Protein–Ligand Interactions: Computational Docking


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).



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