Dataset Description
Dataset Summary
This dataset is a mirror of the Uniprot/SwissProt database. It contains the names and sequences of >500K proteins. This dataset was parsed from the FASTA file at https://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/complete/uniprot_sprot.fasta.gz. Supported Tasks and Leaderboards: None Languages: English
Dataset Structure
Data Instances
Data Fields: id, description, sequence Data… See the full description on the dataset page: https://huggingface.co/datasets/damlab/uniprot.
This dataset is a selection of The Therapeutic Target Database (release 4.3.02, 18th Oct 2013) protein IDs for successful targets. The web page states 388 but these reduced to 345 human Swiss-Prot accessions.
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All unigenes of Portunus sanguinolentus hit to the Swiss-Prot database.
This dataset is a supplementary data from "Analysis of in vitro bioactivity data extracted from drug discovery literature and patents: Ranking 1654 human protein targets by assayed compounds and molecular scaffolds" (2011). In this case the Entrez Gene IDs were mapped to 1651 human Swiss-Prot accessions but this includes both approved and research targets.
This dataset is a supplementary data from "Novelty in the target landscape of the pharmaceutical industry" (2013). The listing of proven drug targets is converted to 248 human Swiss-Prot accessions.
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The large-scale identification and quantitation of proteins via nanoliquid chromatography (LC)-tandem mass spectrometry (MS/MS) offers a unique opportunity to gain unprecedented insight into the microbial composition and biomolecular activity of true environmental samples. However, in order to realize this potential for marine biofilms, new methods of protein extraction must be developed as many compounds naturally present in biofilms are known to interfere with common proteomic manipulations and LC-MS/MS techniques. In this study, we used amino acid analyses (AAA) and LC-MS/MS to compare the efficacy of three sample preparation methods [6 M guanidine hydrochloride (GuHCl) protein extraction + in-solution digestion + 2D LC; sodium dodecyl sulfate (SDS) protein extraction + 1D gel LC; phenol protein extraction + 1D gel LC] for the metaproteomic analyses of an environmental marine biofilm. The AAA demonstrated that proteins constitute 1.24% of the biofilm wet weight and that the compared methods varied in their protein extraction efficiencies (0.85–15.15%). Subsequent LC-MS/MS analyses revealed that the GuHCl method resulted in the greatest number of proteins identified by one or more peptides whereas the phenol method provided the greatest sequence coverage of identified proteins. As expected, metagenomic sequencing of the same biofilm sample enabled the creation of a searchable database that increased the number of protein identifications by 48.7% (≥1 peptide) or 54.7% (≥2 peptides) when compared to SwissProt database identifications. Taken together, our results provide methods and evidence-based recommendations to consider for qualitative or quantitative biofilm metaproteome experimental design.
Curated component of UniProtKB (produced by the UniProt consortium). It contains hundreds of thousands of protein descriptions, including function, domain structure, subcellular location, post-translational modifications and functionally characterized variants.
Dataset
Swissprot is a high quality manually annotated protein database. The dataset contains annotations with the functional properties of the proteins. Here we extract proteins with Enzyme Commission labels. The dataset is ported from Protinfer: https://github.com/google-research/proteinfer. The leaf level EC-labels are extracted and indexed, the mapping is provided in idx_mapping.json. Proteins without leaf-level-EC tags are removed.
Example
The protein Q87BZ2 have… See the full description on the dataset page: https://huggingface.co/datasets/lightonai/SwissProt-EC-leaf.
The SWISS-PROT protein knowledgebase (http://www.expasy.org/sprot/ and http://www.ebi.ac.uk/swissprot/) connects amino acid sequences with the current knowledge in the Life Sciences. Each protein entry provides an interdisciplinary overview of relevant information by bringing together experimental results, computed features and sometimes even contradictory conclusions. Detailed expertise that goes beyond the scope of SWISS-PROT is made available via direct links to specialised databases. SWISS-PROT provides annotated entries for all species, but concentrates on the annotation of entries from human (the HPI project) and other model organisms to ensure the presence of high quality annotation for representative members of all protein families. Part of the annotation can be transferred to other family members, as is already done for microbes by the High-quality Automated and Manual Annotation of microbial Proteomes (HAMAP) project. Protein families and groups of proteins are regularly reviewed to keep up with current scientific findings. Complementarily, TrEMBL strives to comprise all protein sequences that are not yet represented in SWISS-PROT, by incorporating a perpetually increasing level of mostly automated annotation. Researchers are welcome to contribute their knowledge to the scientific community by submitting relevant findings to SWISS-PROT at swiss-prot@expasy.org.
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Gene Ontology according to the Swiss-Prot database for the substrates of the minimal kinome, shown for humanized substrate set.
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PROSITE is a database of protein families and domains. It consists of biologically significant sites, patterns and profiles that help to reliably identify to which known protein family a new sequence belongs. PROSITE is based at the Swiss Institute of Bioinformatics (SIB), Geneva, Switzerland.
The Peptide Sequence Database contains putative peptide sequences from human, mouse, rat, and zebrafish. Compressed to eliminate redundancy, these are about 40 fold smaller than a brute force enumeration. Current and old releases are available for download. Each species'' peptide sequence database comprises peptide sequence data from releveant species specific UniGene and IPI clusters, plus all sequences from their consituent EST, mRNA and protein sequence databases, namely RefSeq proteins and mRNAs, UniProt''s SwissProt and TrEMBL, GenBank mRNA, ESTs, and high-throughput cDNAs, HInv-DB, VEGA, EMBL, IPI protein sequences, plus the enumeration of all combinations of UniProt sequence variants, Met loss PTM, and signal peptide cleavages. The README file contains some information about the non amino-acid symbols O (digest site corresponding to a protein N- or C-terminus) and J (no digest sequence join) used in these peptide sequence databases and information about how to configure various search engines to use them. Some search engines handle (very) long sequences badly and in some cases must be patched to use these peptide sequence databases. All search engines supported by the PepArML meta-search engine can (or can be patched to) successfully search these peptide sequence databases.
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NCBIfam is a collection of protein families, featuring curated multiple sequence alignments, hidden Markov models (HMMs) and annotation, which provides a tool for identifying functionally related proteins based on sequence homology. NCBIfam is maintained at the National Center for Biotechnology Information (Bethesda, MD). NCBIfam includes models from TIGRFAMs, another database of protein families developed at The Institute for Genomic Research, then at the J. Craig Venter Institute (Rockville, MD, US).
bayes-group-diffusion/swissprot dataset hosted on Hugging Face and contributed by the HF Datasets community
Central repository for collection of functional information on proteins, with accurate and consistent annotation. In addition to capturing core data mandatory for each UniProtKB entry (mainly, the amino acid sequence, protein name or description, taxonomic data and citation information), as much annotation information as possible is added. This includes widely accepted biological ontologies, classifications and cross-references, and experimental and computational data. The UniProt Knowledgebase consists of two sections, UniProtKB/Swiss-Prot and UniProtKB/TrEMBL. UniProtKB/Swiss-Prot (reviewed) is a high quality manually annotated and non-redundant protein sequence database which brings together experimental results, computed features, and scientific conclusions. UniProtKB/TrEMBL (unreviewed) contains protein sequences associated with computationally generated annotation and large-scale functional characterization that await full manual annotation. Users may browse by taxonomy, keyword, gene ontology, enzyme class or pathway.
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Protein sequence and structure data
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 Aquaria web resource at the time of download (2022-02-08).
The PSSH2 data set
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.
Calculating PSSH2
The Swissprot and PDB data was downloaded in November 2021. Generating PSSH2: We used UniRef30_2021_03 (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.
PDB based sequence-to-structure alignments
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.
This data covers sequences and PDB structures in the timeframe until February 2022.
Evaluating PSSH2
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:
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/)
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).
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").
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 PSSH CATH validation.csv.
Known errors
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).
A collection of secreted proteins from Human, Mouse and Rat proteomes, which includes sequences from SwissProt, Trembl, Ensembl and Refseq. The 18,152 entries are classified into fourteen functional categories, including "apolipoprotein", "cytokine", "protease", "toxin", etc. To make the dataset more comprehensive, nine related datasets were also collected, such as SPDI, Riken mouse secretome, SwissProt vertebrate secreted proteins, SubLoc etc.
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The CATH-Gene3D database describes protein families and domain architectures in complete genomes. Protein families are formed using a Markov clustering algorithm, followed by multi-linkage clustering according to sequence identity. Mapping of predicted structure and sequence domains is undertaken using hidden Markov models libraries representing CATH and Pfam domains. CATH-Gene3D is based at University College, London, UK.
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License information was derived automatically
The PSSH2 data set
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.
This dataset contains the Swissprot and PDB data used for generating PSSH2 along with the PSSH2 data itself. This consists of the sequence-to-structure alignments used in Aquaria (aquaria.ws) and also for the Covid19 resource of Aquaria (http://aquaria.ws/covid).
Calculating PSSH2
The main bunch of Swissprot and PDB data was downloaded in February 2020, but incremental updates, especially as related to Covid19 were added until July 2020.
Generating PSSH2: We used Uniclust30 from HH-suite, a database of non-redundant UniProt sequence clusters in which the highest pairwise sequence identity between clusters was 30% (http://gwdu111.gwdg.de/~compbiol/uniclust/2018_08/uniclust30_2018_08_hhsuite.tar.gz). 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 between February and July 2020.
Evaluating PSSH2
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:
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 February 2020 (202002) as well as previous data from September 2017 (201709) and has since been repeated for data from October 2020 (202010). The results are collected in PSSH CATH validation.csv.
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License information was derived automatically
SFLD (Structure-Function Linkage Database) is a hierarchical classification of enzymes that relates specific sequence-structure features to specific chemical capabilities.
Dataset Description
Dataset Summary
This dataset is a mirror of the Uniprot/SwissProt database. It contains the names and sequences of >500K proteins. This dataset was parsed from the FASTA file at https://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/complete/uniprot_sprot.fasta.gz. Supported Tasks and Leaderboards: None Languages: English
Dataset Structure
Data Instances
Data Fields: id, description, sequence Data… See the full description on the dataset page: https://huggingface.co/datasets/damlab/uniprot.