Protein–Ligand Interactions: Computational Docking

Abstract

A pharmaceutical drug compound is usually a small organic molecule, also termed as ligand, that binds to the target protein and alters the natural activity of the protein, thus, leading to a therapeutic effect. Computational docking or computer‐aided docking is an extremely useful tool to gain an understanding of protein–ligand interactions which is important for the drug discovery. Computational docking is the process of computationally predicting the placement and binding affinity of the ligand in the binding pocket of the protein. Docking methods rely on a search algorithm which computes the placement of the ligand in the binding pocket and a scoring function which estimates the binding affinity, that is, how strongly the ligand interacts with the protein. A variety of methods have been developed to solve the computational docking problems that range from simple point‐matching algorithms to explicit physical simulation methods.

Key Concepts:

  • Computational docking methods play an important role in the drug discovery process.

  • A docking method computes the placement of a ligand in the binding pocket of a protein and estimates the binding affinity.

  • Rigid‐body docking methods treat both the protein and ligand as rigid bodies.

  • Flexible ligand methods treat the ligand as a flexible molecule and flexible receptor methods treat both the ligand and the protein as flexible molecules.

  • Two main features of computational docking techniques are a conformation search algorithm and a scoring function that estimates binding affinity.

  • Most of the computational docking programs treat the protein as a rigid molecule and the ligand as a flexible molecule.

  • Protein flexibility is an important determinant of the accuracy of docking programs.

  • Efforts have been made to account for protein flexibility in docking methods, but more needs to be done.

Keywords: docking; drug design; protein–ligand docking; flexible docking; flexible receptor; scoring function

Figure 1.

(a) The anticancer drug imatinib (blue) in the binding pocket of the Abelson kinase (orange), a proto‐oncoprotein. A mutant version of this protein is involved in the development of chronic myelogenous leukaemia. Note the marked geometric complementarity of the two molecules: atoms of the drug occupy cavities in the surface of the binding pocket. Both molecules are rendered as Connolly surfaces. PDB structure 1IEP. (b) Several amino acid residues of the binding pocket of the Abelson kinase (coloured) form hydrogen bonds with partially charged atoms on a molecule of imatinib (grey). These interactions help define the chemical complementarity between protein and ligand that enables stable binding. (c) Hydrophobic amino acid residues of the Abelson kinase binding pocket help stabilise the hydrophobic rings of imatinib. Additionally, the presence of hydrophobic groups helps exclude water from the binding pocket, which might otherwise interfere with the hydrogen bonds illustrated in (b).

Figure 2.

The effect rotation about the bond between atom groups B and C. This is the type of motion responsible for most large‐scale rearrangements of organic molecules, including proteins and ligands.

Figure 3.

The basic operations in a genetic algorithm. In the initial population each gene is a string of numbers that represent a possible solution to the problem at hand (in this case, the docking of a ligand to a protein). In selection, several genes are randomly chosen from the initial population, with a bias based on a fitness function, perhaps the score of the docking each represents. In crossing over, pairs of genes exchange a part of their sequence. The genes resulting from crossing over are then copied in sufficient quantity to restore the original size of the population. Finally, in the mutation phase, some points of some genes are randomly changed. If the genes are represented as strings of bits, for instance, bits selected for mutation are flipped from 0 to 1 or vice versa. Thus, even genes sharing a common parent are likely to be slightly different, allowing a gradual evolution of the solution.

Figure 4.

Protein flexibility is important for ligand binding in the aldose reductase enzyme (grey surface representation) that plays a role in diabetes‐related complications. (a) PDB structure 1AH4, holo conformation of aldose reductase in complex with the coenzyme (shown as orange spheres), (b) PDB structure 1AH3, a few residues (shown as sticks) that surround the binding pocket of the aldose reductase change conformation to allow binding of pharmaceutical inhibitor tolrestat (shown as green spheres).

close

References

Abagyan R, Totrov M and Kuznetsov D (1994) ICM – a new method for protein modeling and design: applications to docking and structure prediction from the distorted native conformation. Journal of Computational Chemistry 15: 488–506.

Bohm H‐J and Stahl M (2002) The use of scoring functions in drug discovery applications. Reviews in Computational Chemistry 18: 41–87.

Bottegoni G, Kufareva I, Totrov M and Abagyan R (2009) Four‐dimensional docking: a fast and accurate account of discrete recetor flexibility in ligand docking. Journal of Medicinal Chemistry 52(2): 397–406.

Claussen H, Buning C, Rarey M and Lengauer T (2001) FlexE: efficient molecular docking considering protein structure variations. Journal of Molecular Biology 308(2): 377–395.

Corbeil CR, Englebienne P and Moitessier N (2007) Docking ligands into flexible and solvated macromolecules. 1. development and validation of FITTED 1.0. Journal of Chemical Information and Modeling 47(2): 435–449.

Davis IW and Baker D (2009) ROSETTALIGAND docking with full ligand and receptor flexibility. Journal of Molecular Biology 385(2): 381–392.

Fischer D, Lin SL, Wolfson HL and Nussinov R (1995) A geometry‐based suite of molecular docking processes. Journal of Molecular Biology 248(2): 459–477.

Halperin I, Ma B, Wolfson H and Nussinov R (2002) Principles of docking: an overview of search algorithms and a guide to scoring functions. Proteins 47(4): 409–443.

Jackson RM (2002) Q‐fit: a probabilistic method for docking molecular fragments by sampling low energy conformational space. Journal of Computer‐Aided Molecular Design 16(1): 43–57.

Jain AN (2003) Surflex: fully automatic flexible molecular docking using a molecular similarity‐based search engine. Journal of Medicinal Chemistry 46(4): 499–511.

Jain AN (2007) Surflex‐Dock 2.1: robust peformance from ligand energetic modeling, ring flexibility, and knowledge‐based search. Journal of Computer‐Aided Molecular Design 21(5): 281–306.

Jones G, Willett P, Glen R, Leach A and Taylor R (1997) Development and validation of a genetic algorithm for flexible docking. Journal of Molecular Biology 267(3): 727–748.

Kokh DB, Wade RC and Wenzel W (2011) Receptor flexibility in small‐molecule docking calculations. Wiley Interdisciplinary Reviews: Computational Molecular Science 1(2): 298–314.

Li X, Li Y, Cheng T, Liu Z and Wang R (2010) Evaluation of the performance of four molecular docking programs on a diverse set of protein‐ligand complexes. Journal of Computational Chemistry 31(11): 2109–2125.

Meiler J and Baker D (2006) ROSETTALIGAND: protein‐small molecule docking with full side‐chain flexibility. Proteins 65(3): 538–548.

Mooij WT and Verdonk ML (2005) General and targeted statistical potentials for protein‐ligand interactions. Proteins 61(2): 272–287.

Morris G, Goodsell D, Halliday R et al. (1998) Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. Journal of Computational Chemistry 19(14): 1639–1662.

Morris GM, Huey R, Lindstrom W et al. (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. Journal of Computational Chemistry 30(16): 2785–2791.

Moustakas DT, Lang PT, Pegg S et al. (2006) Development and validation of a modular, extensible docking program: DOCK 5. Journal of Computer‐Aided Molecular Design 20(10–11): 601–619.

Muegge I and Rarey M (2001) Small molecule docking and scoring. Reviews in Computational Chemistry 17: 1–60.

Österberg F, Morris GM, Sanner MF, Olson AJ and Goodsell DS (2002) Automated docking to multiple target structures: Incorporation of protein mobility and structural water heterogeneity in AutoDock. Proteins 46(1): 34–40.

Perola E, Walters WP and Charifson PS (2004) A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance. Proteins 56(2): 235–249.

Plewczynski D, Lazienwski M, Grotthuss MV, Rychlewski L and Ginalski K (2011) VoteDock: consensus docking method for prediction of protein‐ligand interactions. Journal of Computational Chemistry 32(4): 568–581.

Rarey M, Kramer B, Lengauer T and Klebe G (1996) A fast flexible docking method using an incremental construction algorithm. Journal of Molecular Biology 261(3): 470–489.

Sauton N, Lagorce D, Villoutreix BO and Miteva MA (2008) MS‐DOCK: accurate multiple conformation and rigid docking protocol for multi‐step virtual ligand screening. BMC Bioinformatics 9: 184.

Schnecke V, Swanson CA, Getzoff ED, Tainer JA and Kuhn LA (1998) Screening a peptidyl database for potential ligands to proteins with side‐chain flexibility. Proteins 33(1): 74–87.

Schneider G (2010) Virtual screening: an endless staircase? Nature Reviews Drug Discovery 9: 273–276.

Schulz‐Gasch T and Stahl M (2003) Binding site characteristics in structure‐based virtual screening: evaluation of current docking tools. Journal of Molecular Modeling 9(4): 47–57.

Sousa SF, Fernandes PA and Ramos MJ (2006) Protein‐ligand docking: current status and future challenges. Proteins: Structure, Function, and Bioinformatics 65(1): 15–26.

Teodoro M and Kavraki L (2003) Conformational flexibility models for the receptor in structure based drug design. Current Pharmaceutical Design 9(20): 1635–1648.

Totrov M and Abagyan R (2008) Flexible ligand docking to multiple receptor conformations: a practical alternative. Current Opinion in Structural Biology 18(2): 178–184.

Trott O and Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry 31(2): 455–461.

Velec HFG, Gohlke H and Klebe G (2005) DrugScoreCSD‐knowledge‐based scoring function derived from small molecule crystal data with superior recognition rate of near‐native ligand poses and better affinity prediction. Journal of Medicinal Chemistry 48(20): 6296–6303.

Further Reading

Fradera X and Mestres J (2004) Guided docking approaches to structure‐based design and screening. Current Topics in Medicinal Chemistry 4(7): 687–700.

Mobley DL and Dill KA (2009) Binding of small‐molecule ligands to proteins: “what you see” is not always “what you get”. Structure 17(4): 489–498.

Plewczynski D, Lazniewski M, Augustyniak R and Ginalski K (2011) Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database. Journal of Computational Chemistry 32(4): 742–755.

Schlick T (2010) Molecular Modeling and Simulation: An Interdisciplinary Guide. New York: Springer ISSN 0939‐6047.

Teague SJ (2003) Implications of protein flexibility for drug discovery. Nature Reviews Drug Discovery 2(7): 527–541.

Waszkowycz B, Clark DE and Gancia E (2011) Outstanding challenges in protein‐ligand docking and structure‐based virtual screening. Wiley Interdisciplinary Reviews: Computational Molecular Science 1(2): 229–259.

Contact Editor close
Submit a note to the editor about this article by filling in the form below.

* Required Field

How to Cite close
Dhanik, Ankur, and Kavraki, Lydia E(Aug 2012) Protein–Ligand Interactions: Computational Docking. In: eLS. John Wiley & Sons Ltd, Chichester. http://www.els.net [doi: 10.1002/9780470015902.a0004105.pub2]