count: 265
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: []
  elixirPlatform: []
  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: '2022-09-01T11:34:17.973845Z'
  biotoolsCURIE: biotools:svmyr
  biotoolsID: svmyr
  collectionID:
  - Bologna Biocomputing Group
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - 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
    - Maintainer
    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: null
    fundrefid: null
    gridid: null
    name: ELIXIR-ITA-BOLOGNA
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  description: A Web Server Detecting Co- and Post-translational Myristoylation in
    Proteins.
  documentation:
  - note: null
    type:
    - User manual
    url: http://busca.biocomp.unibo.it/lipipred/about/
  download: []
  editPermission:
    authors:
    - ELIXIR-ITA-BOLOGNA
    - savo
    type: group
  elixirCommunity: []
  elixirNode:
  - Italy
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein sequence
        uri: http://edamontology.org/data_2976
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: null
    operation:
    - term: PTM site prediction
      uri: http://edamontology.org/operation_0417
    - 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
      - term: JSON
        uri: http://edamontology.org/format_3464
      - term: TSV
        uri: http://edamontology.org/format_3475
  homepage: https://busca.biocomp.unibo.it/lipipred
  homepage_status: 0
  language: []
  lastUpdate: '2024-03-11T16:39:58.647418Z'
  license: null
  link: []
  maturity: Mature
  name: SVMyr
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1016/J.JMB.2022.167605
    metadata:
      abstract: Myristoylation (MYR) is a protein modification where a myristoyl group
        is covalently attached to an exposed (N-terminal) glycine residue. Glycine
        myristoylation occurs during protein translation (co-translation) or after
        (post-translation). Myristoylated proteins have a role in signal transduction,
        apoptosis, and pathogen-mediated processes and their prediction can help in
        functionally annotating the fraction of proteins undergoing MYR in different
        proteomes. Here we present SVMyr, a web server allowing the detection of both
        co- and post-translational myristoylation sites, based on Support Vector Machines
        (SVM). The input encodes composition and physicochemical features of the octapeptides,
        known to act as substrates and to physically interact with N-myristoyltransferases
        (NMTs), the enzymes catalyzing the myristoylation reaction. The method, adopting
        a cross validation procedure, scores with values of Area Under the Curve (AUC)
        and Matthews Correlation Coefficient (MCC) of 0.92 and 0.61, respectively.
        When benchmarked on an independent dataset including experimentally detected
        88 medium/high confidence co-translational myristoylation sites and 528 negative
        examples, SVMyr outperforms available methods, with AUC and MCC equal to 0.91
        and 0.58, respectively. A unique feature of SVMyr is the ability to predict
        post-translational myristoylation sites by coupling the trained SVMs with
        the detection of caspase cleavage sites, identified by searching regular motifs
        matching upstream caspase cleavage sites, as reported in literature. Finally,
        SVMyr confirms 96% of the UniProt set of the electronically annotated myristoylated
        proteins (31,048) and identifies putative myristoylomes in eight different
        proteomes, highlighting also new putative NMT substrates. SVMyr is freely
        available through a user-friendly web server at https://busca.biocomp.unibo.it/lipipred.
      authors:
      - name: Madeo G.
      - name: Savojardo C.
      - name: Martelli P.L.
      - name: Casadio R.
      citationCount: 5
      date: '2022-06-15T00:00:00Z'
      journal: Journal of Molecular Biology
      title: 'SVMyr: A Web Server Detecting Co- and Post-translational Myristoylation
        in Proteins'
    note: null
    pmcid: null
    pmid: '35662454'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Protein modifications
    uri: http://edamontology.org/topic_0601
  - term: Lipids
    uri: http://edamontology.org/topic_0153
  - term: Sequence sites, features and motifs
    uri: http://edamontology.org/topic_0160
  validated: 1
  version:
  - '1.0'
- accessibility: Open access
  additionDate: '2024-01-28T18:03:20.705547Z'
  biotoolsCURIE: biotools:coconat
  biotoolsID: coconat
  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
    - Maintainer
    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: CoCoNat is a novel deep-learning based method for predicting coiled-coil
    regions, register annotation and oligomenrization state. CoCoNat adopts a sequence
    encoding based on two state-of-the-art protein language models and a deep-learning
    architetcure to perform prediction.
  documentation:
  - note: null
    type:
    - Command-line options
    url: https://github.com/BolognaBiocomp/coconat
  - note: null
    type:
    - User manual
    url: https://coconat.biocomp.unibo.it/help/
  download:
  - note: null
    type: Source code
    url: https://github.com/BolognaBiocomp/coconat
    version: '1.0'
  editPermission:
    authors:
    - ELIXIR-ITA-BOLOGNA
    - savo
    type: group
  elixirCommunity: []
  elixirNode:
  - Italy
  elixirPlatform:
  - Tools
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein sequence
        uri: http://edamontology.org/data_2976
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: null
    operation:
    - term: Protein super-secondary structure prediction
      uri: http://edamontology.org/operation_0268
    output:
    - data:
        term: Protein report
        uri: http://edamontology.org/data_0896
      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: https://coconat.biocomp.unibo.it
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-03-11T16:30:11.896748Z'
  license: GPL-3.0
  link: []
  maturity: Mature
  name: CoCoNat
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1093/bioinformatics/btad495
    metadata:
      abstract: 'Motivation: Coiled-coil domains (CCD) are widespread in all organisms
        and perform several crucial functions. Given their relevance, the computational
        detection of CCD is very important for protein functional annotation. State-of-the-art
        prediction methods include the precise identification of CCD boundaries, the
        annotation of the typical heptad repeat pattern along the coiled-coil helices
        as well as the prediction of the oligomerization state. Results: In this article,
        we describe CoCoNat, a novel method for predicting coiled-coil helix boundaries,
        residue-level register annotation, and oligomerization state. Our method encodes
        sequences with the combination of two state-of-the-art protein language models
        and implements a three-step deep learning procedure concatenated with a Grammatical-Restrained
        Hidden Conditional Random Field for CCD identification and refinement. A final
        neural network predicts the oligomerization state. When tested on a blind
        test set routinely adopted, CoCoNat obtains a performance superior to the
        current state-of-the-art both for residue-level and segment-level CCD. CoCoNat
        significantly outperforms the most recent state-of-the-art methods on register
        annotation and prediction of oligomerization states.'
      authors:
      - name: Madeo G.
      - name: Savojardo C.
      - name: Manfredi M.
      - name: Martelli P.L.
      - name: Casadio R.
      citationCount: 1
      date: '2023-08-01T00:00:00Z'
      journal: Bioinformatics
      title: 'CoCoNat: a novel method based on deep learning for coiled-coil prediction'
    note: null
    pmcid: PMC10425188
    pmid: '37540220'
    type:
    - Primary
    version: null
  - doi: 10.21769/BioProtoc.4935
    metadata:
      abstract: 'Coiled-coil domains (CCDs) are structural motifs observed in proteins
        in all organisms that perform several crucial functions. The computational
        identification of CCD segments over a protein sequence is of great importance
        for its functional characterization. This task can essentially be divided
        into three separate steps: the detection of segment boundaries, the annotation
        of the heptad repeat pattern along the segment, and the classification of
        its oligomerization state. Several methods have been proposed over the years
        addressing one or more of these predictive steps. In this protocol, we illustrate
        how to make use of CoCoNat, a novel approach based on protein language models,
        to characterize CCDs. CoCoNat is, at its release (August 2023), the state
        of the art for CCD detection. The web server allows users to submit input
        protein sequences and visualize the predicted domains after a few minutes.
        Optionally, precomputed segments can be provided to the model, which will
        predict the oligomerization state for each of them. CoCoNat can be easily
        integrated into biological pipelines by downloading the standalone version,
        which provides a single executable script to produce the output.'
      authors:
      - name: Manfredi M.
      - name: Savojardo C.
      - name: Martelli P.L.
      - name: Casadio R.
      citationCount: 0
      date: '2024-02-20T00:00:00Z'
      journal: Bio-protocol
      title: 'CoCoNat: A Deep Learning–Based Tool for the Prediction of Coiled-coil
        Domains in Protein Sequences'
    note: null
    pmcid: PMC10883893
    pmid: '38405078'
    type:
    - Usage
    version: null
  relation: []
  toolType:
  - Web application
  - Command-line tool
  topic:
  - term: Protein structure analysis
    uri: http://edamontology.org/topic_2814
  - term: Protein folds and structural domains
    uri: http://edamontology.org/topic_0736
  validated: 0
  version:
  - '1.0'
- accessibility: Open access
  additionDate: '2024-03-11T15:00:11.214878Z'
  biotoolsCURIE: biotools:kofamscan
  biotoolsID: kofamscan
  collectionID: []
  community: null
  confidence_flag: null
  cost: Free of charge
  credit: []
  description: KofamScan is a gene function annotation tool based on KEGG Orthology
    and hidden Markov model. You need KOfam database to use this tool.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein sequence
        uri: http://edamontology.org/data_2976
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: null
    operation:
    - term: Sequence analysis
      uri: http://edamontology.org/operation_2403
    - term: Gene functional annotation
      uri: http://edamontology.org/operation_3672
    output: []
  homepage: https://github.com/takaram/kofam_scan
  homepage_status: 0
  language: []
  lastUpdate: '2024-03-11T15:00:11.219298Z'
  license: MIT
  link: []
  maturity: Mature
  name: kofamscan
  operatingSystem: []
  otherID: []
  owner: rlibouban
  publication: []
  relation: []
  toolType:
  - Bioinformatics portal
  - Command-line tool
  - Web application
  topic:
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  validated: 0
  version:
  - 1.3.0
- accessibility: Open access
  additionDate: '2023-01-05T08:42:10.922859Z'
  biotoolsCURIE: biotools:E-SNPsGO
  biotoolsID: E-SNPsGO
  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: matteo.manfredi4@unibo.it
    fundrefid: null
    gridid: null
    name: Matteo Manfredi
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - 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
    - Developer
    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: E-SNPs&GO is a machine-learning method the pathogenicity of human variations.
    E-SNPs&GO classify input variations into pathogenic or benign.
  documentation:
  - note: null
    type:
    - User manual
    url: https://esnpsandgo.biocomp.unibo.it/help/
  download: []
  editPermission:
    authors:
    - ELIXIR-ITA-BOLOGNA
    type: group
  elixirCommunity:
  - Rare Diseases
  elixirNode:
  - Italy
  elixirPlatform:
  - Tools
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein sequence
        uri: http://edamontology.org/data_2976
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    - data:
        term: Sequence variations
        uri: http://edamontology.org/data_3498
      format:
      - term: Textual format
        uri: http://edamontology.org/format_2330
    note: null
    operation:
    - term: Variant effect prediction
      uri: http://edamontology.org/operation_0331
    output:
    - data:
        term: Score
        uri: http://edamontology.org/data_1772
      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: https://esnpsandgo.biocomp.unibo.it/
  homepage_status: 0
  language:
  - Other
  lastUpdate: '2024-03-07T17:58:00.968691Z'
  license: null
  link: []
  maturity: Mature
  name: E-SNPs and GO
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: PierLuigiMartelli
  publication:
  - doi: 10.1093/bioinformatics/btac678
    metadata:
      abstract: 'MOTIVATION: The advent of massive DNA sequencing technologies is
        producing a huge number of human single-nucleotide polymorphisms occurring
        in protein-coding regions and possibly changing their sequences. Discriminating
        harmful protein variations from neutral ones is one of the crucial challenges
        in precision medicine. Computational tools based on artificial intelligence
        provide models for protein sequence encoding, bypassing database searches
        for evolutionary information. We leverage the new encoding schemes for an
        efficient annotation of protein variants. RESULTS: E-SNPs&GO is a novel method
        that, given an input protein sequence and a single amino acid variation, can
        predict whether the variation is related to diseases or not. The proposed
        method adopts an input encoding completely based on protein language models
        and embedding techniques, specifically devised to encode protein sequences
        and GO functional annotations. We trained our model on a newly generated dataset
        of 101 146 human protein single amino acid variants in 13 661 proteins, derived
        from public resources. When tested on a blind set comprising 10 266 variants,
        our method well compares to recent approaches released in literature for the
        same task, reaching a Matthews Correlation Coefficient score of 0.72. We propose
        E-SNPs&GO as a suitable, efficient and accurate large-scale annotator of protein
        variant datasets. AVAILABILITY AND IMPLEMENTATION: The method is available
        as a webserver at https://esnpsandgo.biocomp.unibo.it. Datasets and predictions
        are available at https://esnpsandgo.biocomp.unibo.it/datasets. SUPPLEMENTARY
        INFORMATION: Supplementary data are available at Bioinformatics online.'
      authors:
      - name: Manfredi M.
      - name: Savojardo C.
      - name: Martelli P.L.
      - name: Casadio R.
      citationCount: 7
      date: '2022-11-30T00:00:00Z'
      journal: Bioinformatics (Oxford, England)
      title: 'E-SNPs&GO: embedding of protein sequence and function improves the annotation
        of human pathogenic variants'
    note: null
    pmcid: PMC9710551
    pmid: '36227117'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Protein variants
    uri: http://edamontology.org/topic_3120
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2022-05-15T09:43:33.456849Z'
  biotoolsCURIE: biotools:deeprex
  biotoolsID: deeprex
  collectionID:
  - Bologna Biocomputing Group
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - 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: null
    typeRole:
    - Primary contact
    url: null
  - email: rita.casadio@unibo.it
    fundrefid: null
    gridid: null
    name: Rita Casadio
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole:
    - Primary contact
    url: null
  - email: matteo.manfredi4@unibo.it
    fundrefid: null
    gridid: null
    name: Matteo Manfredi
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole:
    - Developer
    url: null
  description: A web server for characterising protein-solvent interaction starting
    from sequence.
  documentation:
  - note: Web application manual
    type:
    - User manual
    url: https://deeprex.biocomp.unibo.it/about/tutorial/
  - note: null
    type:
    - Command-line options
    url: https://github.com/BolognaBiocomp/deeprex
  download:
  - note: null
    type: Container file
    url: https://hub.docker.com/r/bolognabiocomp/deeprex
    version: null
  - note: null
    type: Source code
    url: https://github.com/BolognaBiocomp/deeprex
    version: null
  editPermission:
    authors:
    - ELIXIR-ITA-BOLOGNA
    type: group
  elixirCommunity: []
  elixirNode:
  - Italy
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein sequence
        uri: http://edamontology.org/data_2976
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: null
    operation:
    - term: Protein disorder prediction
      uri: http://edamontology.org/operation_3904
    - term: Molecular surface calculation
      uri: http://edamontology.org/operation_0387
    - term: Protein structure validation
      uri: http://edamontology.org/operation_0321
    output: []
  homepage: https://deeprex.biocomp.unibo.it
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-03-07T17:57:11.201008Z'
  license: GPL-3.0
  link:
  - note: null
    type:
    - Repository
    url: https://github.com/BolognaBiocomp/deeprex
  maturity: Mature
  name: DeepREx
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1016/J.CSBJ.2021.10.016
    metadata:
      abstract: 'Protein–solvent interaction provides important features for protein
        surface engineering when the structure is absent or partially solved. Presently,
        we can integrate the notion of solvent exposed/buried residues with that of
        their flexibility and intrinsic disorder to highlight regions where mutations
        may increase or decrease protein stability in order to modify proteins for
        biotechnological reasons, while preserving their functional integrity. Here
        we describe a web server, which provides the unique possibility of integrating
        knowledge of solvent and non-solvent exposure with that of residue conservation,
        flexibility and disorder of a protein sequence, for a better understanding
        of which regions are relevant for protein integrity. The core of the webserver
        is DeepREx, a novel deep learning-based tool that classifies each residue
        in the sequence as buried or exposed. DeepREx is trained on a high-quality,
        non-redundant dataset derived from the Protein Data Bank comprising 2332 monomeric
        protein chains and benchmarked on a blind test set including 200 protein sequences
        unrelated with the training set. Results show that DeepREx performs at the
        state-of-the-art in the field. In turn, the Web Server, DeepREx-WS, supplements
        the predictions of DeepREx with features that allow a better characterisation
        of exposed and buried regions: i) residue conservation derived from multiple
        sequence alignment; ii) local sequence hydrophobicity; iii) residue flexibility
        computed with MEDUSA; iv) a predictor of secondary structure; v) the presence
        of disordered regions as derived from MobiDB-Lite3.0. The web server allows
        browsing, selecting and intersecting the different features. We demonstrate
        a possible application of the DeepREx-WS for assisting the identification
        of residues to be variated in protein surface engineering processes.'
      authors:
      - name: Manfredi M.
      - name: Savojardo C.
      - name: Martelli P.L.
      - name: Casadio R.
      citationCount: 3
      date: '2021-01-01T00:00:00Z'
      journal: Computational and Structural Biotechnology Journal
      title: 'DeepREx-WS: A web server for characterising protein–solvent interaction
        starting from sequence'
    note: null
    pmcid: PMC8566768
    pmid: '34765094'
    type: []
    version: null
  - doi: 10.3389/fmolb.2020.626363
    metadata:
      abstract: 'Solvent accessibility (SASA) is a key feature of proteins for determining
        their folding and stability. SASA is computed from protein structures with
        different algorithms, and from protein sequences with machine-learning based
        approaches trained on solved structures. Here we ask the question as to which
        extent solvent exposure of residues can be associated to the pathogenicity
        of the variation. By this, SASA of the wild-type residue acquires a role in
        the context of functional annotation of protein single-residue variations
        (SRVs). By mapping variations on a curated database of human protein structures,
        we found that residues targeted by disease related SRVs are less accessible
        to solvent than residues involved in polymorphisms. The disease association
        is not evenly distributed among the different residue types: SRVs targeting
        glycine, tryptophan, tyrosine, and cysteine are more frequently disease associated
        than others. For all residues, the proportion of disease related SRVs largely
        increases when the wild-type residue is buried and decreases when it is exposed.
        The extent of the increase depends on the residue type. With the aid of an
        in house developed predictor, based on a deep learning procedure and performing
        at the state-of-the-art, we are able to confirm the above tendency by analyzing
        a large data set of residues subjected to variations and occurring in some
        12,494 human protein sequences still lacking three-dimensional structure (derived
        from HUMSAVAR). Our data support the notion that surface accessible area is
        a distinguished property of residues that undergo variation and that pathogenicity
        is more frequently associated to the buried property than to the exposed one.'
      authors:
      - name: Savojardo C.
      - name: Manfredi M.
      - name: Martelli P.L.
      - name: Casadio R.
      citationCount: 51
      date: '2021-01-07T00:00:00Z'
      journal: Frontiers in Molecular Biosciences
      title: 'Solvent Accessibility of Residues Undergoing Pathogenic Variations in
        Humans: From Protein Structures to Protein Sequences'
    note: null
    pmcid: null
    pmid: null
    type:
    - Benchmarking study
    version: null
  relation: []
  toolType:
  - Web application
  - Command-line tool
  topic:
  - term: Protein structural motifs and surfaces
    uri: http://edamontology.org/topic_0166
  - term: Biotechnology
    uri: http://edamontology.org/topic_3297
  - term: Protein folding, stability and design
    uri: http://edamontology.org/topic_0130
  - term: Sequence sites, features and motifs
    uri: http://edamontology.org/topic_0160
  - term: Imaging
    uri: http://edamontology.org/topic_3382
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2021-06-14T13:42:20Z'
  biotoolsCURIE: biotools:benz-ws
  biotoolsID: benz-ws
  collectionID:
  - Bologna Biocomputing Group
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - 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
  - email: null
    fundrefid: null
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    name: ELIXIR-ITA-BOLOGNA
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  description: The Bologna ENZyme Web Server (BENZ WS) annotates the Enzyme Commission
    numbers (EC numbers) of enzymes. BENZ WS is based on HMMs and PFAMs and returns
    a four-level EC number for a target which is retained by the system.
  documentation:
  - note: null
    type:
    - User manual
    url: https://benzdb.biocomp.unibo.it/help
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode:
  - Italy
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein sequence
        uri: http://edamontology.org/data_2976
      format:
      - term: FASTA-like
        uri: http://edamontology.org/format_2546
    note: null
    operation:
    - term: Sequence annotation
      uri: http://edamontology.org/operation_0361
    - term: Query and retrieval
      uri: http://edamontology.org/operation_0224
    - term: Filtering
      uri: http://edamontology.org/operation_3695
    output:
    - data:
        term: Protein report
        uri: http://edamontology.org/data_0896
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
  homepage: https://benzdb.biocomp.unibo.it
  homepage_status: 0
  language: []
  lastUpdate: '2024-03-07T17:56:22.660972Z'
  license: null
  link: []
  maturity: Mature
  name: BENZ WS
  operatingSystem: []
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1093/NAR/GKAB328
    metadata:
      abstract: The Bologna ENZyme Web Server (BENZ WS) annotates four-level Enzyme
        Commission numbers (EC numbers) as defined by the International Union of Biochemistry
        and Molecular Biology (IUBMB). BENZ WS filters a target sequence with a combined
        system of Hidden Markov Models, modelling protein sequences annotated with
        the same molecular function, and Pfams, carrying along conserved protein domains.
        BENZ returns, when successful, for any enzyme target sequence an associated
        four-level EC number. Our system can annotate both monofunctional and polyfunctional
        enzymes, and it can be a valuable resource for sequence functional annotation.
      authors:
      - name: Baldazzi D.
      - name: Savojardo C.
      - name: Martelli P.L.
      - name: Casadio R.
      citationCount: 6
      date: '2021-07-02T00:00:00Z'
      journal: Nucleic Acids Research
      title: 'BENZ WS: The Bologna ENZyme Web Server for four-level EC number annotation'
    note: null
    pmcid: null
    pmid: '33963861'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Enzymes
    uri: http://edamontology.org/topic_0821
  - term: Sequence sites, features and motifs
    uri: http://edamontology.org/topic_0160
  - term: Biochemistry
    uri: http://edamontology.org/topic_3292
  - term: Protein folds and structural domains
    uri: http://edamontology.org/topic_0736
  - term: Sequence analysis
    uri: http://edamontology.org/topic_0080
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2023-03-28T22:48:48.088931Z'
  biotoolsCURIE: biotools:ispred-seq
  biotoolsID: ispred-seq
  collectionID:
  - Bologna Biocomputing Group
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: pierluigi.martelli@unibo.it
    fundrefid: null
    gridid: null
    name: Pier Luigi Martelli
    note: null
    orcidid: http://orcid.org/0000-0002-0274-5669
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    url: null
  - email: rita.casadio@unibo.it
    fundrefid: null
    gridid: null
    name: Rita Casadio
    note: null
    orcidid: http://orcid.org/0000-0002-7462-7039
    rorid: null
    typeEntity: null
    typeRole:
    - Primary contact
    url: null
  - email: castrense.savojardo2@unibo.it
    fundrefid: null
    gridid: null
    name: Castrense Savojardo
    note: null
    orcidid: http://orcid.org/0000-0002-7359-0633
    rorid: null
    typeEntity: null
    typeRole:
    - Primary contact
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: Matteo Manfredi
    note: null
    orcidid: null
    rorid: null
    typeEntity: null
    typeRole:
    - Primary contact
    url: null
  - email: null
    fundrefid: null
    gridid: null
    name: ELIXIR-ITA-BOLOGNA
    note: null
    orcidid: null
    rorid: null
    typeEntity: Institute
    typeRole:
    - Provider
    url: null
  description: Deep neural networks and embeddings for predicting interaction sites
    in protein sequences.
  documentation:
  - note: null
    type:
    - User manual
    url: https://ispredws.biocomp.unibo.it/sequence/about/tutorial/
  download: []
  editPermission:
    authors:
    - ELIXIR-ITA-BOLOGNA
    type: group
  elixirCommunity: []
  elixirNode:
  - Italy
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein sequence
        uri: http://edamontology.org/data_2976
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: null
    operation:
    - term: Protein interaction prediction
      uri: http://edamontology.org/operation_2492
    - term: Protein interaction network prediction
      uri: http://edamontology.org/operation_3094
    - term: Protein feature detection
      uri: http://edamontology.org/operation_3092
    - term: Protein-protein docking
      uri: http://edamontology.org/operation_3899
    output:
    - data:
        term: Protein features
        uri: http://edamontology.org/data_1277
      format:
      - term: TSV
        uri: http://edamontology.org/format_3475
      - term: HTML
        uri: http://edamontology.org/format_2331
  homepage: https://ispredws.biocomp.unibo.it
  homepage_status: 0
  language: []
  lastUpdate: '2024-03-07T17:55:59.780584Z'
  license: null
  link: []
  maturity: Mature
  name: ISPRED-SEQ
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1016/j.jmb.2023.167963
    metadata:
      abstract: The knowledge of protein–protein interaction sites (PPIs) is crucial
        for protein functional annotation. Here we address the problem focusing on
        the prediction of putative PPIs considering as input protein sequences. The
        issue is important given the huge volume of protein sequences compared to
        experimental and/or computed structures. Taking advantage of protein language
        models, recently developed, and Deep Neural networks, here we describe ISPRED-SEQ,
        which overpasses state-of-the-art predictors addressing the same problem.
        ISPRED-SEQ is freely available for testing at https://ispredws.biocomp.unibo.it.
      authors:
      - name: Manfredi M.
      - name: Savojardo C.
      - name: Martelli P.L.
      - name: Casadio R.
      citationCount: 5
      date: '2023-07-15T00:00:00Z'
      journal: Journal of Molecular Biology
      title: 'ISPRED-SEQ: Deep Neural Networks and Embeddings for Predicting Interaction
        Sites in Protein Sequences'
    note: null
    pmcid: null
    pmid: '37356906'
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Protein interactions
    uri: http://edamontology.org/topic_0128
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: Protein structure analysis
    uri: http://edamontology.org/topic_2814
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-03-07T16:59:02.773893Z'
  biotoolsCURIE: biotools:e-prsa
  biotoolsID: e-prsa
  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: matteo.manfredi4@unibo.it
    fundrefid: null
    gridid: null
    name: Matteo Manfredi
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole:
    - Primary contact
    - Developer
    - Maintainer
    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:
    - Contributor
    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
    - Support
    url: null
  description: E-pRSA is a method for predicting the Relative Solvent Accessibility
    of residues in a protein chain without requiring previous knowledge of the 3-dimensional
    structure. E-pRSA is based on embeddings derived from protein language models.
  documentation:
  - note: null
    type:
    - User manual
    url: https://e-prsa.biocomp.unibo.it/main/about/tutorial/
  download: []
  editPermission:
    authors:
    - savo
    - ELIXIR-ITA-BOLOGNA
    type: group
  elixirCommunity:
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  - Italy
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  elixir_badge: 0
  function:
  - cmd: null
    input:
    - data:
        term: Protein sequence
        uri: http://edamontology.org/data_2976
      format:
      - term: FASTA-like
        uri: http://edamontology.org/format_2546
    note: null
    operation:
    - term: Accessible surface calculation
      uri: http://edamontology.org/operation_0384
    - term: Protein feature detection
      uri: http://edamontology.org/operation_3092
    output:
    - data:
        term: Sequence report
        uri: http://edamontology.org/data_2955
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
  homepage: https://e-prsa.biocomp.unibo.it/main
  homepage_status: 0
  language: []
  lastUpdate: '2024-03-07T17:02:02.229706Z'
  license: null
  link: []
  maturity: Mature
  name: E-pRSA
  operatingSystem:
  - Linux
  - Mac
  - Windows
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1016/j.jmb.2024.168494
    metadata: null
    note: null
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
  relation: []
  toolType:
  - Web application
  topic:
  - term: Sequence sites, features and motifs
    uri: http://edamontology.org/topic_0160
  validated: 0
  version:
  - '1.0'
- accessibility: Open access
  additionDate: '2021-01-27T11:47:58Z'
  biotoolsCURIE: biotools:betaware-deep
  biotoolsID: betaware-deep
  collectionID:
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  community: null
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  cost: Free of charge
  credit:
  - email: null
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    name: ELIXIR-ITA-BOLOGNA
    note: null
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  - email: castrense.savojardo2@unibo.it
    fundrefid: null
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    name: Castrense Savojardo
    note: null
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    url: null
  - email: pierluigi.martelli@unibo.it
    fundrefid: null
    gridid: null
    name: Pier Luigi Martelli
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    rorid: null
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    url: null
  description: BetAware-Deep is web server for predicting the topology of TransMembrane
    Beta-Barrel (TMBB) proteins in Gram-negative bacteria.
  documentation:
  - note: null
    type:
    - User manual
    url: https://busca.biocomp.unibo.it/betaware2/help/
  download: []
  editPermission:
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    type: group
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  function:
  - cmd: null
    input:
    - data:
        term: Protein sequence
        uri: http://edamontology.org/data_2976
      format:
      - term: FASTA
        uri: http://edamontology.org/format_1929
    note: null
    operation:
    - term: Transmembrane protein prediction
      uri: http://edamontology.org/operation_0269
    output:
    - data:
        term: Report
        uri: http://edamontology.org/data_2048
      format:
      - term: HTML
        uri: http://edamontology.org/format_2331
      - term: JSON
        uri: http://edamontology.org/format_3464
      - term: Textual format
        uri: http://edamontology.org/format_2330
  homepage: https://busca.biocomp.unibo.it/betaware2/
  homepage_status: 0
  language: []
  lastUpdate: '2024-03-07T11:47:33.221761Z'
  license: null
  link: []
  maturity: Mature
  name: BetAware-Deep
  operatingSystem:
  - Linux
  - Windows
  - Mac
  otherID: []
  owner: ELIXIR-ITA-BOLOGNA
  publication:
  - doi: 10.1016/j.jmb.2020.166729
    metadata:
      abstract: TransMembrane β-Barrel (TMBB) proteins located in the outer membranes
        of Gram-negative bacteria are crucial for many important biological processes
        and primary candidates as drug targets. Structure determination of TMBB proteins
        is challenging and hence computational methods devised for the analysis of
        TMBB proteins are important for complementing experimental approaches. Here,
        we present a novel web server called BetAware-Deep that is able to accurately
        identify the topology of TMBB proteins (i.e. the number and orientation of
        membrane-spanning segments along the protein sequence) and to discriminate
        them from other protein types. The method in BetAware-Deep defines new features
        by exploiting a non-canonical computation of the hydrophobic moment and by
        adopting sequence-profile weighting of the White&Wimley hydrophobicity scale.
        These features are processed using a two-step approach based on deep learning
        and probabilistic graphical models. BetAware-Deep has been trained on a dataset
        comprising 58 TMBBs and benchmarked on a novel set of 15 TMBB proteins. Results
        showed that BetAware-Deep outperforms two recently released state-of-the-art
        methods for topology prediction, predicting correct topologies of 10 out of
        15 proteins. TMBB detection was also assessed on a larger dataset comprising
        1009 TMBB proteins and 7571 non-TMBB proteins. Even in this benchmark, BetAware-Deep
        scored at the level of top-performing methods. A web server has been developed
        allowing users to analyze input protein sequences and providing topology prediction
        together with a rich set of information including a graphical representation
        of the residue-level annotations and prediction probabilities. BetAware-Deep
        is available at https://busca.biocomp.unibo.it/betaware2.
      authors:
      - name: Madeo G.
      - name: Savojardo C.
      - name: Martelli P.L.
      - name: Casadio R.
      citationCount: 10
      date: '2021-05-28T00:00:00Z'
      journal: Journal of Molecular Biology
      title: 'BetAware-Deep: An Accurate Web Server for Discrimination and Topology
        Prediction of Prokaryotic Transmembrane β-barrel Proteins'
    note: null
    pmcid: null
    pmid: '33972021'
    type:
    - Primary
    version: null
  relation:
  - biotoolsID: betaware
    type: isNewVersionOf
  toolType:
  - Web application
  topic:
  - term: Membrane and lipoproteins
    uri: http://edamontology.org/topic_0820
  validated: 0
  version:
  - '1.0'
next: ?page=2
previous: null
