Protein–ligand docking

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.[1] 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 that calculate the site, geometry and energy of small molecules or peptides interacting with proteins are available, such as AutoDock and AutoDock Vina, rDock, FlexAID, Molecular Operating Environment, and Glide.

Protein flexibility

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.[2] 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.[3] Using coarse grained protein models to overcome this problem seems to be a promising approach.[2] 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.[4]

See also
  • Docking (molecular)
  • Protein–protein docking
  • Virtual screening
  • List of protein-ligand docking software

References
  1. Taylor, R.D.; Jewsbury, P.J.; Essex, J.W. (2002-03-01). "A review of protein-small molecule docking methods". Journal of Computer-Aided Molecular Design. 16 (3): 151–166. doi:10.1023/A:1020155510718. ISSN 1573-4951. PMID 12363215.
  2. Antunes, Dinler A; Devaurs, Didier; Kavraki, Lydia E (2015-09-28). "Understanding the challenges of protein flexibility in drug design" (PDF). Expert Opinion on Drug Discovery. 10 (12): 1301–1313. doi:10.1517/17460441.2015.1094458. hdl:1911/88215. ISSN 1746-0441. PMID 26414598.
  3. Cerqueira, N. M. F. S. A.; Bras, N. F.; Fernandes, P. A.; Ramos, M. J. (2009-07-10). "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.
  4. 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.
  • BioLiP, a comprehensive ligand-protein interaction database
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