Protein–ligand docking is a molecular modelling technique. The goal of protein–ligand docking is to predict the position and orientation of a ligand (a small molecule) when it is bound to a protein receptor or enzyme. Pharmaceutical research employs docking techniques for a variety of purposes, most notably in the virtual screening of large databases of available chemicals in order to select likely drug candidates.
Several protein–ligand docking software applications are available, such as AutoDock, rDock (free and opensource), EADock, CABS-dock, Rosetta FlexPepDock or GalaxyPepDock. There are also web service (Molecular Docking Server, SwissDock, CABS-dock, Rosetta FlexPepDock) that calculate the site, geometry and energy of small molecules or peptides interacting with proteins.
Computational capacity has increased dramatically over the last decade making possible the use of more sophisticated and computationally intensive methods in computer-assisted drug design. However, dealing with receptor flexibility in docking methodologies is still a thorny issue. The main reason behind this difficulty is the large number of degrees of freedom that have to be considered in this kind of calculations. However, in most of the cases, neglecting it leads to poor docking results in terms of binding pose prediction in real-world settings. Using coarse grained protein models to overcome this problem seems to be a promising approach. Coarse-grained models are often implemented in the case of protein-peptide docking, as they frequently involve large-scale conformation transitions of the protein receptor.
- Step by step installation of MGLTools 1.5.2 (AutoDockTools, Python Molecular Viewer and Visual Programming Environment) on Ubuntu Linux 8.04
Public distribution of docking software
- AutoDock and MGLTools on Debian
Public ligand-protein binding database
- A comprehensive ligand-protein interaction database on BioLiP
- Ligand:Receptor Docking with MOE (Molecular Operating Environment)
- Antunes, Dinler A; Devaurs, Didier; Kavraki, Lydia E (2015-09-28). "Understanding the challenges of protein flexibility in drug design". Expert Opinion on Drug Discovery. 10 (12): 1301–1313. doi:10.1517/17460441.2015.1094458. hdl:1911/88215. ISSN 1746-0441. PMID 26414598.
- Cerqueira NM, Fernandes PA, Eriksson LA, Ramos MJ (July 2009). "MADAMM: A multistaged docking with an automated molecular modeling protocol". Proteins: Structure, Function, and Bioinformatics. 74 (1): 192–206. doi:10.1002/prot.22146. PMID 18618708.
- Ciemny, Maciej; Kurcinski, Mateusz; Kamel, Karol; Kolinski, Andrzej; Alam, Nawsad; Schueler-Furman, Ora; Kmiecik, Sebastian (2018-05-04). "Protein–peptide docking: opportunities and challenges". Drug Discovery Today. 23 (8): 1530–1537. doi:10.1016/j.drudis.2018.05.006. ISSN 1359-6446. PMID 29733895.
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