count: 377
list:
- accessibility: Open access
  additionDate: '2022-03-22T18:57:49.937151Z'
  biotoolsCURIE: biotools:proteinortho
  biotoolsID: proteinortho
  collectionID: []
  community: null
  confidence_flag: null
  cost: Free of charge
  credit:
  - email: null
    fundrefid: null
    gridid: null
    name: Marcus Lechner
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Sonja J Prohaska
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole: []
    url: null
  description: Proteinortho is a tool to detect orthologous genes within different
    species
  documentation: []
  download:
  - note: null
    type: Source code
    url: https://gitlab.com/paulklemm_PHD/proteinortho/-/archive/master/proteinortho-master.zip
    version: null
  - note: Installation with dpkg (root privileges are required)
    type: Downloads page
    url: https://packages.debian.org/unstable/proteinortho
    version: null
  editPermission:
    authors: []
    type: private
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  elixirNode: []
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  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Nucleic acid sequence
        uri: http://edamontology.org/data_2977
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    - data:
        term: Protein sequence
        uri: http://edamontology.org/data_2976
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: null
    operation:
    - term: Homology-based gene prediction
      uri: http://edamontology.org/operation_3663
    output:
    - data:
        term: Report
        uri: http://edamontology.org/data_2048
      format:
      - term: XML
        uri: http://edamontology.org/format_2332
      - term: HTML
        uri: http://edamontology.org/format_2331
  homepage: https://www.bioinf.uni-leipzig.de/Software/proteinortho/
  homepage_status: 0
  language:
  - Perl
  - C++
  - Python
  lastUpdate: '2024-03-12T09:10:35.206389Z'
  license: GPL-2.0
  link:
  - note: null
    type:
    - Repository
    url: https://gitlab.com/paulklemm_PHD/proteinortho
  - note: null
    type:
    - Issue tracker
    url: https://gitlab.com/paulklemm_PHD/proteinortho/-/issues?sort=created_date&state=opened
  - note: null
    type:
    - Galaxy service
    url: https://toolshed.g2.bx.psu.edu/repository?repository_id=584d8accff31aefe&changeset_revision=4850f0d15f01
  maturity: null
  name: Proteinortho
  operatingSystem:
  - Linux
  - Mac
  - Windows
  otherID: []
  owner: klemmp
  publication:
  - doi: 10.1186/1471-2105-12-124
    metadata:
      abstract: 'Background: Orthology analysis is an important part of data analysis
        in many areas of bioinformatics such as comparative genomics and molecular
        phylogenetics. The ever-increasing flood of sequence data, and hence the rapidly
        increasing number of genomes that can be compared simultaneously, calls for
        efficient software tools as brute-force approaches with quadratic memory requirements
        become infeasible in practise. The rapid pace at which new data become available,
        furthermore, makes it desirable to compute genome-wide orthology relations
        for a given dataset rather than relying on relations listed in databases.Results:
        The program Proteinortho described here is a stand-alone tool that is geared
        towards large datasets and makes use of distributed computing techniques when
        run on multi-core hardware. It implements an extended version of the reciprocal
        best alignment heuristic. We apply Proteinortho to compute orthologous proteins
        in the complete set of all 717 eubacterial genomes available at NCBI at the
        beginning of 2009. We identified thirty proteins present in 99% of all bacterial
        proteomes.Conclusions: Proteinortho significantly reduces the required amount
        of memory for orthology analysis compared to existing tools, allowing such
        computations to be performed on off-the-shelf hardware. © 2011 Lechner et
        al; licensee BioMed Central Ltd.'
      authors:
      - name: Lechner M.
      - name: Findeiss S.
      - name: Steiner L.
      - name: Marz M.
      - name: Stadler P.F.
      - name: Prohaska S.J.
      citationCount: 656
      date: '2011-04-28T00:00:00Z'
      journal: BMC Bioinformatics
      title: 'Proteinortho: Detection of (Co-)orthologs in large-scale analysis'
    note: null
    pmcid: PMC3114741
    pmid: '21526987'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Desktop application
  topic:
  - term: Phylogeny
    uri: http://edamontology.org/topic_0084
  validated: 1
  version:
  - 6.0.14
- accessibility: Open access
  additionDate: '2015-01-22T11:31:41Z'
  biotoolsCURIE: biotools:predgpi
  biotoolsID: predgpi
  collectionID:
  - Bologna Biocomputing Group
  community: null
  confidence_flag: null
  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: andrea@biocomp.unibo.it
    fundrefid: null
    gridid: null
    name: Andrea Pierleoni
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: casadio@biocomp.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
  - 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:
    - Maintainer
    url: null
  description: Prediction system for GPI-anchored proteins.
  documentation:
  - note: null
    type:
    - General
    url: http://gpcr.biocomp.unibo.it/predgpi
  download: []
  editPermission:
    authors:
    - Nimna
    type: group
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  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: Prediction
    operation:
    - term: Molecular surface analysis
      uri: http://edamontology.org/operation_3351
    output:
    - data:
        term: Protein report
        uri: http://edamontology.org/data_0896
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
  homepage: http://gpcr.biocomp.unibo.it/predgpi
  homepage_status: 0
  language: []
  lastUpdate: '2024-03-11T16:58:28.865213Z'
  license: null
  link: []
  maturity: Mature
  name: PredGPI
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1186/1471-2105-9-392
    metadata:
      abstract: 'Background: Several eukaryotic proteins associated to the extracellular
        leaflet of the plasma membrane carry a Glycosylphosphatidylinositol (GPI)
        anchor, which is linked to the C-terminal residue after a proteolytic cleavage
        occurring at the so called ω-site. Computational methods were developed to
        discriminate proteins that undergo this post-translational modification starting
        from their aminoacidic sequences. However more accurate methods are needed
        for a reliable annotation of whole proteomes. Results: Here we present PredGPI,
        a prediction method that, by coupling a Hidden Markov Model (HMM) and a Support
        Vector Machine (SVM), is able to efficiently predict both the presence of
        the GPI-anchor and the position of the ω-site. PredGPI is trained on a non-redundant
        dataset of experimentally characterized GPI-anchored proteins whose annotation
        was carefully checked in the literature. Conclusion: PredGPI outperforms all
        the other previously described methods and is able to correctly replicate
        the results of previously published high-throughput experiments. PredGPI reaches
        a lower rate of false positive predictions with respect to other available
        methods and it is therefore a costless, rapid and accurate method for screening
        whole proteomes. © 2008 Pierleoni et al; licensee BioMed Central Ltd.'
      authors:
      - name: Pierleoni A.
      - name: Martelli P.
      - name: Casadio R.
      citationCount: 463
      date: '2008-09-23T00:00:00Z'
      journal: BMC Bioinformatics
      title: 'PredGPI: A GPI-anchor predictor'
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Protein secondary structure
    uri: http://edamontology.org/topic_3542
  - term: Protein properties
    uri: http://edamontology.org/topic_0123
  validated: 1
  version:
  - '1.0'
- accessibility: Open access
  additionDate: '2016-01-22T15:53:12Z'
  biotoolsCURIE: biotools:tppred_2.0
  biotoolsID: tppred_2.0
  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:
    - Developer
    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: piero.fariselli@unito.it
    fundrefid: null
    gridid: null
    name: Piero Fariselli
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    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: null
  description: Mitochondrial targeting peptide prediction.
  documentation:
  - note: null
    type:
    - General
    url: https://tppred3.biocomp.unibo.it/tppred3/default/help
  - note: Installation instructions
    type:
    - Command-line options
    url: https://tppred2.biocomp.unibo.it/tppred2/default/software
  download:
  - note: null
    type: Source code
    url: http://biocomp.unibo.it/savojard/tppred2.tar.gz
    version: null
  editPermission:
    authors:
    - ELIXIR-ITA-BOLOGNA
    - savo
    type: group
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  elixirNode:
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  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: Prediction
    operation:
    - term: Protein subcellular localisation prediction
      uri: http://edamontology.org/operation_2489
    - term: Protein feature detection
      uri: http://edamontology.org/operation_3092
    - term: Sequence feature detection
      uri: http://edamontology.org/operation_0253
    - term: Protein cleavage site prediction
      uri: http://edamontology.org/operation_0422
    output:
    - data:
        term: Protein report
        uri: http://edamontology.org/data_0896
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
  homepage: https://tppred2.biocomp.unibo.it
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-03-11T16:57:44.699185Z'
  license: GPL-3.0
  link: []
  maturity: Mature
  name: TPpred 2.0
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1093/bioinformatics/btu411
    metadata: null
    note: null
    pmcid: null
    pmid: '24974200'
    type:
    - Primary
    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:
    - Primary
    version: null
  relation:
  - biotoolsID: tppred_1.0
    type: isNewVersionOf
  - biotoolsID: tppred_3.0
    type: hasNewVersion
  toolType:
  - Command-line tool
  - Web application
  topic:
  - term: Sequence sites, features and motifs
    uri: http://edamontology.org/topic_0160
  - term: Small molecules
    uri: http://edamontology.org/topic_0154
  - term: Protein targeting and localisation
    uri: http://edamontology.org/topic_0140
  validated: 1
  version:
  - '2.0'
- accessibility: Open access
  additionDate: '2015-01-22T11:31:40Z'
  biotoolsCURIE: biotools:memloci
  biotoolsID: memloci
  collectionID:
  - Bologna Biocomputing Group
  community: null
  confidence_flag: null
  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: andrea@biocomp.unibo.it
    fundrefid: null
    gridid: null
    name: Andrea Pierleoni
    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
  - 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:
    - Maintainer
    url: null
  description: Predictor for the subcellular localization of proteins associated or
    inserted in eukaryotes membranes.
  documentation:
  - note: null
    type:
    - General
    url: https://mu2py.biocomp.unibo.it/memloci/default/info
  download: []
  editPermission:
    authors: []
    type: private
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  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: Prediction
    operation:
    - term: Protein subcellular localisation prediction
      uri: http://edamontology.org/operation_2489
    output:
    - data:
        term: Protein features
        uri: http://edamontology.org/data_1277
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
  homepage: https://mu2py.biocomp.unibo.it/memloci
  homepage_status: 0
  language: []
  lastUpdate: '2024-03-11T16:57:20.943436Z'
  license: null
  link: []
  maturity: Mature
  name: MemLoci
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1093/bioinformatics/btr108
    metadata:
      abstract: 'Motivation: Subcellular localization is a key feature in the process
        of functional annotation of both globular and membrane proteins. In the absence
        of experimental data, protein localization is inferred on the basis of annotation
        transfer upon sequence similarity search. However, predictive tools are necessary
        when the localization of homologs is not known. This is so particularly for
        membrane proteins. Furthermore, most of the available predictors of subcellular
        localization are specifically trained on globular proteins and poorly perform
        on membrane proteins. Results: Here we develop MemLoci, a new support vector
        machinebased tool that discriminates three membrane protein localizations:
        plasma, internal and organelle membrane. When tested on an independent set,
        MemLoci outperforms existing methods, reaching an overall accuracy of 70%
        on predicting the location in the three membrane types, with a generalized
        correlation coefficient as high as 0.50. © The Author 2011. Published by Oxford
        University Press. All rights reserved.'
      authors:
      - name: Pierleoni A.
      - name: Martelli P.L.
      - name: Casadio R.
      citationCount: 47
      date: '2011-05-01T00:00:00Z'
      journal: Bioinformatics
      title: 'MemLoci: Predicting subcellular localization of membrane proteins in
        eukaryotes'
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Protein targeting and localisation
    uri: http://edamontology.org/topic_0140
  - term: Membrane and lipoproteins
    uri: http://edamontology.org/topic_0820
  - term: Model organisms
    uri: http://edamontology.org/topic_0621
  validated: 1
  version:
  - '1.0'
- accessibility: null
  additionDate: '2015-01-22T11:31:40Z'
  biotoolsCURIE: biotools:mempype
  biotoolsID: mempype
  collectionID:
  - Bologna Biocomputing Group
  community: null
  confidence_flag: null
  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: andrea@biocomp.unibo.it
    fundrefid: null
    gridid: null
    name: Andrea Pierleoni
    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: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    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:
    - Maintainer
    url: null
  description: Prediction of topology and subcellular localization of Eukaryotic membrane
    proteins.
  documentation:
  - note: null
    type:
    - General
    url: https://mu2py.biocomp.unibo.it/mempype/default/help
  download: []
  editPermission:
    authors: []
    type: private
  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: Prediction
    operation:
    - term: Protein secondary structure prediction (helices)
      uri: http://edamontology.org/operation_0468
    - term: Protein signal peptide detection
      uri: http://edamontology.org/operation_0418
    - term: Protein cleavage site prediction
      uri: http://edamontology.org/operation_0422
    - term: Protein subcellular localisation prediction
      uri: http://edamontology.org/operation_2489
    output:
    - data:
        term: Protein report
        uri: http://edamontology.org/data_0896
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
  homepage: https://mu2py.biocomp.unibo.it/mempype
  homepage_status: 0
  language: []
  lastUpdate: '2024-03-11T16:56:43.617506Z'
  license: null
  link: []
  maturity: Mature
  name: MemPype
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1093/nar/gkr282
    metadata:
      abstract: 'MemPype is a Python-based pipeline including previously published
        methods for the prediction of signal peptides (SPEP), glycophosphatidylinositol
        (GPI) anchors (PredGPI), all-alpha membrane topology (ENSEMBLE), and a recent
        method (MemLoci) that specifically discriminates the localization of eukaryotic
        membrane proteins in: ''cell membrane'', ''internal membranes'', ''organelle
        membranes''. MemLoci scores with accuracy of 70 and generalized correlation
        coefficient (GCC) of 0.50 on a rigorous homology-unbiased validation set and
        overpasses other predictors for subcellular localization. The annotation process
        is based both on inheritance through homology and computational methods. Each
        submitted protein first retrieves, when available, up to 25 similar proteins
        (with sequence identity ≥50 and alignment coverage ≥50 on both sequences).
        This helps the identification of membrane-associated proteins and detailed
        localization tags. Each protein is also filtered for the presence of a GPI
        anchor [0.8 false positive rate (FPR)]. A positive score of GPI anchor prediction
        labels the sequence as exposed to ''Cell surface''. Concomitantly the sequence
        is analysed for the presence of a signal peptide and classified with MemLoci
        into one of three discriminated classes. Finally the sequence is filtered
        for predicting its putative all-alpha protein membrane topology (FPR<1). The
        web server is available at: http://mu2py.biocomp.unibo.it/ mempype. © 2011
        The Author(s).'
      authors:
      - name: Pierleoni A.
      - name: Indio V.
      - name: Savojardo C.
      - name: Fariselli P.
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        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.
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        likely to be localized in mitochondria and plastids. TPpred predictions correlate
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        when available. TPpred was also trained and tested to predict the cleavage
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        This value is lower than the error reported by other methods currently available.
        © 2013 The Author.'
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      - name: Savojardo C.
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      abstract: 'Motivation: A tool for reliably predicting the impact of variations
        on protein stability is extremely important for both protein engineering and
        for understanding the effects of Mendelian and somatic mutations in the genome.
        Next Generation Sequencing studies are constantly increasing the number of
        protein sequences. Given the huge disproportion between protein sequences
        and structures, there is a need for tools suited to annotate the effect of
        mutations starting from protein sequence without relying on the structure.
        Here, we describe INPS, a novel approach for annotating the effect of non-synonymous
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        regression and it is trained to predict the thermodynamic free energy change
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        when tested in cross-validation on a non-redundant dataset. INPS performs
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        show that INPS predictions are complementary to those of the state-of-the-art,
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      abstract: 'Motivation: Protein function depends on its structural stability.
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        Recently, we introduced INPS, a method suited to predict the effect of variations
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        INPS scores slightly lower than the structure-based INPS3D and both on the
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      authors:
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      - name: Fariselli P.
      - name: Martelli P.L.
      - name: Casadio R.
      citationCount: 147
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      journal: Bioinformatics
      title: 'INPS-MD: A web server to predict stability of protein variants from
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  - term: Rare diseases
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  description: Support Vector Machines based Predictor of human Deleterious Single
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      abstract: 'Motivation: Human single nucleotide polymorphisms (SNPs) are the
        most frequent type of genetic variation in human population. One of the most
        important goals of SNP projects is to understand which human genotype variations
        are related to Mendelian and complex diseases. Great interest is focused on
        non-synonymous coding SNPs (nsSNPs) that are responsible of protein single
        point mutation. nsSNPs can be neutral or disease associated. It is known that
        the mutation of only one residue in a protein sequence can be related to a
        number of pathological conditions of dramatic social impact such as Altzheimer''s,
        Parkinson''s and Creutzfeldt-Jakob''s diseases. The quality and completeness
        of presently available SNPs databases allows the application of machine learning
        techniques to predict the insurgence of human diseases due to single point
        protein mutation starting from the protein sequence. Results: In this paper,
        we develop a method based on support vector machines (SVMs) that starting
        from the protein sequence information can predict whether a new phenotype
        derived from a nsSNP can be related to a genetic disease in humans. Using
        a dataset of 21 185 single point mutations, 61% of which are disease-related,
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        in the specific task of predicting whether a single point mutation can be
        disease related or not. Our method, although based on less information, outperforms
        other web-available predictors implementing different approaches. © 2006 Oxford
        University Press.'
      authors:
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      - name: Calabrese R.
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      citationCount: 664
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      journal: Bioinformatics
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    metadata:
      abstract: 'Motivation: A tool for reliably predicting the impact of variations
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        for understanding the effects of Mendelian and somatic mutations in the genome.
        Next Generation Sequencing studies are constantly increasing the number of
        protein sequences. Given the huge disproportion between protein sequences
        and structures, there is a need for tools suited to annotate the effect of
        mutations starting from protein sequence without relying on the structure.
        Here, we describe INPS, a novel approach for annotating the effect of non-synonymous
        mutations on the protein stability from its sequence. INPS is based on SVM
        regression and it is trained to predict the thermodynamic free energy change
        upon single-point variations in protein sequences. Results: We show that INPS
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        when tested in cross-validation on a non-redundant dataset. INPS performs
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        on protein stability from protein sequence and whose performance is competitive
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        a web server for the prediction of protein stability changes upon single point
        variation from protein sequence and/or structure. Here, we complement INPS
        with a new predictor (INPS3D) that exploits features derived from protein
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        values of 0.58 in cross validation and of 0.72 on a blind test set. The sequence-based
        INPS scores slightly lower than the structure-based INPS3D and both on the
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      - name: Martelli P.L.
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    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  description: Predictor of effects of single point protein mutation on its stability
    from protein sequences or structures.
  documentation:
  - note: null
    type:
    - General
    url: http://gpcr2.biocomp.unibo.it/~emidio/I-Mutant3.0/I-MutantSuite_Help.html
  download: []
  editPermission:
    authors:
    - ELIXIR-ITA-BOLOGNA
    - emidio
    type: group
  elixirCommunity: []
  elixirNode:
  - Italy
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: PDB ID
        uri: http://edamontology.org/data_1127
      format: []
    - data:
        term: Protein chain
        uri: http://edamontology.org/data_1467
      format: []
    - data:
        term: PDB residue number
        uri: http://edamontology.org/data_1742
      format: []
    note: Prediction from PDB
    operation:
    - term: Protein structure analysis
      uri: http://edamontology.org/operation_2406
    output:
    - data:
        term: Protein structure report
        uri: http://edamontology.org/data_1537
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
  - cmd: null
    input:
    - data:
        term: Protein sequence (raw)
        uri: http://edamontology.org/data_2974
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: Prediction from sequence
    operation:
    - term: Protein structure analysis
      uri: http://edamontology.org/operation_2406
    output:
    - data:
        term: Protein structure report
        uri: http://edamontology.org/data_1537
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
  - cmd: null
    input:
    - data:
        term: Protein sequence (raw)
        uri: http://edamontology.org/data_2974
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: Prediction of Disease
    operation:
    - term: Protein property calculation
      uri: http://edamontology.org/operation_0250
    output:
    - data:
        term: Disease report
        uri: http://edamontology.org/data_1622
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
  homepage: http://gpcr2.biocomp.unibo.it/cgi/predictors/I-Mutant3.0/I-Mutant3.0.cgi
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-03-11T16:51:11.386098Z'
  license: null
  link: []
  maturity: Mature
  name: I-MUTANT Suite
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: null
    metadata:
      abstract: 'I-Mutant2.0 is a support vector machine (SVM)-based tool for the
        automatic prediction of protein stability changes upon single point mutations.
        I-Mutant2.0 predictions are performed starting either from the protein structure
        or, more importantly, from the protein sequence. This latter task, to the
        best of our knowledge, is exploited for the first time. The method was trained
        and tested on a data set derived from ProTherm, which is presently the most
        comprehensive available database of thermodynamic experimental data of free
        energy changes of protein stability upon mutation under different conditions.
        I-Mutant2.0 can be used both as a classifier for predicting the sign of the
        protein stability change upon mutation and as a regression estimator for predicting
        the related ΔΔG values. Acting as a classifier, I-Mutant2.0 correctly predicts
        (with a cross-validation procedure) 80% or 77% of the data set, depending
        on the usage of structural or sequence information, respectively. When predicting
        ΔΔG values associated with mutations, the correlation of predicted with expected/
        experimental values is 0.71 (with a standard error of 1.30 kcal/mol) and 0.62
        (with a standard error of 1.45 kcal/mol) when structural or sequence information
        are respectively adopted. Our web interface allows the selection of a predictive
        mode that depends on the availability of the protein structure and/or sequence.
        In this latter case, the web server requires only pasting of a protein sequence
        in a raw format. We therefore introduce I-Mutant2.0 as a unique and valuable
        helper for protein design, even when the protein structure is not yet known
        with atomic resolution. Availability: http://gpcr.biocomp.unibo.it/cgi/ predictors/I-Mutant2.0/I-Mutant2.0.cgi.
        © 2005 Oxford University Press.'
      authors:
      - name: Capriotti E.
      - name: Fariselli P.
      - name: Casadio R.
      citationCount: 1396
      date: '2005-07-01T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'I-Mutant2.0: Predicting stability changes upon mutation from the protein
        sequence or structure'
    note: null
    pmcid: null
    pmid: '15980478'
    type: []
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Protein folding, stability and design
    uri: http://edamontology.org/topic_0130
  - term: DNA polymorphism
    uri: http://edamontology.org/topic_2885
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  - term: Protein structure analysis
    uri: http://edamontology.org/topic_2814
  - term: Structure prediction
    uri: http://edamontology.org/topic_0082
  - term: Proteins
    uri: http://edamontology.org/topic_0078
  validated: 1
  version:
  - '3.0'
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