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{ "count": 259, "next": "?page=2", "previous": null, "list": [ { "name": "SNO-DCA", "description": "A model for predicting S-nitrosylation sites based on densely connected convolutional networks and attention mechanism.", "homepage": "https://sno.cangmang.xyz/SNO-DCA", "biotoolsID": "sno_dca", "biotoolsCURIE": "biotools:sno_dca", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0417", "term": "PTM site prediction" }, { "uri": "http://edamontology.org/operation_3927", "term": "Network analysis" }, { "uri": "http://edamontology.org/operation_3937", "term": "Feature extraction" }, { "uri": "http://edamontology.org/operation_0224", "term": "Query and retrieval" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_0601", "term": "Protein modifications" }, { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" }, { "uri": "http://edamontology.org/topic_3510", "term": "Protein sites, features and motifs" } ], "operatingSystem": [], "language": [ "Python" ], "license": null, "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/peanono/SNO-DCA", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1016/J.HELIYON.2023.E23187", "pmid": "38148797", "pmcid": "PMC10750070", "type": [], "version": null, "note": null, "metadata": { "title": "SNO-DCA: A model for predicting S-nitrosylation sites based on densely connected convolutional networks and attention mechanism", "abstract": "Protein S-nitrosylation is a reversible oxidative reduction post-translational modification that is widely present in the biological community. S-nitrosylation can regulate protein function and is closely associated with a variety of diseases, thus identifying S-nitrosylation sites are crucial for revealing the function of proteins and related drug discovery. Traditional experimental methods are time-consuming and expensive; therefore, it is necessary to explore more efficient computational methods. Deep learning algorithms perform well in the field of bioinformatics sites prediction, and many studies show that they outperform existing machine learning algorithms. In this work, we proposed a deep learning algorithm-based predictor SNO-DCA for distinguishing between S-nitrosylated and non-S-nitrosylated sequences. First, one-hot encoding of protein sequences was performed. Second, the dense convolutional blocks were used to capture feature information, and an attention module was added to weigh different features to improve the prediction ability of the model. The 10-fold cross-validation and independent testing experimental results show that our SNO-DCA model outperforms existing S-nitrosylation sites prediction models under imbalanced data. In this paper, a web server prediction website: https://sno.cangmang.xyz/SNO-DCA/was established to provide an online prediction service for users. SNO-DCA can be available at https://github.com/peanono/SNO-DCA.", "date": "2024-01-15T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Jia J." }, { "name": "Lv P." }, { "name": "Wei X." }, { "name": "Qiu W." } ], "journal": "Heliyon" } } ], "credit": [ { "name": "Jianhua Jia", "email": "jjh163yx@163.com", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-18T13:12:26.515271Z", "lastUpdate": "2024-04-18T13:12:26.517501Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "SNPs and GO", "description": "A server for the prediction of single point protein mutations likely to be involved in the insurgence of diseases in humans.s.", "homepage": "http://snps-and-go.biocomp.unibo.it/snps-and-go/index.html", "biotoolsID": "snps_and_go", "biotoolsCURIE": "biotools:snps_and_go", "version": [ "1.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2422", "term": "Data retrieval" }, { "uri": "http://edamontology.org/operation_2414", "term": "Protein function analysis" }, { "uri": "http://edamontology.org/operation_3225", "term": "Variant classification" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3021", "term": "UniProt accession" }, "format": [ { "uri": "http://edamontology.org/format_2330", "term": "Textual format" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_0896", "term": "Protein report" }, "format": [ { "uri": "http://edamontology.org/format_2330", "term": "Textual format" } ] } ], "note": "Prediction", "cmd": null } ], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_0199", "term": "Genetic variation" }, { "uri": "http://edamontology.org/topic_3510", "term": "Protein sites, features and motifs" }, { "uri": "http://edamontology.org/topic_3473", "term": "Data mining" }, { "uri": "http://edamontology.org/topic_3325", "term": "Rare diseases" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [], "license": null, "collectionID": [ "RD-connect", "Rare Disease", "Bologna Biocomputing Group" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [ "Italy" ], "elixirCommunity": [ "Rare Diseases" ], "link": [], "download": [], "documentation": [ { "url": "http://snps-and-go.biocomp.unibo.it/snps-and-go/help2.htm", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1002/humu.21047", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Functional annotations improve the predictive score of human disease-related mutations in proteins", "abstract": "Single nucleotide polymorphisms (SNPs) are the simplest and most frequent form of human DNA variation, also valuable as genetic markers of disease susceptibility. The most investigated SNPs are missense mutations resulting in residue substitutions in the protein. Here we propose SNPs&GO, an accurate method that, starting from a protein sequence, can predict whether a mutation is disease related or not by exploiting the protein functional annotation. The scoring efficiency of SNPs&GO is as high as 82%, with a Matthews correlation coefficient equal to 0.63 over a wide set of annotated nonsynonymous mutations in proteins, including 16,330 disease-related and 17,432 neutral polymorphisms. SNPs&GO collects in unique framework information derived from protein sequence, evolutionary information, and function as encoded in the Gene Ontology terms, and outperforms other available predictive methods. © 2009 Wiley-Liss, Inc.", "date": "2009-08-01T00:00:00Z", "citationCount": 486, "authors": [ { "name": "Calabrese R." }, { "name": "Capriotti E." }, { "name": "Fariselli P." }, { "name": "Martelli P.L." }, { "name": "Casadio R." } ], "journal": "Human Mutation" } } ], "credit": [ { "name": "ELIXIR-ITA-BOLOGNA", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Provider" ], "note": null }, { "name": "Rita Casadio", "email": "rita.casadio@unibo.it", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null }, { "name": "Pier Luigi Martelli", "email": "pierluigi.martelli@unibo.it", "url": null, "orcidid": "https://orcid.org/0000-0002-0274-5669", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "community": null, "owner": "ELIXIR-ITA-BOLOGNA", "additionDate": "2015-01-22T11:31:41Z", "lastUpdate": "2024-03-11T16:55:42.141343Z", "editPermission": { "type": "group", "authors": [ "ELIXIR-EE" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "TPpred 3.0", "description": "Organelle-targeting peptide detection and cleavage-site prediction.", "homepage": "http://tppred3.biocomp.unibo.it", "biotoolsID": "tppred_3.0", "biotoolsCURIE": "biotools:tppred_3.0", "version": [ "3" ], "otherID": [], "relation": [ { "biotoolsID": "tppred_2.0", "type": "isNewVersionOf" }, { "biotoolsID": "tppred_1.0", "type": "isNewVersionOf" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2489", "term": "Protein subcellular localisation prediction" }, { "uri": "http://edamontology.org/operation_0422", "term": "Protein cleavage site prediction" }, { "uri": "http://edamontology.org/operation_3092", "term": "Protein feature detection" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2974", "term": "Protein sequence (raw)" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_0896", "term": "Protein report" }, "format": [ { "uri": "http://edamontology.org/format_2331", "term": "HTML" } ] } ], "note": "Predictio Protein sequence in FASTA format", "cmd": null } ], "toolType": [ "Web application", "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" }, { "uri": "http://edamontology.org/topic_3510", "term": "Protein sites, features and motifs" }, { "uri": "http://edamontology.org/topic_0140", "term": "Protein targeting and localisation" }, { "uri": "http://edamontology.org/topic_0154", "term": "Small molecules" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [], "license": "GPL-3.0", "collectionID": [ "Bologna Biocomputing Group" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [ "Italy" ], "elixirCommunity": [], "link": [], "download": [ { "url": "https://github.com/BolognaBiocomp/tppred3", "type": "Source code", "note": null, "version": null }, { "url": "https://hub.docker.com/r/bolognabiocomp/tppred3", "type": "Container file", "note": null, "version": null } ], "documentation": [ { "url": "https://tppred3.biocomp.unibo.it/tppred3/default/help", "type": [ "General" ], "note": null }, { "url": "https://github.com/BolognaBiocomp/tppred3", "type": [ "Command-line options" ], "note": null } ], "publication": [ { "doi": "10.1093/bioinformatics/btv367", "pmid": "26079349", "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "TPpred3 detects and discriminates mitochondrial and chloroplastic targeting peptides in eukaryotic proteins", "abstract": "Motivation: Molecular recognition of N-terminal targeting peptides is the most common mechanism controlling the import of nuclear-encoded proteins into mitochondria and chloroplasts. When experimental information is lacking, computational methods can annotate targeting peptides, and determine their cleavage sites for characterizing protein localization, function, and mature protein sequences. The problem of discriminating mitochondrial from chloroplastic propeptides is particularly relevant when annotating proteomes of photosynthetic Eukaryotes, endowed with both types of sequences. Results: Here, we introduce TPpred3, a computational method that given any Eukaryotic protein sequence performs three different tasks: (i) the detection of targeting peptides; (ii) their classification as mitochondrial or chloroplastic and (iii) the precise localization of the cleavage sites in an organelle-specific framework. Our implementation is based on our TPpred previously introduced. Here, we integrate a new N-to-1 Extreme Learning Machine specifically designed for the classification task (ii). For the last task, we introduce an organelle-specific Support Vector Machine that exploits sequence motifs retrieved with an extensive motif-discovery analysis of a large set of mitochondrial and chloroplastic proteins. We show that TPpred3 outperforms the state-of-the-art methods in all the three tasks.", "date": "2015-03-25T00:00:00Z", "citationCount": 38, "authors": [ { "name": "Savojardo C." }, { "name": "Martelli P.L." }, { "name": "Fariselli P." }, { "name": "Casadio R." } ], "journal": "Bioinformatics" } }, { "doi": "10.1093/bioinformatics/btu411", "pmid": "24974200", "pmcid": null, "type": [ "Other" ], "version": null, "note": null, "metadata": { "title": "TPpred2: improving the prediction of mitochondrial targeting peptide cleavage sites by exploiting sequence motifs", "abstract": "CONTACT: gigi@biocomp.unibo.it SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. SUMMARY: Targeting peptides are N-terminal sorting signals in proteins that promote their translocation to mitochondria through the interaction with different protein machineries. We recently developed TPpred, a machine learning-based method scoring among the best ones available to predict the presence of a targeting peptide into a protein sequence and its cleavage site. Here we introduce TPpred2 that improves TPpred performances in the task of identifying the cleavage site of the targeting peptides. TPpred2 is now available as a web interface and as a stand-alone version for users who can freely download and adopt it for processing large volumes of sequences. Availability and implementaion: TPpred2 is available both as web server and stand-alone version at http://tppred2.biocomp.unibo.it.", "date": "2014-10-15T00:00:00Z", "citationCount": 31, "authors": [ { "name": "Savojardo C." }, { "name": "Martelli P.L." }, { "name": "Fariselli P." }, { "name": "Casadio R." } ], "journal": "Bioinformatics (Oxford, England)" } }, { "doi": "10.1093/bioinformatics/btt089", "pmid": "23428638", "pmcid": null, "type": [ "Other" ], "version": null, "note": null, "metadata": { "title": "The prediction of organelle-targeting peptides in eukaryotic proteins with Grammatical-Restrained Hidden Conditional Random Fields", "abstract": "Motivation: Targeting peptides are the most important signal controlling the import of nuclear encoded proteins into mitochondria and plastids. In the lack of experimental information, their prediction is an essential step when proteomes are annotated for inferring both the localization and the sequence of mature proteins.Results: We developed TPpred a new predictor of organelle-targeting peptides based on Grammatical-Restrained Hidden Conditional Random Fields. TPpred is trained on a non-redundant dataset of proteins where the presence of a target peptide was experimentally validated, comprising 297 sequences. When tested on the 297 positive and some other 8010 negative examples, TPpred outperformed available methods in both accuracy and Matthews correlation index (96% and 0.58, respectively). Given its very low-false-positive rate (3.0%), TPpred is, therefore, well suited for large-scale analyses at the proteome level. We predicted that from ∼4 to 9% of the sequences of human, Arabidopsis thaliana and yeast proteomes contain targeting peptides and are, therefore, likely to be localized in mitochondria and plastids. TPpred predictions correlate to a good extent with the experimental annotation of the subcellular localization, when available. TPpred was also trained and tested to predict the cleavage site of the organelle-targeting peptide: on this task, the average error of TPpred on mitochondrial and plastidic proteins is 7 and 15 residues, respectively. This value is lower than the error reported by other methods currently available. © 2013 The Author.", "date": "2013-04-15T00:00:00Z", "citationCount": 14, "authors": [ { "name": "Indio V." }, { "name": "Martelli P.L." }, { "name": "Savojardo C." }, { "name": "Fariselli P." }, { "name": "Casadio R." } ], "journal": "Bioinformatics" } }, { "doi": "10.1007/978-1-0716-1262-0_28", "pmid": "34118055", "pmcid": null, "type": [ "Review", "Benchmarking study" ], "version": null, "note": null, "metadata": { "title": "Computer-Aided Prediction of Protein Mitochondrial Localization", "abstract": "Protein sequences, directly translated from genomic data, need functional and structural annotation. Together with molecular function and biological process, subcellular localization is an important feature necessary for understanding the protein role and the compartment where the mature protein is active. In the case of mitochondrial proteins, their precursor sequences translated by the ribosome machinery include specific patterns from which it is possible not only to recognize their final destination within the organelle but also which of the mitochondrial subcompartments the protein is intended for. Four compartments are routinely discriminated, including the inner and the outer membranes, the intermembrane space, and the matrix. Here we discuss to which extent it is feasible to develop computational methods for detecting mitochondrial targeting peptides in the precursor sequence and to discriminate their final destination in the organelle. We benchmark two of our methods on the general task of recognizing human mitochondrial proteins endowed with an experimentally characterized targeting peptide (TPpred3) and predicting which submitochondrial compartment is the final destination (DeepMito). We describe how to adopt our web servers in order to discriminate which human proteins are endowed with mitochondrial targeting peptides, the position of cleavage sites, and which submitochondrial compartment are intended for. By this, we add some other 1788 human proteins to the 450 ones already manually annotated in UniProt with a mitochondrial targeting peptide, providing for each of them also the characterization of the suborganellar localization.", "date": "2021-01-01T00:00:00Z", "citationCount": 2, "authors": [ { "name": "Martelli P.L." }, { "name": "Savojardo C." }, { "name": "Fariselli P." }, { "name": "Tartari G." }, { "name": "Casadio R." } ], "journal": "Methods in Molecular Biology" } } ], "credit": [ { "name": "ELIXIR-ITA-BOLOGNA", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Provider" ], "note": null }, { "name": "Castrense Savojardo", "email": "castrense.savojardo2@unibo.it", "url": "http://biocomp.unibo.it/savojard/", "orcidid": "https://orcid.org/0000-0002-7359-0633", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact", "Developer" ], "note": null } ], "community": null, "owner": "ELIXIR-ITA-BOLOGNA", "additionDate": "2016-01-22T15:50:02Z", "lastUpdate": "2024-03-11T16:48:08.106769Z", "editPermission": { "type": "group", "authors": [ "ELIXIR-ITA-BOLOGNA", "savo" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "BUSCA", "description": "Prediction of protein subcellular localization from sequence", "homepage": "http://busca.biocomp.unibo.it", "biotoolsID": "busca", "biotoolsCURIE": "biotools:busca", "version": [ "1.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2489", "term": "Protein subcellular localisation prediction" }, { "uri": "http://edamontology.org/operation_0269", "term": "Transmembrane protein prediction" }, { "uri": "http://edamontology.org/operation_3092", "term": "Protein feature detection" }, { "uri": "http://edamontology.org/operation_0418", "term": "Protein signal peptide detection" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2974", "term": "Protein sequence (raw)" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] }, { "data": { "uri": "http://edamontology.org/data_3028", "term": "Taxonomy" }, "format": [ { "uri": "http://edamontology.org/format_2330", "term": "Textual format" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_1277", "term": "Protein features" }, "format": [ { "uri": "http://edamontology.org/format_2331", "term": "HTML" }, { "uri": "http://edamontology.org/format_3464", "term": "JSON" }, { "uri": "http://edamontology.org/format_3475", "term": "TSV" } ] } ], "note": "Prediction of subcellular localization as GO-term and protein features along sequence", "cmd": null } ], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_3307", "term": "Computational biology" }, { "uri": "http://edamontology.org/topic_3510", "term": "Protein sites, features and motifs" }, { "uri": "http://edamontology.org/topic_0123", "term": "Protein properties" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [], "license": null, "collectionID": [ "Bologna Biocomputing Group" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [ "Italy" ], "elixirCommunity": [], "link": [ { "url": "http://busca.biocomp.unibo.it", "type": [ "Other" ], "note": null }, { "url": "http://busca.biocomp.unibo.it/help", "type": [ "Helpdesk" ], "note": null } ], "download": [], "documentation": [ { "url": "http://busca.biocomp.unibo.it/help", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1093/nar/gky320", "pmid": "29718411", "pmcid": "PMC6031068", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "BUSCA: An integrative web server to predict subcellular localization of proteins", "abstract": "Here, we present BUSCA (http://busca.biocomp. unibo.it), a novel web server that integrates different computational tools for predicting protein subcellular localization. BUSCA combines methods for identifying signal and transit peptides (DeepSig and TPpred3), GPI-anchors (PredGPI) and transmembrane domains (ENSEMBLE3.0 and BetAware) with tools for discriminating subcellular localization of both globular and membrane proteins (BaCelLo, MemLoci and SChloro). Outcomes from the different tools are processed and integrated for annotating subcellular localization of both eukaryotic and bacterial protein sequences. We benchmark BUSCA against protein targets derived from recent CAFA experiments and other specific data sets, reporting performance at the state-of-the-art. BUSCA scores better than all other evaluated methods on 2732 targets from CAFA2, with a F1 value equal to 0.49 and among the best methods when predicting targets from CAFA3. We propose BUSCA as an integrated and accurate resource for the annotation of protein subcellular localization.", "date": "2018-07-02T00:00:00Z", "citationCount": 225, "authors": [ { "name": "Savojardo C." }, { "name": "Martelli P.L." }, { "name": "Fariselli P." }, { "name": "Profiti G." }, { "name": "Casadio R." } ], "journal": "Nucleic Acids Research" } } ], "credit": [ { "name": "ELIXIR-ITA-BOLOGNA", "email": null, "url": "http://biocomp.unibo.it", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Provider" ], "note": null }, { "name": "Castrense Savojardo", "email": "castrense.savojardo2@unibo.it", "url": null, "orcidid": "https://orcid.org/0000-0002-7359-0633", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": null }, { "name": "Castrense Savojardo", "email": "castrense.savojardo2@unibo.it", "url": "http://biocomp.unibo.it/savojard", "orcidid": "https://orcid.org/0000-0002-7359-0633", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null }, { "name": "Pier Luigi Martelli", "email": "pierluigi.martelli@unibo.it", "url": "http://biocomp.unibo.it", "orcidid": "https://orcid.org/0000-0002-0274-5669", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "community": null, "owner": "ELIXIR-ITA-BOLOGNA", "additionDate": "2018-05-28T14:38:24Z", "lastUpdate": "2024-03-07T17:52:55.021634Z", "editPermission": { "type": "group", "authors": [ "ELIXIR-ITA-BOLOGNA", "savo" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "DeepSig", "description": "Prediction of secretory signal peptides in protein sequences", "homepage": "https://deepsig.biocomp.unibo.it", "biotoolsID": "deepsig", "biotoolsCURIE": "biotools:deepsig", "version": [ "1.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0418", "term": "Protein signal peptide detection" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2974", "term": "Protein sequence (raw)" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] }, { "data": { "uri": "http://edamontology.org/data_3028", "term": "Taxonomy" }, "format": [ { "uri": "http://edamontology.org/format_2330", "term": "Textual format" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_0896", "term": "Protein report" }, "format": [ { "uri": "http://edamontology.org/format_2331", "term": "HTML" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Web application", "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3307", "term": "Computational biology" }, { "uri": "http://edamontology.org/topic_3510", "term": "Protein sites, features and motifs" }, { "uri": "http://edamontology.org/topic_0123", "term": "Protein properties" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [], "license": "GPL-3.0", "collectionID": [ "Bologna Biocomputing Group" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [ "Italy" ], "elixirCommunity": [], "link": [ { "url": "https://deepsig.biocomp.unibo.it", "type": [ "Other" ], "note": null }, { "url": "https://github.com/BolognaBiocomp/deepsig", "type": [ "Other" ], "note": null } ], "download": [ { "url": "https://github.com/BolognaBiocomp/deepsig", "type": "Source code", "note": null, "version": "1.2.5" }, { "url": "https://hub.docker.com/r/bolognabiocomp/deepsig", "type": "Container file", "note": null, "version": null } ], "documentation": [ { "url": "https://github.com/BolognaBiocomp/deepsig", "type": [ "Command-line options" ], "note": null } ], "publication": [ { "doi": "10.1093/bioinformatics/btx818", "pmid": "29280997", "pmcid": "PMC5946842", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "DeepSig: Deep learning improves signal peptide detection in proteins", "abstract": "Motivation The identification of signal peptides in protein sequences is an important step toward protein localization and function characterization. Results Here, we present DeepSig, an improved approach for signal peptide detection and cleavage-site prediction based on deep learning methods. Comparative benchmarks performed on an updated independent dataset of proteins show that DeepSig is the current best performing method, scoring better than other available state-of-the-art approaches on both signal peptide detection and precise cleavage-site identification. Availability and implementation DeepSig is available as both standalone program and web server at https://deepsig.biocomp.unibo.it. All datasets used in this study can be obtained from the same website.", "date": "2018-05-15T00:00:00Z", "citationCount": 76, "authors": [ { "name": "Savojardo C." }, { "name": "Martelli P.L." }, { "name": "Fariselli P." }, { "name": "Casadio R." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "ELIXIR-ITA-BOLOGNA", "email": null, "url": "http://biocomp.unibo.it", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Provider" ], "note": null }, { "name": "Castrense Savojardo", "email": "castrense.savojardo2@unibo.it", "url": null, "orcidid": "https://orcid.org/0000-0002-7359-0633", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": null }, { "name": "Castrense Savojardo", "email": "castrense.savojardo2@unibo.it", "url": "http://biocomp.unibo.it/savojard", "orcidid": "https://orcid.org/0000-0002-7359-0633", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null }, { "name": "Pier Luigi Martelli", "email": "pierluigi.martelli@unibo.it", "url": "http://biocomp.unibo.it", "orcidid": "https://orcid.org/0000-0002-0274-5669", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "community": null, "owner": "ELIXIR-ITA-BOLOGNA", "additionDate": "2018-05-28T14:50:09Z", "lastUpdate": "2024-03-07T11:15:41.300063Z", "editPermission": { "type": "group", "authors": [ "savo", "ELIXIR-ITA-BOLOGNA" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "GPCRsclass", "description": "Tool for predicting amine-binding receptors based on a protein sequence provided by the user.", "homepage": "http://webs.iiitd.edu.in/raghava/gpcrsclass/", "biotoolsID": "gpcrsclass", "biotoolsCURIE": "biotools:gpcrsclass", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2479", "term": "Protein sequence analysis" }, { "uri": "http://edamontology.org/operation_0269", "term": "Transmembrane protein prediction" }, { "uri": "http://edamontology.org/operation_3092", "term": "Protein feature detection" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_0078", "term": "Proteins" }, { "uri": "http://edamontology.org/topic_3510", "term": "Protein sites, features and motifs" }, { "uri": "http://edamontology.org/topic_0157", "term": "Sequence composition, complexity and repeats" }, { "uri": "http://edamontology.org/topic_0623", "term": "Gene and protein families" }, { "uri": "http://edamontology.org/topic_0082", "term": "Structure prediction" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [], "license": null, "collectionID": [ "gpcr" ], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [ { "url": "http://webs.iiitd.edu.in/raghava/gpcrsclass/help.html", "type": [ "General" ], "note": null } ], "publication": [ { "doi": null, "pmid": "15980444", "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "GPCRsclass: A web tool for the classification of amine type of G-protein-coupled receptors", "abstract": "The receptors of amine subfamily are specifically major drug targets for therapy of nervous disorders and psychiatric diseases. The recognition of novel amine type of receptors and their cognate ligands is of paramount interest for pharmaceutical companies. In the past, Chou and co-workers have shown that different types of amine receptors are correlated with their amino acid composition and are predictable on its basis with considerable accuracy [Elrod and Chou (2002) Protein Eng., 15, 713-715]. This motivated us to develop a better method for the recognition of novel amine receptors and for their further classification. The method was developed on the basis of amino acid composition and dipeptide composition of proteins using support vector machine. The method was trained and tested on 167 proteins of amine subfamily of G-protein-coupled receptors (GPCRs). The method discriminated amine subfamily of GPCRs from globular proteins with Matthew's correlation coefficient of 0.98 and 0.99 using amino acid composition and dipeptide composition, respectively. In classifying different types of amine receptors using amino acid composition and dipeptide composition, the method achieved an accuracy of 89.8 and 96.4%, respectively. The performance of the method was evaluated using 5-fold cross-validation. The dipeptide composition based method predicted 67.6% of protein sequences with an accuracy of 100% with a reliability index ≥5. A web server GPCRsclass has been developed for predicting amine-binding receptors from its amino acid sequence [http://www.imtech.res.in/raghava/gpcrsclass/ and http://bioinformatics.uams.edu/raghava/gpersclass/ (mirror site)]. © 2005 Oxford University Press.", "date": "2005-07-01T00:00:00Z", "citationCount": 57, "authors": [ { "name": "Bhasin M." }, { "name": "Raghava G.P.S." } ], "journal": "Nucleic Acids Research" } } ], "credit": [ { "name": "G.P.S. Raghava", "email": "raghava@iiitd.ac.in", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": "Prof Raghava is know to develop open source in bioinformatics" } ], "community": null, "owner": "raghavagps", "additionDate": "2017-02-10T14:15:37Z", "lastUpdate": "2024-02-15T12:37:17.336664Z", "editPermission": { "type": "group", "authors": [ "mbermudez1" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "GPCR-PEnDB", "description": "A database of protein sequences and derived features to facilitate prediction and classification of G protein-coupled receptors.\n\nTo analyze the sequence either upload a file with the sequences in FASTA format, or copy and paste the sequence(s) inside the text area below.", "homepage": "http://gpcr.utep.edu", "biotoolsID": "gpcr-pendb", "biotoolsCURIE": "biotools:gpcr-pendb", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0269", "term": "Transmembrane protein prediction" }, { "uri": "http://edamontology.org/operation_2995", "term": "Sequence classification" }, { "uri": "http://edamontology.org/operation_0253", "term": "Sequence feature detection" }, { "uri": "http://edamontology.org/operation_0224", "term": "Query and retrieval" }, { "uri": "http://edamontology.org/operation_3431", "term": "Deposition" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_0820", "term": "Membrane and lipoproteins" }, { "uri": "http://edamontology.org/topic_0623", "term": "Gene and protein families" }, { "uri": "http://edamontology.org/topic_3510", "term": "Protein sites, features and motifs" }, { "uri": "http://edamontology.org/topic_3300", "term": "Physiology" }, { "uri": "http://edamontology.org/topic_0154", "term": "Small molecules" } ], "operatingSystem": [], "language": [ "SQL" ], "license": null, "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1093/DATABASE/BAAA087", "pmid": "33216895", "pmcid": "PMC7678784", "type": [], "version": null, "note": null, "metadata": { "title": "GPCR-PEnDB: A database of protein sequences and derived features to facilitate prediction and classification of G protein-coupled receptors", "abstract": "© 2020 The Author(s). Published by Oxford University Press.G protein-coupled receptors (GPCRs) constitute the largest group of membrane receptor proteins in eukaryotes. Due to their significant roles in various physiological processes such as vision, smell and inflammation, GPCRs are the targets of many prescription drugs. However, the functional and sequence diversity of GPCRs has kept their prediction and classification based on amino acid sequence data as a challenging bioinformatics problem. There are existing computational approaches, mainly using machine learning and statistical methods, to predict and classify GPCRs based on amino acid sequence and sequence derived features. In this paper, we describe a searchable MySQL database, named GPCR-PEnDB (GPCR Prediction Ensemble Database), of confirmed GPCRs and non-GPCRs. It was constructed with the goal of allowing users to conveniently access useful information of GPCRs in a wide range of organisms and to compile reliable training and testing datasets for different combinations of computational tools. This database currently contains 3129 confirmed GPCR and 3575 non-GPCR sequences collected from the UniProtKB/Swiss-Prot protein database, encompassing over 1200 species. The non-GPCR entries include transmembrane proteins for evaluating various prediction programs' abilities to distinguish GPCRs from other transmembrane proteins. Each protein is linked to information about its source organism, classification, sequence lengths and composition, and other derived sequence features. We present examples of using this database along with its graphical user interface, to query for GPCRs with specific sequence properties and to compare the accuracies of five tools for GPCR prediction. This initial version of GPCR-PEnDB will provide a framework for future extensions to include additional sequence and feature data to facilitate the design and assessment of software tools and experimental studies to help understand the functional roles of GPCRs. Database URL: gpcr.utep.edu/database.", "date": "2020-01-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Begum K." }, { "name": "Mohl J.E." }, { "name": "Ayivor F." }, { "name": "Perez E.E." }, { "name": "Leung M.-Y." } ], "journal": "Database" } } ], "credit": [ { "name": "Khodeza Begum", "email": "kbegum@utep.edu", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Niclaskn", "additionDate": "2021-01-18T11:45:30Z", "lastUpdate": "2024-02-14T13:13:29.618153Z", "editPermission": { "type": "group", "authors": [ "mbermudez1" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "GPCRDB", "description": "GPCRdb contains reference data, interactive visualisation and experiment design tools for G protein-coupled receptors (GPCRs). GPCRdb curates sequence alignments, structures and receptor mutations from literature. Interactive diagrams visualise receptor residues (e.g. snakeplot and helix box plot) and relationships (e.g phylogenetic trees).", "homepage": "https://gpcrdb.org", "biotoolsID": "gpcrdb", "biotoolsCURIE": "biotools:gpcrdb", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0346", "term": "Sequence similarity search" }, { "uri": "http://edamontology.org/operation_0477", "term": "Protein modelling" }, { "uri": "http://edamontology.org/operation_2928", "term": "Alignment" }, { "uri": "http://edamontology.org/operation_0337", "term": "Visualisation" }, { "uri": "http://edamontology.org/operation_2451", "term": "Sequence comparison" }, { "uri": "http://edamontology.org/operation_0240", "term": "Sequence motif comparison" }, { "uri": "http://edamontology.org/operation_0291", "term": "Sequence clustering" }, { "uri": "http://edamontology.org/operation_2990", "term": "Classification" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_1460", "term": "Protein structure" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_2974", "term": "Protein sequence (raw)" }, "format": [] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_1381", "term": "Sequence alignment (pair)" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_0857", "term": "Sequence search results" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_1707", "term": "Phylogenetic tree image" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_0872", "term": "Phylogenetic tree" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_2884", "term": "Plot" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_2886", "term": "Protein sequence record" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_1277", "term": "Protein features" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_1460", "term": "Protein structure" }, "format": [] } ], "note": null, "cmd": null } ], "toolType": [ "Web application", "Database portal", "Web service" ], "topic": [ { "uri": "http://edamontology.org/topic_1317", "term": "Structural biology" }, { "uri": "http://edamontology.org/topic_3293", "term": "Phylogenetics" }, { "uri": "http://edamontology.org/topic_0080", "term": "Sequence analysis" }, { "uri": "http://edamontology.org/topic_3542", "term": "Protein secondary structure" }, { "uri": "http://edamontology.org/topic_3510", "term": "Protein sites, features and motifs" }, { "uri": "http://edamontology.org/topic_3534", "term": "Protein binding sites" }, { "uri": "http://edamontology.org/topic_2275", "term": "Molecular modelling" }, { "uri": "http://edamontology.org/topic_0082", "term": "Structure prediction" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "Python", "SQL" ], "license": null, "collectionID": [], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/protwis/protwis", "type": [ "Repository" ], "note": "Source of the framework that runs GPCRdb" }, { "url": "https://github.com/protwis/gpcrdb_data", "type": [ "Repository" ], "note": "Data repository for the GPCRdb" }, { "url": "https://github.com/protwis/protwis/issues", "type": [ "Issue tracker" ], "note": "Issue tracker for the GPCRdb" } ], "download": [], "documentation": [ { "url": "https://docs.gpcrdb.org", "type": [ "General" ], "note": "Documentation of the GPCRdb ranging from a general introduction to tool descriptions and development instructions." }, { "url": "https://gpcrdb.org/services/reference/", "type": [ "API documentation" ], "note": "Documentation for the REST web services" } ], "publication": [ { "doi": "10.1093/nar/gkv1178", "pmid": null, "pmcid": null, "type": [], "version": null, "note": "2016 GPCRdb publication", "metadata": { "title": "GPCRdb: An information system for G protein-coupled receptors", "abstract": "© The Author(s) 2015.Recent developments in G protein-coupled receptor (GPCR) structural biology and pharmacology have greatly enhanced our knowledge of receptor structure-function relations, and have helped improve the scientific foundation for drug design studies. The GPCR database, GPCRdb, serves a dual role in disseminating and enabling new scientific developments by providing reference data, analysis tools and interactive diagrams. This paper highlights new features in the fifth major GPCRdb release: (i) GPCR crystal structure browsing, superposition and display of ligand interactions; (ii) direct deposition by users of point mutations and their effects on ligand binding; (iii) refined snake and helix box residue diagram looks; and (iii) phylogenetic trees with receptor classification colour schemes. Under the hood, the entire GPCRdb front- and back-ends have been recoded within one infrastructure, ensuring a smooth browsing experience and development. GPCRdb is available at http://www.gpcrdb.org/ and it's open source code at https://bitbucket.org/gpcr/protwis.", "date": "2016-01-01T00:00:00Z", "citationCount": 196, "authors": [ { "name": "Isberg V." }, { "name": "Mordalski S." }, { "name": "Munk C." }, { "name": "Rataj K." }, { "name": "Harpsoe K." }, { "name": "Hauser A.S." }, { "name": "Vroling B." }, { "name": "Bojarski A.J." }, { "name": "Vriend G." }, { "name": "Gloriam D.E." } ], "journal": "Nucleic Acids Research" } }, { "doi": "10.1093/nar/gkaa1080", "pmid": null, "pmcid": null, "type": [ "Other" ], "version": null, "note": "2021 GPCRdb publication", "metadata": { "title": "GPCRdb in 2021: Integrating GPCR sequence, structure and function", "abstract": "© The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.G protein-coupled receptors (GPCRs) form both the largest family of membrane proteins and drug targets, mediating the action of one-third of medicines. The GPCR database, GPCRdb serves >4 000 researchers every month and offers reference data, analysis of own or literature data, experiment design and dissemination of published datasets. Here, we describe new and updated GPCRdb resources with a particular focus on integration of sequence, structure and function. GPCRdb contains all human non-olfactory GPCRs (and >27 000 orthologs), G-proteins and arrestins. It includes over 2 000 drug and in-trial agents and nearly 200 000 ligands with activity and availability data. GPCRdb annotates all published GPCR structures (updated monthly), which are also offered in a refined version (with re-modeled missing/distorted regions and reverted mutations) and provides structure models of all human non-olfactory receptors in inactive, intermediate and active states. Mutagenesis data in the GPCRdb spans natural genetic variants, GPCR-G protein interfaces, ligand sites and thermostabilising mutations. A new sequence signature tool for identification of functional residue determinants has been added and two data driven tools to design ligand site mutations and constructs for structure determination have been updated extending their coverage of receptors and modifications. The GPCRdb is available at https://gpcrdb.org.", "date": "2021-01-08T00:00:00Z", "citationCount": 129, "authors": [ { "name": "Kooistra A.J." }, { "name": "Mordalski S." }, { "name": "Pandy-Szekeres G." }, { "name": "Esguerra M." }, { "name": "Mamyrbekov A." }, { "name": "Munk C." }, { "name": "Keseru G.M." }, { "name": "Gloriam D.E." } ], "journal": "Nucleic Acids Research" } }, { "doi": "10.1093/nar/gkx1109", "pmid": null, "pmcid": null, "type": [], "version": null, "note": "2018 GPCRdb publication", "metadata": { "title": "GPCRdb in 2018: Adding GPCR structure models and ligands", "abstract": "© The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.G protein-coupled receptors are the most abundant mediators of both human signalling processes and therapeutic effects. Herein, we report GPCRome-wide homology models of unprecedented quality, and roughly 150 000 GPCR ligands with data on biological activities and commercial availability. Based on the strategy of 'Less model - more Xtal', each model exploits both a main template and alternative local templates. This achieved higher similarity to new structures than any of the existing resources, and refined crystal structures with missing or distorted regions. Models are provided for inactive, intermediate and active states - except for classes C and F that so far only have inactive templates. The ligand database has separate browsers for: (i) target selection by receptor, family or class, (ii) ligand filtering based on cross-experiment activities (min, max and mean) or chemical properties, (iii) ligand source data and (iv) commercial availability. SMILES structures and activity spreadsheets can be downloaded for further processing. Furthermore, three recent landmark publications on GPCR drugs, G protein selectivity and genetic variants have been accompanied with resources that now let readers view and analyse the findings themselves in GPCRdb. Altogether, this update will enable scientific investigation for the wider GPCR community. GPCRdb is available at http://www.gpcrdb.org.", "date": "2018-01-01T00:00:00Z", "citationCount": 299, "authors": [ { "name": "Pandy-Szekeres G." }, { "name": "Munk C." }, { "name": "Tsonkov T.M." }, { "name": "Mordalski S." }, { "name": "Harpsoe K." }, { "name": "Hauser A.S." }, { "name": "Bojarski A.J." }, { "name": "Gloriam D.E." } ], "journal": "Nucleic Acids Research" } }, { "doi": "10.1038/s41586-020-2888-2", "pmid": null, "pmcid": null, "type": [], "version": null, "note": "GPCR isoform resource as available in the GPCRdb", "metadata": { "title": "Combinatorial expression of GPCR isoforms affects signalling and drug responses", "abstract": "© 2020, The Author(s), under exclusive licence to Springer Nature Limited.G-protein-coupled receptors (GPCRs) are membrane proteins that modulate physiology across human tissues in response to extracellular signals. GPCR-mediated signalling can differ because of changes in the sequence1,2 or expression3 of the receptors, leading to signalling bias when comparing diverse physiological systems4. An underexplored source of such bias is the generation of functionally diverse GPCR isoforms with different patterns of expression across different tissues. Here we integrate data from human tissue-level transcriptomes, GPCR sequences and structures, proteomics, single-cell transcriptomics, population-wide genetic association studies and pharmacological experiments. We show how a single GPCR gene can diversify into several isoforms with distinct signalling properties, and how unique isoform combinations expressed in different tissues can generate distinct signalling states. Depending on their structural changes and expression patterns, some of the detected isoforms may influence cellular responses to drugs and represent new targets for developing drugs with improved tissue selectivity. Our findings highlight the need to move from a canonical to a context-specific view of GPCR signalling that considers how combinatorial expression of isoforms in a particular cell type, tissue or organism collectively influences receptor signalling and drug responses.", "date": "2020-11-26T00:00:00Z", "citationCount": 39, "authors": [ { "name": "Marti-Solano M." }, { "name": "Crilly S.E." }, { "name": "Malinverni D." }, { "name": "Munk C." }, { "name": "Harris M." }, { "name": "Pearce A." }, { "name": "Quon T." }, { "name": "Mackenzie A.E." }, { "name": "Wang X." }, { "name": "Peng J." }, { "name": "Tobin A.B." }, { "name": "Ladds G." }, { "name": "Milligan G." }, { "name": "Gloriam D.E." }, { "name": "Puthenveedu M.A." }, { "name": "Babu M.M." } ], "journal": "Nature" } }, { "doi": "10.1038/s41592-018-0302-x", "pmid": null, "pmcid": null, "type": [], "version": null, "note": "GPCR construct design tool as available in the GPCRdb", "metadata": { "title": "An online resource for GPCR structure determination and analysis", "abstract": "© 2019, Springer Nature America, Inc.G-protein-coupled receptors (GPCRs) transduce physiological and sensory stimuli into appropriate cellular responses and mediate the actions of one-third of drugs. GPCR structural studies have revealed the general bases of receptor activation, signaling, drug action and allosteric modulation, but so far cover only 13% of nonolfactory receptors. We broadly surveyed the receptor modifications/engineering and methods used to produce all available GPCR crystal and cryo-electron microscopy (cryo-EM) structures, and present an interactive resource integrated in GPCRdb (http://www.gpcrdb.org) to assist users in designing constructs and browsing appropriate experimental conditions for structure studies.", "date": "2019-02-01T00:00:00Z", "citationCount": 85, "authors": [ { "name": "Munk C." }, { "name": "Mutt E." }, { "name": "Isberg V." }, { "name": "Nikolajsen L.F." }, { "name": "Bibbe J.M." }, { "name": "Flock T." }, { "name": "Hanson M.A." }, { "name": "Stevens R.C." }, { "name": "Deupi X." }, { "name": "Gloriam D.E." } ], "journal": "Nature Methods" } }, { "doi": "10.1093/nar/gkac1013", "pmid": "36395823", "pmcid": null, "type": [ "Primary" ], "version": null, "note": "2023 GPCRdb publication", "metadata": null }, { "doi": "10.1038/s41594-021-00675-6", "pmid": "34759374", "pmcid": null, "type": [], "version": null, "note": "An online GPCR structure analysis platform", "metadata": { "title": "An online GPCR structure analysis platform", "abstract": "© 2021, The Author(s), under exclusive licence to Springer Nature America, Inc.We present an online, interactive platform for comparative analysis of all available G-protein coupled receptor (GPCR) structures while correlating to functional data. The comprehensive platform encompasses structure similarity, secondary structure, protein backbone packing and movement, residue–residue contact networks, amino acid properties and prospective design of experimental mutagenesis studies. This lets any researcher tap the potential of sophisticated structural analyses enabling a plethora of basic and applied receptor research studies.", "date": "2021-11-01T00:00:00Z", "citationCount": 3, "authors": [ { "name": "Kooistra A.J." }, { "name": "Munk C." }, { "name": "Hauser A.S." }, { "name": "Gloriam D.E." } ], "journal": "Nature Structural and Molecular Biology" } } ], "credit": [ { "name": "GPCRDB", "email": "info@gpcr.org", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Project", "typeRole": [ "Provider" ], "note": "Contact for general and support questions regarding the GPCRdb" }, { "name": "David Gloriam", "email": null, "url": "https://drug.ku.dk/staff/?pure=en/persons/352279", "orcidid": "https://orcid.org/0000-0002-4299-7561", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": "Head of the GPCRdb" } ], "community": null, "owner": "GPCRDB", "additionDate": "2015-02-04T09:25:19Z", "lastUpdate": "2024-02-14T13:13:19.740941Z", "editPermission": { "type": "group", "authors": [ "AJK_dev", "mbermudez1" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "Phobius (EBI)", "description": "Predict transmembrane topology and signal peptides from the amino acid sequence of a protein.", "homepage": "https://www.ebi.ac.uk/jdispatcher/pfa/phobius", "biotoolsID": "phobius", "biotoolsCURIE": "biotools:phobius", "version": [ "1" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0418", "term": "Protein signal peptide detection" }, { "uri": "http://edamontology.org/operation_0269", "term": "Transmembrane protein prediction" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2976", "term": "Protein sequence" }, "format": [] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_1277", "term": "Protein features" }, "format": [] } ], "note": null, "cmd": null } ], "toolType": [ "Web application", "Web service" ], "topic": [ { "uri": "http://edamontology.org/topic_0160", "term": "Sequence sites, features and motifs" }, { "uri": "http://edamontology.org/topic_0080", "term": "Sequence analysis" }, { "uri": "http://edamontology.org/topic_0820", "term": "Membrane and lipoproteins" }, { "uri": "http://edamontology.org/topic_0736", "term": "Protein folds and structural domains" }, { "uri": "http://edamontology.org/topic_3510", "term": "Protein sites, features and motifs" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [], "license": null, "collectionID": [ "EBI Tools", "Job Dispatcher Tools" ], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://www.ebi.ac.uk/about/contact/support/job-dispatcher-services", "type": [ "Helpdesk" ], "note": null } ], "download": [ { "url": "https://phobius.sbc.su.se/", "type": "Downloads page", "note": null, "version": null } ], "documentation": [ { "url": "http://www.ebi.ac.uk/about/terms-of-use", "type": [ "Terms of use" ], "note": null }, { "url": "https://www.ebi.ac.uk/jdispatcher/help", "type": [ "General" ], "note": null }, { "url": "https://phobius.sbc.su.se/", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1016/j.jmb.2004.03.016", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": null }, { "doi": "10.1093/nar/gkac240", "pmid": null, "pmcid": null, "type": [ "Other" ], "version": null, "note": null, "metadata": null } ], "credit": [ { "name": "Erik Sonnhammer", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": null }, { "name": "Stockholm Bioinformatics Center", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Provider" ], "note": null }, { "name": "EMBL-EBI", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Provider" ], "note": null }, { "name": "Job Dispatcher", "email": null, "url": "https://www.ebi.ac.uk/jdispatcher", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Project", "typeRole": [ "Primary contact" ], "note": null } ], "community": null, "owner": "jdispatcher", "additionDate": "2015-01-29T15:48:07Z", "lastUpdate": "2024-02-05T19:11:36.037096Z", "editPermission": { "type": "group", "authors": [ "nandana", "biomadeira" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "BaPreS", "description": "Predicting bacteriocins using an optimal set of features.", "homepage": "https://github.com/suraiya14/BaPreS", "biotoolsID": "bapres", "biotoolsCURIE": "biotools:bapres", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3482", "term": "Antimicrobial resistance prediction" }, { "uri": "http://edamontology.org/operation_3092", "term": "Protein feature detection" }, { "uri": "http://edamontology.org/operation_0253", "term": "Sequence feature detection" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Workflow" ], "topic": [ { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" }, { "uri": "http://edamontology.org/topic_0154", "term": "Small molecules" }, { "uri": "http://edamontology.org/topic_3510", "term": "Protein sites, features and motifs" } ], "operatingSystem": [], "language": [ "R", "Python" ], "license": "CC-BY-4.0", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://shiny.tricities.wsu.edu/bacteriocin-prediction/", "type": [ "Other" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1186/S12859-023-05330-Z", "pmid": "37592230", "pmcid": "PMC10433575", "type": [], "version": null, "note": null, "metadata": { "title": "BaPreS: a software tool for predicting bacteriocins using an optimal set of features", "abstract": "Background: Antibiotic resistance is a major public health concern around the globe. As a result, researchers always look for new compounds to develop new antibiotic drugs for combating antibiotic-resistant bacteria. Bacteriocin becomes a promising antimicrobial agent to fight against antibiotic resistance, due to cases of both broad and narrow killing spectra. Sequence matching methods are widely used to identify bacteriocins by comparing them with the known bacteriocin sequences; however, these methods often fail to detect new bacteriocin sequences due to their high diversity. The ability to use a machine learning approach can help find new highly dissimilar bacteriocins for developing highly effective antibiotic drugs. The aim of this work is to develop a machine learning-based software tool called BaPreS (Bacteriocin Prediction Software) using an optimal set of features for detecting bacteriocin protein sequences with high accuracy. We extracted potential features from known bacteriocin and non-bacteriocin sequences by considering the physicochemical and structural properties of the protein sequences. Then we reduced the feature set using statistical justifications and recursive feature elimination technique. Finally, we built support vector machine (SVM) and random forest (RF) models using the selected features and utilized the best machine learning model to implement the software tool. Results: We applied BaPreS to an established dataset and evaluated its prediction performance. Acquired results show that the software tool can achieve a prediction accuracy of 95.54% for testing protein sequences. This tool allows users to add new bacteriocin or non-bacteriocin sequences in the training dataset to further enhance the predictive power of the tool. We compared the prediction performance of the BaPreS with a popular sequence matching-based tool and a deep learning-based method, and our software tool outperformed both. Conclusions: BaPreS is a bacteriocin prediction tool that can be used to discover new highly dissimilar bacteriocins for developing highly effective antibiotic drugs. This software tool can be used with Windows, Linux and macOS operating systems. The open-source software package and its user manual are available at https://github.com/suraiya14/BaPreS .", "date": "2023-12-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Akhter S." }, { "name": "Miller J.H." } ], "journal": "BMC Bioinformatics" } } ], "credit": [ { "name": "Suraiya Akhter", "email": "suraiya.akhter@wsu.edu", "url": null, "orcidid": "https://orcid.org/0000-0001-6577-2034", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "John H. Miller", "email": "jmiller16@wsu.edu", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-01-29T07:22:06.494786Z", "lastUpdate": "2024-01-29T07:22:06.498387Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" } ] }