count: 259
list:
- accessibility: null
  additionDate: '2024-04-18T13:12:26.515271Z'
  biotoolsCURIE: biotools:sno_dca
  biotoolsID: sno_dca
  collectionID: []
  community: null
  confidence_flag: tool
  cost: null
  credit:
  - email: jjh163yx@163.com
    fundrefid: null
    gridid: null
    name: Jianhua Jia
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: A model for predicting S-nitrosylation sites based on densely connected
    convolutional networks and attention mechanism.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: PTM site prediction
      uri: http://edamontology.org/operation_0417
    - term: Network analysis
      uri: http://edamontology.org/operation_3927
    - term: Feature extraction
      uri: http://edamontology.org/operation_3937
    - term: Query and retrieval
      uri: http://edamontology.org/operation_0224
    output: []
  homepage: https://sno.cangmang.xyz/SNO-DCA
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-04-18T13:12:26.517501Z'
  license: null
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/peanono/SNO-DCA
  maturity: null
  name: SNO-DCA
  operatingSystem: []
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1016/J.HELIYON.2023.E23187
    metadata:
      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.'
      authors:
      - name: Jia J.
      - name: Lv P.
      - name: Wei X.
      - name: Qiu W.
      citationCount: 0
      date: '2024-01-15T00:00:00Z'
      journal: Heliyon
      title: 'SNO-DCA: A model for predicting S-nitrosylation sites based on densely
        connected convolutional networks and attention mechanism'
    note: null
    pmcid: PMC10750070
    pmid: '38148797'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Protein modifications
    uri: http://edamontology.org/topic_0601
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: Protein sites, features and motifs
    uri: http://edamontology.org/topic_3510
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2015-01-22T11:31:41Z'
  biotoolsCURIE: biotools:snps_and_go
  biotoolsID: snps_and_go
  collectionID:
  - RD-connect
  - Rare Disease
  - Bologna Biocomputing Group
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: ELIXIR-ITA-BOLOGNA
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  - email: rita.casadio@unibo.it
    fundrefid: null
    gridid: null
    name: Rita Casadio
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: pierluigi.martelli@unibo.it
    fundrefid: null
    gridid: null
    name: Pier Luigi Martelli
    note: null
    orcidid: https://orcid.org/0000-0002-0274-5669
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: A server for the prediction of single point protein mutations likely
    to be involved in the insurgence of diseases in humans.s.
  documentation:
  - note: null
    type:
    - General
    url: http://snps-and-go.biocomp.unibo.it/snps-and-go/help2.htm
  download: []
  editPermission:
    authors:
    - ELIXIR-EE
    type: group
  elixirCommunity:
  - Rare Diseases
  elixirNode:
  - Italy
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: UniProt accession
        uri: http://edamontology.org/data_3021
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    note: Prediction
    operation:
    - term: Data retrieval
      uri: http://edamontology.org/operation_2422
    - term: Protein function analysis
      uri: http://edamontology.org/operation_2414
    - term: Variant classification
      uri: http://edamontology.org/operation_3225
    output:
    - data:
        term: Protein report
        uri: http://edamontology.org/data_0896
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
  homepage: http://snps-and-go.biocomp.unibo.it/snps-and-go/index.html
  homepage_status: 0
  language: []
  lastUpdate: '2024-03-11T16:55:42.141343Z'
  license: null
  link: []
  maturity: Mature
  name: SNPs and GO
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1002/humu.21047
    metadata:
      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.
      authors:
      - name: Calabrese R.
      - name: Capriotti E.
      - name: Fariselli P.
      - name: Martelli P.L.
      - name: Casadio R.
      citationCount: 486
      date: '2009-08-01T00:00:00Z'
      journal: Human Mutation
      title: Functional annotations improve the predictive score of human disease-related
        mutations in proteins
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  - term: Protein sites, features and motifs
    uri: http://edamontology.org/topic_3510
  - term: Data mining
    uri: http://edamontology.org/topic_3473
  - term: Rare diseases
    uri: http://edamontology.org/topic_3325
  validated: 1
  version:
  - '1.0'
- accessibility: Open access
  additionDate: '2016-01-22T15:50:02Z'
  biotoolsCURIE: biotools:tppred_3.0
  biotoolsID: tppred_3.0
  collectionID:
  - Bologna Biocomputing Group
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: ELIXIR-ITA-BOLOGNA
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  - email: castrense.savojardo2@unibo.it
    fundrefid: null
    gridid: null
    name: Castrense Savojardo
    note: null
    orcidid: https://orcid.org/0000-0002-7359-0633
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Developer
    url: http://biocomp.unibo.it/savojard/
  description: Organelle-targeting peptide detection and cleavage-site prediction.
  documentation:
  - note: null
    type:
    - General
    url: https://tppred3.biocomp.unibo.it/tppred3/default/help
  - note: null
    type:
    - Command-line options
    url: https://github.com/BolognaBiocomp/tppred3
  download:
  - note: null
    type: Source code
    url: https://github.com/BolognaBiocomp/tppred3
    version: null
  - note: null
    type: Container file
    url: https://hub.docker.com/r/bolognabiocomp/tppred3
    version: null
  editPermission:
    authors:
    - ELIXIR-ITA-BOLOGNA
    - savo
    type: group
  elixirCommunity: []
  elixirNode:
  - Italy
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein sequence (raw)
        uri: http://edamontology.org/data_2974
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: Predictio Protein sequence in FASTA format
    operation:
    - term: Protein subcellular localisation prediction
      uri: http://edamontology.org/operation_2489
    - term: Protein cleavage site prediction
      uri: http://edamontology.org/operation_0422
    - term: Protein feature detection
      uri: http://edamontology.org/operation_3092
    output:
    - data:
        term: Protein report
        uri: http://edamontology.org/data_0896
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
  homepage: http://tppred3.biocomp.unibo.it
  homepage_status: 0
  language: []
  lastUpdate: '2024-03-11T16:48:08.106769Z'
  license: GPL-3.0
  link: []
  maturity: Mature
  name: TPpred 3.0
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1093/bioinformatics/btv367
    metadata:
      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.'
      authors:
      - name: Savojardo C.
      - name: Martelli P.L.
      - name: Fariselli P.
      - name: Casadio R.
      citationCount: 38
      date: '2015-03-25T00:00:00Z'
      journal: Bioinformatics
      title: TPpred3 detects and discriminates mitochondrial and chloroplastic targeting
        peptides in eukaryotic proteins
    note: null
    pmcid: null
    pmid: '26079349'
    type:
    - Primary
    version: null
  - doi: 10.1093/bioinformatics/btu411
    metadata:
      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.'
      authors:
      - name: Savojardo C.
      - name: Martelli P.L.
      - name: Fariselli P.
      - name: Casadio R.
      citationCount: 31
      date: '2014-10-15T00:00:00Z'
      journal: Bioinformatics (Oxford, England)
      title: 'TPpred2: improving the prediction of mitochondrial targeting peptide
        cleavage sites by exploiting sequence motifs'
    note: null
    pmcid: null
    pmid: '24974200'
    type:
    - Other
    version: null
  - doi: 10.1093/bioinformatics/btt089
    metadata:
      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.'
      authors:
      - name: Indio V.
      - name: Martelli P.L.
      - name: Savojardo C.
      - name: Fariselli P.
      - name: Casadio R.
      citationCount: 14
      date: '2013-04-15T00:00:00Z'
      journal: Bioinformatics
      title: The prediction of organelle-targeting peptides in eukaryotic proteins
        with Grammatical-Restrained Hidden Conditional Random Fields
    note: null
    pmcid: null
    pmid: '23428638'
    type:
    - Other
    version: null
  - doi: 10.1007/978-1-0716-1262-0_28
    metadata:
      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.
      authors:
      - name: Martelli P.L.
      - name: Savojardo C.
      - name: Fariselli P.
      - name: Tartari G.
      - name: Casadio R.
      citationCount: 2
      date: '2021-01-01T00:00:00Z'
      journal: Methods in Molecular Biology
      title: Computer-Aided Prediction of Protein Mitochondrial Localization
    note: null
    pmcid: null
    pmid: '34118055'
    type:
    - Review
    - Benchmarking study
    version: null
  relation:
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    type: isNewVersionOf
  - biotoolsID: tppred_1.0
    type: isNewVersionOf
  toolType:
  - Web application
  - Command-line tool
  topic:
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: Protein sites, features and motifs
    uri: http://edamontology.org/topic_3510
  - term: Protein targeting and localisation
    uri: http://edamontology.org/topic_0140
  - term: Small molecules
    uri: http://edamontology.org/topic_0154
  validated: 1
  version:
  - '3'
- accessibility: Open access
  additionDate: '2018-05-28T14:38:24Z'
  biotoolsCURIE: biotools:busca
  biotoolsID: busca
  collectionID:
  - Bologna Biocomputing Group
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: ELIXIR-ITA-BOLOGNA
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: http://biocomp.unibo.it
  - email: castrense.savojardo2@unibo.it
    fundrefid: null
    gridid: null
    name: Castrense Savojardo
    note: null
    orcidid: https://orcid.org/0000-0002-7359-0633
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: castrense.savojardo2@unibo.it
    fundrefid: null
    gridid: null
    name: Castrense Savojardo
    note: null
    orcidid: https://orcid.org/0000-0002-7359-0633
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: http://biocomp.unibo.it/savojard
  - email: pierluigi.martelli@unibo.it
    fundrefid: null
    gridid: null
    name: Pier Luigi Martelli
    note: null
    orcidid: https://orcid.org/0000-0002-0274-5669
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: http://biocomp.unibo.it
  description: Prediction of protein subcellular localization from sequence
  documentation:
  - note: null
    type:
    - User manual
    url: http://busca.biocomp.unibo.it/help
  download: []
  editPermission:
    authors:
    - ELIXIR-ITA-BOLOGNA
    - savo
    type: group
  elixirCommunity: []
  elixirNode:
  - Italy
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein sequence (raw)
        uri: http://edamontology.org/data_2974
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    - data:
        term: Taxonomy
        uri: http://edamontology.org/data_3028
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    note: Prediction of subcellular localization as GO-term and protein features along
      sequence
    operation:
    - term: Protein subcellular localisation prediction
      uri: http://edamontology.org/operation_2489
    - term: Transmembrane protein prediction
      uri: http://edamontology.org/operation_0269
    - term: Protein feature detection
      uri: http://edamontology.org/operation_3092
    - term: Protein signal peptide detection
      uri: http://edamontology.org/operation_0418
    output:
    - data:
        term: Protein features
        uri: http://edamontology.org/data_1277
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
      - term: JSON
        uri: http://edamontology.org/format_3464
      - term: TSV
        uri: http://edamontology.org/format_3475
  homepage: http://busca.biocomp.unibo.it
  homepage_status: 0
  language: []
  lastUpdate: '2024-03-07T17:52:55.021634Z'
  license: null
  link:
  - note: null
    type:
    - Other
    url: http://busca.biocomp.unibo.it
  - note: null
    type:
    - Helpdesk
    url: http://busca.biocomp.unibo.it/help
  maturity: Mature
  name: BUSCA
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1093/nar/gky320
    metadata:
      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.
      authors:
      - name: Savojardo C.
      - name: Martelli P.L.
      - name: Fariselli P.
      - name: Profiti G.
      - name: Casadio R.
      citationCount: 225
      date: '2018-07-02T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'BUSCA: An integrative web server to predict subcellular localization
        of proteins'
    note: null
    pmcid: PMC6031068
    pmid: '29718411'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Computational biology
    uri: http://edamontology.org/topic_3307
  - term: Protein sites, features and motifs
    uri: http://edamontology.org/topic_3510
  - term: Protein properties
    uri: http://edamontology.org/topic_0123
  validated: 1
  version:
  - '1.0'
- accessibility: Open access
  additionDate: '2018-05-28T14:50:09Z'
  biotoolsCURIE: biotools:deepsig
  biotoolsID: deepsig
  collectionID:
  - Bologna Biocomputing Group
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: ELIXIR-ITA-BOLOGNA
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: http://biocomp.unibo.it
  - email: castrense.savojardo2@unibo.it
    fundrefid: null
    gridid: null
    name: Castrense Savojardo
    note: null
    orcidid: https://orcid.org/0000-0002-7359-0633
    rorid: null
    typeEntity: Person
    typeRole:
    - Developer
    url: null
  - email: castrense.savojardo2@unibo.it
    fundrefid: null
    gridid: null
    name: Castrense Savojardo
    note: null
    orcidid: https://orcid.org/0000-0002-7359-0633
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: http://biocomp.unibo.it/savojard
  - email: pierluigi.martelli@unibo.it
    fundrefid: null
    gridid: null
    name: Pier Luigi Martelli
    note: null
    orcidid: https://orcid.org/0000-0002-0274-5669
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: http://biocomp.unibo.it
  description: Prediction of secretory signal peptides in protein sequences
  documentation:
  - note: null
    type:
    - Command-line options
    url: https://github.com/BolognaBiocomp/deepsig
  download:
  - note: null
    type: Source code
    url: https://github.com/BolognaBiocomp/deepsig
    version: 1.2.5
  - note: null
    type: Container file
    url: https://hub.docker.com/r/bolognabiocomp/deepsig
    version: null
  editPermission:
    authors:
    - savo
    - ELIXIR-ITA-BOLOGNA
    type: group
  elixirCommunity: []
  elixirNode:
  - Italy
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein sequence (raw)
        uri: http://edamontology.org/data_2974
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    - data:
        term: Taxonomy
        uri: http://edamontology.org/data_3028
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    note: null
    operation:
    - term: Protein signal peptide detection
      uri: http://edamontology.org/operation_0418
    output:
    - data:
        term: Protein report
        uri: http://edamontology.org/data_0896
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
  homepage: https://deepsig.biocomp.unibo.it
  homepage_status: 0
  language: []
  lastUpdate: '2024-03-07T11:15:41.300063Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Other
    url: https://deepsig.biocomp.unibo.it
  - note: null
    type:
    - Other
    url: https://github.com/BolognaBiocomp/deepsig
  maturity: Mature
  name: DeepSig
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1093/bioinformatics/btx818
    metadata:
      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.
      authors:
      - name: Savojardo C.
      - name: Martelli P.L.
      - name: Fariselli P.
      - name: Casadio R.
      citationCount: 76
      date: '2018-05-15T00:00:00Z'
      journal: Bioinformatics
      title: 'DeepSig: Deep learning improves signal peptide detection in proteins'
    note: null
    pmcid: PMC5946842
    pmid: '29280997'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  - Command-line tool
  topic:
  - term: Computational biology
    uri: http://edamontology.org/topic_3307
  - term: Protein sites, features and motifs
    uri: http://edamontology.org/topic_3510
  - term: Protein properties
    uri: http://edamontology.org/topic_0123
  validated: 1
  version:
  - '1.0'
- accessibility: null
  additionDate: '2017-02-10T14:15:37Z'
  biotoolsCURIE: biotools:gpcrsclass
  biotoolsID: gpcrsclass
  collectionID:
  - gpcr
  community: null
  confidence_flag: null
  cost: null
  credit:
  - email: raghava@iiitd.ac.in
    fundrefid: null
    gridid: null
    name: G.P.S. Raghava
    note: Prof Raghava is know to develop open source in bioinformatics
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Tool for predicting amine-binding receptors based on a protein sequence
    provided by the user.
  documentation:
  - note: null
    type:
    - General
    url: http://webs.iiitd.edu.in/raghava/gpcrsclass/help.html
  download: []
  editPermission:
    authors:
    - mbermudez1
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Protein sequence analysis
      uri: http://edamontology.org/operation_2479
    - term: Transmembrane protein prediction
      uri: http://edamontology.org/operation_0269
    - term: Protein feature detection
      uri: http://edamontology.org/operation_3092
    output: []
  homepage: http://webs.iiitd.edu.in/raghava/gpcrsclass/
  homepage_status: 0
  language: []
  lastUpdate: '2024-02-15T12:37:17.336664Z'
  license: null
  link: []
  maturity: null
  name: GPCRsclass
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: raghavagps
  publication:
  - doi: null
    metadata:
      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.
      authors:
      - name: Bhasin M.
      - name: Raghava G.P.S.
      citationCount: 57
      date: '2005-07-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'GPCRsclass: A web tool for the classification of amine type of G-protein-coupled
        receptors'
    note: null
    pmcid: null
    pmid: '15980444'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Proteins
    uri: http://edamontology.org/topic_0078
  - term: Protein sites, features and motifs
    uri: http://edamontology.org/topic_3510
  - term: Sequence composition, complexity and repeats
    uri: http://edamontology.org/topic_0157
  - term: Gene and protein families
    uri: http://edamontology.org/topic_0623
  - term: Structure prediction
    uri: http://edamontology.org/topic_0082
  validated: 1
  version: []
- accessibility: null
  additionDate: '2021-01-18T11:45:30Z'
  biotoolsCURIE: biotools:gpcr-pendb
  biotoolsID: gpcr-pendb
  collectionID: []
  community: null
  confidence_flag: tool
  cost: null
  credit:
  - email: kbegum@utep.edu
    fundrefid: null
    gridid: null
    name: Khodeza Begum
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: 'A database of protein sequences and derived features to facilitate
    prediction and classification of G protein-coupled receptors.


    To 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.'
  documentation: []
  download: []
  editPermission:
    authors:
    - mbermudez1
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Transmembrane protein prediction
      uri: http://edamontology.org/operation_0269
    - term: Sequence classification
      uri: http://edamontology.org/operation_2995
    - term: Sequence feature detection
      uri: http://edamontology.org/operation_0253
    - term: Query and retrieval
      uri: http://edamontology.org/operation_0224
    - term: Deposition
      uri: http://edamontology.org/operation_3431
    output: []
  homepage: http://gpcr.utep.edu
  homepage_status: 0
  language:
  - SQL
  lastUpdate: '2024-02-14T13:13:29.618153Z'
  license: null
  link: []
  maturity: null
  name: GPCR-PEnDB
  operatingSystem: []
  otherID: []
  owner: Niclaskn
  publication:
  - doi: 10.1093/DATABASE/BAAA087
    metadata:
      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.'
      authors:
      - name: Begum K.
      - name: Mohl J.E.
      - name: Ayivor F.
      - name: Perez E.E.
      - name: Leung M.-Y.
      citationCount: 0
      date: '2020-01-01T00:00:00Z'
      journal: Database
      title: 'GPCR-PEnDB: A database of protein sequences and derived features to
        facilitate prediction and classification of G protein-coupled receptors'
    note: null
    pmcid: PMC7678784
    pmid: '33216895'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Membrane and lipoproteins
    uri: http://edamontology.org/topic_0820
  - term: Gene and protein families
    uri: http://edamontology.org/topic_0623
  - term: Protein sites, features and motifs
    uri: http://edamontology.org/topic_3510
  - term: Physiology
    uri: http://edamontology.org/topic_3300
  - term: Small molecules
    uri: http://edamontology.org/topic_0154
  validated: 1
  version: []
- accessibility: Open access
  additionDate: '2015-02-04T09:25:19Z'
  biotoolsCURIE: biotools:gpcrdb
  biotoolsID: gpcrdb
  collectionID: []
  community: null
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: info@gpcr.org
    fundrefid: null
    gridid: null
    name: GPCRDB
    note: Contact for general and support questions regarding the GPCRdb
    orcidid: null
    rorid: null
    typeEntity: Project
    typeRole:
    - Provider
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: David Gloriam
    note: Head of the GPCRdb
    orcidid: https://orcid.org/0000-0002-4299-7561
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: https://drug.ku.dk/staff/?pure=en/persons/352279
  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).
  documentation:
  - note: Documentation of the GPCRdb ranging from a general introduction to tool
      descriptions and development instructions.
    type:
    - General
    url: https://docs.gpcrdb.org
  - note: Documentation for the REST web services
    type:
    - API documentation
    url: https://gpcrdb.org/services/reference/
  download: []
  editPermission:
    authors:
    - AJK_dev
    - mbermudez1
    type: group
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein structure
        uri: http://edamontology.org/data_1460
      format: []
    - data:
        term: Protein sequence (raw)
        uri: http://edamontology.org/data_2974
      format: []
    note: null
    operation:
    - term: Sequence similarity search
      uri: http://edamontology.org/operation_0346
    - term: Protein modelling
      uri: http://edamontology.org/operation_0477
    - term: Alignment
      uri: http://edamontology.org/operation_2928
    - term: Visualisation
      uri: http://edamontology.org/operation_0337
    - term: Sequence comparison
      uri: http://edamontology.org/operation_2451
    - term: Sequence motif comparison
      uri: http://edamontology.org/operation_0240
    - term: Sequence clustering
      uri: http://edamontology.org/operation_0291
    - term: Classification
      uri: http://edamontology.org/operation_2990
    output:
    - data:
        term: Sequence alignment (pair)
        uri: http://edamontology.org/data_1381
      format: []
    - data:
        term: Sequence search results
        uri: http://edamontology.org/data_0857
      format: []
    - data:
        term: Phylogenetic tree image
        uri: http://edamontology.org/data_1707
      format: []
    - data:
        term: Phylogenetic tree
        uri: http://edamontology.org/data_0872
      format: []
    - data:
        term: Plot
        uri: http://edamontology.org/data_2884
      format: []
    - data:
        term: Protein sequence record
        uri: http://edamontology.org/data_2886
      format: []
    - data:
        term: Protein features
        uri: http://edamontology.org/data_1277
      format: []
    - data:
        term: Protein structure
        uri: http://edamontology.org/data_1460
      format: []
  homepage: https://gpcrdb.org
  homepage_status: 0
  language:
  - Python
  - SQL
  lastUpdate: '2024-02-14T13:13:19.740941Z'
  license: null
  link:
  - note: Source of the framework that runs GPCRdb
    type:
    - Repository
    url: https://github.com/protwis/protwis
  - note: Data repository for the GPCRdb
    type:
    - Repository
    url: https://github.com/protwis/gpcrdb_data
  - note: Issue tracker for the GPCRdb
    type:
    - Issue tracker
    url: https://github.com/protwis/protwis/issues
  maturity: Mature
  name: GPCRDB
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: GPCRDB
  publication:
  - doi: 10.1093/nar/gkv1178
    metadata:
      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.'
      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.
      citationCount: 196
      date: '2016-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'GPCRdb: An information system for G protein-coupled receptors'
    note: 2016 GPCRdb publication
    pmcid: null
    pmid: null
    type: []
    version: null
  - doi: 10.1093/nar/gkaa1080
    metadata:
      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.
      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.
      citationCount: 129
      date: '2021-01-08T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'GPCRdb in 2021: Integrating GPCR sequence, structure and function'
    note: 2021 GPCRdb publication
    pmcid: null
    pmid: null
    type:
    - Other
    version: null
  - doi: 10.1093/nar/gkx1109
    metadata:
      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.'
      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.
      citationCount: 299
      date: '2018-01-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'GPCRdb in 2018: Adding GPCR structure models and ligands'
    note: 2018 GPCRdb publication
    pmcid: null
    pmid: null
    type: []
    version: null
  - doi: 10.1038/s41586-020-2888-2
    metadata:
      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.
      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.
      citationCount: 39
      date: '2020-11-26T00:00:00Z'
      journal: Nature
      title: Combinatorial expression of GPCR isoforms affects signalling and drug
        responses
    note: GPCR isoform resource as available in the GPCRdb
    pmcid: null
    pmid: null
    type: []
    version: null
  - doi: 10.1038/s41592-018-0302-x
    metadata:
      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.
      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.
      citationCount: 85
      date: '2019-02-01T00:00:00Z'
      journal: Nature Methods
      title: An online resource for GPCR structure determination and analysis
    note: GPCR construct design tool as available in the GPCRdb
    pmcid: null
    pmid: null
    type: []
    version: null
  - doi: 10.1093/nar/gkac1013
    metadata: null
    note: 2023 GPCRdb publication
    pmcid: null
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    type:
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    version: null
  - doi: 10.1038/s41594-021-00675-6
    metadata:
      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.
      authors:
      - name: Kooistra A.J.
      - name: Munk C.
      - name: Hauser A.S.
      - name: Gloriam D.E.
      citationCount: 3
      date: '2021-11-01T00:00:00Z'
      journal: Nature Structural and Molecular Biology
      title: An online GPCR structure analysis platform
    note: An online GPCR structure analysis platform
    pmcid: null
    pmid: '34759374'
    type: []
    version: null
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  topic:
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  - term: Phylogenetics
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  - term: Sequence analysis
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  - term: Protein secondary structure
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  - term: Protein sites, features and motifs
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  - term: Protein binding sites
    uri: http://edamontology.org/topic_3534
  - term: Molecular modelling
    uri: http://edamontology.org/topic_2275
  - term: Structure prediction
    uri: http://edamontology.org/topic_0082
  validated: 1
  version: []
- accessibility: null
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  biotoolsCURIE: biotools:phobius
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    name: Erik Sonnhammer
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    name: Stockholm Bioinformatics Center
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  description: Predict transmembrane topology and signal peptides from the amino acid
    sequence of a protein.
  documentation:
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    - Terms of use
    url: http://www.ebi.ac.uk/about/terms-of-use
  - note: null
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    input:
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    operation:
    - term: Protein signal peptide detection
      uri: http://edamontology.org/operation_0418
    - term: Transmembrane protein prediction
      uri: http://edamontology.org/operation_0269
    output:
    - data:
        term: Protein features
        uri: http://edamontology.org/data_1277
      format: []
  homepage: https://www.ebi.ac.uk/jdispatcher/pfa/phobius
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    metadata: null
    note: null
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    type:
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    version: null
  - doi: 10.1093/nar/gkac240
    metadata: null
    note: null
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  topic:
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    uri: http://edamontology.org/topic_0160
  - term: Sequence analysis
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  - term: Membrane and lipoproteins
    uri: http://edamontology.org/topic_0820
  - term: Protein folds and structural domains
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  - term: Protein sites, features and motifs
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    fundrefid: null
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  - email: jmiller16@wsu.edu
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    gridid: null
    name: John H. Miller
    note: null
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    url: null
  description: Predicting bacteriocins using an optimal set of features.
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  function:
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    operation:
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      uri: http://edamontology.org/operation_3482
    - term: Protein feature detection
      uri: http://edamontology.org/operation_3092
    - term: Sequence feature detection
      uri: http://edamontology.org/operation_0253
    output: []
  homepage: https://github.com/suraiya14/BaPreS
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  language:
  - R
  - Python
  lastUpdate: '2024-01-29T07:22:06.498387Z'
  license: CC-BY-4.0
  link:
  - note: null
    type:
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    url: https://shiny.tricities.wsu.edu/bacteriocin-prediction/
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  publication:
  - doi: 10.1186/S12859-023-05330-Z
    metadata:
      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 .'
      authors:
      - name: Akhter S.
      - name: Miller J.H.
      citationCount: 0
      date: '2023-12-01T00:00:00Z'
      journal: BMC Bioinformatics
      title: 'BaPreS: a software tool for predicting bacteriocins using an optimal
        set of features'
    note: null
    pmcid: PMC10433575
    pmid: '37592230'
    type: []
    version: null
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  - term: Small molecules
    uri: http://edamontology.org/topic_0154
  - term: Protein sites, features and motifs
    uri: http://edamontology.org/topic_3510
  validated: 0
  version: []
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