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Formally, the protein-ligand docking problem is the following: We are given a geometric and chemical description of a protein and an arbitrary small organic molecule. We want to determine computationally whether the small molecule will bind to the protein, and if so, we would like to estimate the geometry of the bound complex, as well as the affinity of the binding. Most algorithms include two components: a search technique to find the optimal placement of the ligand in the binding pocket of the protein, and a scoring function to rate each placement, as well as to rank candidate ligands against each other. The remainder of this module will cover a range of docking approaches, starting with the simplest, rigid-receptor methods, which make very restrictive assumptions about the dynamics of the protein and candidate ligands, and then moving on to more complex approaches that allow the receptor to change conformation. The latter methods have the potential to identify ligands that might be missed by simpler approaches.

Trypsin, a protease involved in digestion (PDB structure ID 3ptb)
Benzamidine, a trypsin inhibitor (PDB structure ID 3ptb)
Stereo view of benzamidine (red) docked in the active site of trypsin (blue) (PDB structure ID 3ptb)

Components of a docking program

As stated earlier, protein-ligand docking methods generally consist of two components: a ligand placement algorithm to enumerate and test possible poses for the ligand in the protein's active site, and a scoring function to evaluate each placement, as well as to evaluate one candidate ligand against another. Each of these componets is introduced in more detail below.

Ligand placement algorithm

The first part of any docking technique is a method to place the ligand in various candidate poses in the binding pocket of the receptor. Although each placement could be completely random and independent, most algorithms either use heuristics based on the chemistry or geometry of the atoms involved (FlexX, DOCK), or use a standard optimization technique such as simulated annealing or a genetic algorithm (Autodock, Gold). A few use explicit molecular dynamics simulation.

Scoring function

The scoring function provides a way to rank placements of ligands relative to one another. Ideally, the score should correspond directly to the binding affinity of the ligand for the protein, so that the best scoring ligands are the best binders. Scoring functions generally fall into three categories:

Explicit force field scoring function

Modified versions of both the AMBER and CHARMM force fields (see this module for more on force fields) have been used as scoring functions. For some complexes, they have been found to provide a good approximation of the free energy of binding. Early versions of Autodock used a subset of the AMBER force field.

Empirical scoring functions

The score is expressed as a weighted sum: i i interactions ΔG i f i l, r where ΔG i is an empirically determined weight for the ith interaction type. It corresponds to the average free energy contribution of a single interaction of that type over the set of receptor-ligand systems used to normalize the scoring function. The types of interactions that might be included in an empirical scoring function include hydrogen bonds, electrostatic interactions, hydrophobic contacts, solvent exclusion volume, and electrostatic interactions, among others. Examples of empirical scoring functions include the Autodock 3.0 scoring function (see below), Protherics Inc.’s ChemScore [1] , and Boehm’s SCORE1 [2] .

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