2021 (202111) as well as previous data from October 2020 (202010), February 2020 (202002) and September 2017 (201709). The results are collected in <a href="https://zenodo.org/api/files/445add84-fcf1-4dfe-b8a1-63dc55f378ee/PSSH%20CATH%20validation.csv?versionId=a5df7473-6efd-442b-b422-3944e9452003">PSSH CATH validation.csv</a>. </p> <p> </p> <p><strong>Known errors</strong></p> <p>Due to processing error, the profile of pdb structure 5fia A / B (sequence md5 052667679fc644184f40063c7602c9e1) is incomplete in the pdb_full hhblits database which led to further errors in generating sequence based alignments for sequences for 1vtm P (sequence md5 c844aff103449363cb8489c78c58ebf1) and 434t A / B (sequence md5 d67aa1c3a36492c719cb48b5e7ecc624).<br> <br> </p>
CusVarDB: A tool for building customized sample-specific variant protein database from Next-generation sequencing datasets
<p>CusVarDB is a windows based tool for creating a variant protein database from Next-generation sequencing datasets. The program supports variant calling for Genome, RNA-Seq and exome datasets.</p> <p>This repository will provide the resultant variant peptides identified in our study and its corresponding information. The detailed information of the table is given below.</p> <p>Supplementary Table 1. This table contains the resultant variant peptides along with its wild-type peptides from BT474, MDMAB157, MFM223, and HCC38 datasets. Along with mutant peptides, this section also provides additional information such as peptide-spectrum match (PSM), Protein accession, cross-correlation value from the search (Xcorr), and retention time (RT).</p> <p>Supplementary Table 2. This table provides the complete details of the resultant peptides. Here the mutant and corresponding wild-type peptides are mentioned in different sheets. For a given mutant peptide its wild-type peptide and corresponding information can be mapped using the VLOOKUP function in Excel by keeping column A (Sl.No) as lookup parameter.</p> <p>Supplementary Table 3. This table briefs about the variants which are already reported in other cancers.</p>
Uncovering hundreds of RNA viral RdRps amongst uncharacterised sequences in public protein databases.
<p>These data are associated with the following manuscript:</p> <p>Brown, K., Firth, A. E. (2025)<br>Uncovering hundreds of RNA viral RdRps amongst uncharacterised sequences in public protein databases.<br><br></p>
In-house sequence database of S.pn recombinant protein vaccine target-related genes
<p>In-house sequence database of S.pn recombinant protein vaccine target-related genes.</p>
Proteome database of 36 million proteins from 4,351 species, including marine microbial sequences
<p>A fasta-formatted database of 36,866,870 predicted proteins representing 4,351 unique species from 117 phyla.</p>
Proteome database of 36 million proteins from 4,351 species, including marine microbial sequences
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Evaluation SIHUMI dataset: A sectioning and database enrichment approach for improved peptide spectrum matching in large, genome-guided protein sequence databases
<p>Dataset for evaluation of database sectioning method for generating an enriched database for mass-spectrometry-based proteomic approaches using large databases. Our evaluation demonstrates that this method helps to increase the sensitivity of PSMs while maintaining acceptable FDR statistics. This dataset was acquired from the protein samples containing proteins from eight microorganisms (<em>Anaerostipes caccae, Bacteroides thetaiotaomicron, Bifidobacterium longum, Blautia producta, Clostridium butyricum, Clostridium ramosum, Escherichia coli, Lactobacillus plantarum</em>) that were grown in a bioreactor. The MS-data for this dataset was acquired by Dr. Hettich's group at Oak Ridge National Laboratory, and it was made available by Dr. Nico Jehmlich from Helmholtz Center for Environmental Research through the 3rd International Metaproteome Symposium (<a href="https://www.ufz.de/index.php?en=44639">https://www.ufz.de/index.php?en=44639</a>). </p>
RNA-sequencing of 69 cell lines in the Human Protein Atlas database
GEO Series GSE240542. Homo sapiens. 141 samples. Type: Expression profiling by high throughput sequencing.
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