References

Citing Us

• Guedes, I. A.; Barreto, A. M. S.; Marinho, D.; Krempser, E.; Kuenemann, M. A.; Sperandio, O.; Dardenne, L. E.; Miteva, M. A. New Machine Learning and Physics-Based Scoring Functions for Drug Discovery. Sci Rep 2021, 11 (1), 3198. https://doi.org/10.1038/s41598-021-82410-1.
  
  Download the curated PDBbind core set v.2013 data set here


• 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.

When using any SARS-CoV-2 target or drug repurposing datasets, please cite the following article:


• Guedes, I. A. et al. Drug design and repurposing with DockThor-VS web server focusing on SARS-CoV-2 therapeutic targets and their non-synonym variants. Sci Rep 11, 5543 (2021).

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.

New Features

General

• 04/27/2020 - Three-dimensional structures now are visualized with the NGL Viewer.
• 04/10/2020 - New redocking Tutorial with new test files available (download the tutorial at Support -> Help).
• 01/24/2020 - Download of the curated LEADS-PEP data set is available in the References tab.

Protein

• 06/08/2020 - Curated structures of relevant therapeutic targets of SARS-CoV-2 are now available. The dataset will be constantly updated with new structures (wild type and selected mutations).
• 04/27/2020 - Neutral cisteine (CYSH) set as the default protonation state.
• 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

• 08/12/2020 - Curated datasets for drug repurposing at reference pH are now available! New datasets will be available soon.
• 08/12/2020 - Virtual screening with up to 200 small molecules as guest user.
• 05/22/2020 - Virtual screening now with up to 5000 small molecules as registered user.
• 01/17/2020 - Dock highly flexible ligands such as peptides with up to 40 residues and 60 dihedrals.
• 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 > 60;
• 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

• 04/27/2020 - The input grid size on each dimension corresponds now to the total size of the grid box on each axis (instead of the half value).
• 04/10/2020 - 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 (or the maximum size allowed). Attention: experimental, use it with caution.
• 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 200 compounds;
  • Few hours for screening up to 5000 molecules.
• Summary of uploaded files.
• 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

• 04/27/2020 - Number of top-energy binding modes are limited to 3 for virtual screening and 10 for single docking experiments.
• 04/10/2020 - Corrected the protein file *_prep.pdb in the docking compacted folder (.zip file) at the directory PROTEIN/.
• 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.

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.