Welcome to DockThor

A Free Web Server for Protein-ligand Docking

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Protein

Add missing hydrogen atoms, complete side chains, change protonation states. Simple and easy!

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Small molecules

Add hydrogen atoms (pH 7), freeze rotatable bonds, get MMFF94S atom types and partial charges. Fast and automatic!

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Cofactors

Consider cofactors and structural waters on virtual screening experiments with automatic MMFF94S parametrization.

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Redocking

Validate docking protocol with redocking experiments. We provide the RMSD between reference and docked poses.

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Blind Docking

Searching for binding sites? Perform blind docking on the entire protein and find cavities!

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Virtual Screening

Perform large scale docking experiments exploring multiple binding modes. Dock them all!


Interactive Analyses. Explore docking poses and predict affinity.

Investigate different binding modes and predict binding affinities. Visualize predicted complexes with JSMol.

500x500

500x500

SDumont Supercomputer. Virtual screening experiments even faster.

Run virtual screening experiments at Santos Dumont supercomputer, located at LNCC, Petrópolis - Brazil.

  1. Upload your protein file

    Add file Add file
    File name Size Status Actions
    {{$parent.protein.name}} {{$parent.protein.size}}
    {{$parent.protein.error}}
    File name Size Status Actions

    {{file.name}} {{file.name}} {{file.name}}

    {{file.size | formatFileSize}}

  2. Select the protonation states

    Chain {{ chainKey }}
    • {{ atomKey }}
      Residue index Residue number Protonation state
      {{indexKey}} {{indexValue.state.index}}
      -



    Modification log
    • Chain {{proteinEditLog[key]['chain']}}: {{proteinEditLog[key]['atom']}} {{proteinEditLog[key]['index']}} was set as {{proteinEditLog[key]['after']}}

    * This area is just avaliable with 'pdb' files

  3. Prepared protein file

  1. Upload your ligand file

    # File name Size Add H Status Actions
    {{($index+1)}}

    {{file.name}} {{file.name}} {{file.name}}

    {{file.size | formatFileSize}}

    Valid structures: {{totalValidStructures}} (max. {{roleStructureValid}})
    Valid structures: {{totalValidStructures}} (max. {{roleStructureValid}})
    Error: number of compounds exceeds the limit allowed.
    Sorry, the following files have the same content:
    • Same content: {{ivalue['originalName']}},

  2. Rotatable bond editor


    Rotatable bonds: {{getRotbCount(rotbElements)}}
    # Atom 1 Atom 2 Disable/Enable
    {{$index + 1}} {{value[3]}}{{value[0]}} {{value[4]}}{{value[1]}}

    Disable/Enable all:



    Modification log
    • Bond {{value[0]}} - {{value[1]}} set as rigid Bond {{value[0]}} - {{value[1]}} set as flexible

    * This area is just avaliable when working with one ligand (pdb, sdf or mol2)

  3. Prepared ligand file

    Download
    Send to DockThor

  1. Upload your cofactor file

    Add file Add file

    Or

    File name Size Add H Status Actions

    {{file.name}} {{file.name}} {{file.name}}

    {{file.size | formatFileSize}}

    Valid structures: {{totalValidStructures}} (max. {{roleStructureValid}})
    Valid structures: {{totalValidStructures}} (max. {{roleStructureValid}})
    Sorry, the number of structure valid is not allowed.
    Sorry, the following files have the same content:
    • Same content: {{ivalue['originalName']}},

  2. Prepared cofactor file

    Download
    Send to DockThor

  1. Check your docking input files

    • Protein ( 1 )
      • {{$parent.proteinInput.codedName}}
    • Ligand ( {{getLigandTotalStructures()}} )
      • {{ligand.fileIdWithExtension}} ( {{ligand.validStructure}} )
    • Cofactor ( {{getCofactorTotalStructures()}} )
      • {{cofactor.fileIdWithExtension}} ( {{cofactor.validStructure}} )

  2. Define the binding site

    Grid center:

    Discretization:

    Total grid points:

    {{points}}

    Protein

    Cofactors

  3. Select the search algorithm precision

    Number of Evaluations:

    Population Size:

    Initial Seed:

    Number of Runs:

    Soft Docking

  4. Identify your docking job

    (success)
    (success)

    Subscribe DockThor e-Newsletters

    Accept terms of use

  1. Select the parameters for analyses of docking results:

    RMSD to cluster conformers :

    Å

    Number of binding modes :

    Compare docking poses with a reference conformation?


  2. Analyze your docking results:


    Rank Compound Score T. Energy I. Energy RMSD
    {{$index + 1}} {{item.name}} {{item.poses[0].score}} {{item.poses[0].tenergy}} {{item.poses[0].ienergy}} {{item.poses[0].rmsd}}
    run{{pose.run}}_model{{pose.model}} {{pose.score}} {{pose.tenergy}} {{pose.ienergy}} {{pose.rmsd}}

    Protein

    Reference Ligand

    Cofactors

  1. Job Status

    Job id: {{job.id}}
    Job Status: {{job.status}}






    Job id "{{job.id}}" not found

References

Citing Us

  • K. B. dos Santos, I. A. Guedes, A. L. M. Karl, and L. Dardenne. Highly Flexible Ligand Docking: Benchmarking of the DockThor Program on the LEADS-PEP Protein-peptide Dataset, J. Chem. Inf. Model., Jan. 2020, doi: 10.1021/acs.jcim.9b00905.

    Download the LEADS-PEP data set here

  • C. S. de Magalhães, D. M. Almeida, H. J. C. Barbosa, and L. E. Dardenne, A dynamic niching genetic algorithm strategy for docking highly flexible ligands, Information Sciences, vol. 289, pp. 206–224, Dec. 2014, doi: 10.1016/j.ins.2014.08.002.

Other Articles

  • I. A. Guedes, F. S. S. Pereira, and L. E. Dardenne, Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges, Front. Pharmacol., vol. 9, pp. 1–18, 2018, doi: 10.3389/fphar.2018.01089.

  • I. A. Guedes, C. S. de Magalhães, and L. E. Dardenne, Receptor–ligand molecular docking, Biophysical Reviews, vol. 6, no. 1, pp. 75–87, Mar. 2014, doi: 10.1007/s12551-013-0130-2.

  • C. S. de Magalhães, H. J. C. Barbosa, and L. E. Dardenne, Selection-Insertion Schemes in Genetic Algorithms for the Flexible Ligand Docking Problem, in Genetic and Evolutionary Computation – GECCO 2004, vol. 3102, K. Deb, Ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004, pp. 368–379.

  • C. S. de de Magalhães, H. J. C. Barbosa, and L. E. Dardenne, A genetic algorithm for the ligand-protein docking problem, Genetics and Molecular Biology, vol. 27, no. 4, pp. 605–610, 2004, doi: 10.1590/S1415-47572004000400022.

Thesis

  • Isabella Alvim Guedes. Development of Empirical Scoring Functions for Predicting Protein-ligand Binding Affinity. D.Sc. Thesis, Laboratório Nacional de Computação Científica/Brasil, 2016.

  • Diogo Marinho Almeida. Dockthor: Implementação, Aprimoramenteo e Validação de um Programa de Docking Receptor-Ligante. M.Sc. Thesis, Laboratório Nacional de Computação Científica/Brasil, 2011.

  • Camila Silva de Magalhães. Algoritmos Genéticos para o Problema de Docking Proteína-Ligante. D.Sc. Thesis, Laboratório Nacional de Computação Científica/Brasil, 2006.

DockThor Team

Laurent Emmanuel Dardenne

Dr.Sci. in Biophysics
Grupo de Modelagem Molecular de Sistemas Biológicos
Laboratório Nacional de Computação Científica (LNCC-MCTIC)
E-mail: dardenne@lncc.br

Lattes: http://lattes.cnpq.br/8344194525615133

Hélio José C. Barbosa

D.Sc. in Civil Engineering
Grupo de Metaheurísticas Inspiradas na Natureza e Aplicações
Laboratório Nacional de Computação Científica (LNCC-MCTIC)
E-mail: hcbm@lncc.br

Lattes: http://lattes.cnpq.br/0375745110240885

Camila Silva de Magalhães

D.Sc. in Computational Modelling
Universidade Federal Rural do Rio de Janeiro (UFRRJ)
E-mail: camila.mag@gmail.com

Lattes: http://lattes.cnpq.br/5956779016222823

Diogo Marinho Almeida

D.Sc. Student in Biotecnology
Universidade Federal do Pará (UFPA)
E-mail: diogo.marinho@gmail.com

Lattes: http://lattes.cnpq.br/2956577623367895

Eduardo Krempser da Silva

D.Sc. in Computational Modelling
Grupo de Metaheurísticas Inspiradas na Natureza e Aplicações
Fundação Oswaldo Cruz (Fiocruz)
E-mail: eduardo.krempser@fiocruz.br

Lattes: http://lattes.cnpq.br/5399601756512708

Fábio Lima Custódio

Dr.Sci. in Computational Modeling
Grupo de Modelagem Molecular de Sistemas Biológicos
Laboratório Nacional de Computação Científica (LNCC-MCTIC)
E-mail: flc@lncc.br

Lattes: http://lattes.cnpq.br/9126339190151859

Isabella Alvim Guedes

D.Sc. in Computational Modelling
Grupo de Modelagem Molecular de Sistemas Biológicos
Laboratório Nacional de Computação Científica (LNCC-MCTIC)
E-mail: isabella@lncc.br

Lattes: http://lattes.cnpq.br/2265756121721735

SINAPAD Team

Antônio Tadeu Azevedo Gomes

Dr.Sc. in Computer Science
Laboratório Nacional de Computação Científica (LNCC-MCTIC)
E-mail: atagomes@lncc.br

Lattes: http://lattes.cnpq.br/8326803963394077

Iuri Malinoski Teixeira

M.Sc. in Computer Science
Laboratório Nacional de Computação Científica (LNCC-MCTIC)
E-mail: malinoski.iuri@gmail.com

LinkedIn: https://br.linkedin.com/in/malinoski-iuri
Lattes: http://lattes.cnpq.br/9139006650391608

Marcelo Monteiro Galheigo

Technologist in Information and Communication Technology
Laboratório Nacional de Computação Científica (LNCC-MCTIC)
E-mail: galheigo@lncc.br

Lattes: http://lattes.cnpq.br/8936826427650220

Vívian Medeiros

M.Sc. in Computational Modelling
Laboratório Nacional de Computação Científica (LNCC-MCTIC)
E-mail: vivian@lncc.br

Lattes: http://lattes.cnpq.br/9244696980224618

Help

Tutorial Files

Basic Tutorial

Under development.

Advanced Tutorial

Under development.

Contact Us

New Features


Protein


  • Hydrogen atoms added to the protein with external tools (e.g. Maestro, Protoss, PDB2PQR) are automatically recognized and the residues protonation states are identified. Residues protonation states could also be modified later.
  • Download options:
    • Prepared file (.pdb) containing the prepared protein file with the final protonation states and polar hydrogen atoms added.
    • Topology file(s) (.in) for the uploaded target protein.
    • Compacted folder (.zip) with all input and output files.
  • Bug fixes.

Cofactors


  • Upload up to 10 cofactor files.
  • MOL2 and SDF format files are now accepted in addition to the PDB and TOP types.
  • Download options:
    • Map file (.csv) containing the relationship between the original file names and random IDs provided by DockThor.
    • Topology file(s) (.top) for the uploaded compounds.
    • Compacted folder (.zip) with all input and output files.
  • Bug fixes.

Ligands


  • 01/17/2020 - Dock highly flexible ligands such as peptides with up to 40 residues and 60 dihedrals.
  • Virtual screening with up to 100 small molecules as guest user.
  • Virtual screening with up to 1000 small molecules as registered user.
  • Upload single or multiple structures file (only available for MOL2 and SDF files).
  • Compounds are filtered to remove invalid structures:
    • Molecular weight > 1500 Da;
    • Rotatable bonds > 40;
    • 2D structure;
    • At least one atom type not recognized by the MMFF94S force field.
  • Recovery the original file names with the table mapfile.csv.
  • Enable/disable all rotatable bonds with just one click.
  • Calculate compound properties with Obprop/OpenBabel (provided in table obprop.csv).
  • Download options:
    • Map file (.csv) containing the relationship between the original file names and random IDs provided by DockThor.
    • Properties table (s) (.csv) containing the compounds properties calculated by Obprop/OpenBabel.
    • Topology file(s) (.top) for the uploaded compounds.
    • Compacted folder (.zip) with all input and output files.
  • Bug fixes.

Docking


  • 01/24/2020 - Softening the MMFF94S Buf-14-7 potential now available (buffering constant = 0.35).
  • Faster virtual screening with the Santos Dumont supercomputer:
    • Minutes for screening up to 100 compounds;
    • Few hours for screening up to 1000 molecules.
  • Summary of uploaded files.
  • Blind docking option that centers the energy grid at the center of mass of the protein and defines the grid dimensions to cover the entire protein (experimental tool, use with caution).
  • Iterative configuration of the grid integrated with the protein visualization.
  • Pre-defined configurations of the search algorithm:
    • Standard: optimized configuration explored through in-house benchmarking studies.
    • Virtual screening: faster protocol for screening compound databases with good accuracy.
    • Explorer: change the parameters as you wish (only available for docking with up to 100 compounds).
  • Receive the e-mail with the results in up to five addresses.
  • Subscribe DockThor e-Newsletters to receive DockThor news.
  • Job name simplified to label + random ID.
  • An e-mail is sent when your job is submitted and a second one when the job is finished.

Analyses


  • Predict binding affinity with a new empirical scoring function composed by terms accounting for intermolecular interactions, ligand entropy, desolvation and lipophilic contacts.
  • Explore multiple binding modes even in virtual screening experiments.
  • Download your docking results:
    • Ranked compounds according to the affinity prediction (bestranking files).
    • Top-ranked binding modes for each compound after clustering (result- files).
  • Explore different clustering parameters when docking a single ligand.

General


  • 01/24/2020 - Download of the curated LEADS-PEP data set is available in the References tab.

Releases Notes

  • Camila Silva de Magalhães, Diogo Marinho Almeida, Hélio José Correa Barbosa, Laurent Emmnanuel Dardenne. A Dynamic Niching Genetic Algorithm Strategy for Docking of Highly Flexible Ligands. Information Sciences, 289, p. 206-224, 2014.

  • Camila Silva de Magalhães, Hélio José Correa Barbosa, Laurent Emmnanuel Dardenne. Selection-Insertion Schemes in Genetic Algorithms for the Flexible Ligand Docking Problem. Lecture Notes in Computer Science, v. 3102/2004, p. 1-12, 2004.

Version 2.0 . Copyright © GMMSB 2019. All Rights Reserved.