Reverse docking

The basic principle of reverse docking is that the binding strength of a small-molecule ligand and a potential protein target is determined by their interaction energy (docking energy). To use reverse docking to predict the targets of a query molecule, a structure grid database of a large number of protein targets is normally required. Then, the query molecule is individually docked with each protein structure in the database. Each docking score is calculated. Finally, the protein targets are sorted according to their docking energy. Generally, a higher rank indicates a greater probability that the protein is a target of the query molecule. In contrast to shape screening and pharmacophore screening, reverse docking involves one level of mapping, which reflects the direct relationship between the query molecule and the target proteins (Figure ​(Figure2).2). However, it is a complex process that includes recognition of a binding site, construction of the docking grid, a molecular docking algorithm, docking score calculation and target evaluation, among other steps (Lee et al., 2016).

In most cases, the active site of a protein is already known and can be determined from its co-crystallized small-molecule ligand. However, for some apo-form structures without co-crystallized ligands, the docking program must first recognize the active binding site of these proteins. If the apo-form structure is from a protein for which other co-crystallized structures are available, its active site can also be identified from those protein structures with co-crystallized ligands. Otherwise, de novo detection of the active site of the apo-form structure is required. The literature describes multiple ways to achieve this task. For example, Wang et al. (2012) uses the “divide-and-conquer” method in idTarget to search the surface structure of the entire protein and possible allosteric structures to find potential binding sites. Kuntz et al. (1982) describes a method that was later used in INVDOCK (Chen and Zhi, 2001) to define a binding site by a group of overlapping probe spheres of certain radii, which fill up a cavity and whose inward-facing surfaces cover the van der Waals surfaces of the protein atoms at the interface. Active site recognition is very useful in attempts to dock a query molecule into cavities other than the binding pockets of known ligands, which can increase the diversity of the binding between the query molecule and protein targets and improve the accuracy of reverse docking.

The database of protein targets used in reverse docking can be a library of protein crystal structure grids with recognized binding sites determined by co-crystallized ligands or available cavities. We can build these databases by continuously downloading a series of protein crystal structures from the Protein Data Bank (PDB); the time-consuming human-computer interaction processes (such as the deletion of water molecules, the addition of hydrogen atoms, and energy optimization) can be accomplished by using a molecular docking program, and the protein structure grids are finally generated. Traditional molecular docking programs, such as DOCK (Allen et al., 2015), AutoDock (Di Muzio et al., 2017), Schrödinger (Schrödinger, 2018) and Discovery Studio (BIOVIA, 2017), can be used to construct a custom target database for reverse docking to search for potential targets of a small molecule. Alternatively, the protein target database can also be a simply processed, automatically constructed protein structure database, and the grids can be generated after the programmed identification of active sites in the process of reverse docking; an example is the idTarget in-house database (Wang et al., 2012). Notably, the lack of a universal protein structure grid database and the need to build a new one for each docking program are the main reasons that reverse docking cannot be used as often as traditional structure-based virtual screening.

At present, reverse screening uses two main types of molecular docking techniques, originally developed in DOCK and AutoDock. DOCK (Ewing et al., 2001) adopts a “geometry matching method” to perform molecular docking by complementing the geometric shape of the docking ligands with that of the protein active site, usually including hydrogen binding sites and locally accessible sites (Shoichet et al., 2010). The matching process is performed by an “anchor and grow algorithm,” in which the anchor is a rigid portion of the ligand that is used to initialize a pruned conformation search, and grow refers to the generation of multiple conformations of the remaining segments to simulate the flexible docking of the ligand (Ewing et al., 2001). AutoDock uses a “docking simulation method” that employs the “genetic algorithm” to sample the conformations of a docking molecule inside a grid of the receptor binding pocket (Willett, 1995). In this algorithm, the molecule starts randomly at the receptor surface and undergoes orientation, translation and rotation to cause conformational changes until the ideal binding pose with the best binding energy is found (Morris et al., 2015). Among three reverse docking programs, INVDOCK (Chen and Zhi, 2001) and TarFisDock (Li et al., 2006) use the DOCK geometry matching method for molecular docking, while idTarget uses the AutoDock genetic algorithm for reverse docking (Wang et al., 2012).

Currently, almost all molecular docking programs can perform flexible-ligand docking due to the small size of the ligands; however, these programs still have difficulty in performing molecular docking with a fully flexible protein. Therefore, depending on the flexibility of the receptor proteins, reverse docking can also be classified into two types: rigid protein docking and semi-flexible protein docking. Although reverse docking with a rigid receptor is fast, it ignores ligand/receptor-induced fit effects. An example of a rigid protein docking program for reverse screening is TarFisDock (Li et al., 2006). Reverse docking with semi-flexible receptors can be achieved by various methods such as side-chain rotations (Liu H. et al., 2015), stretching of active pocket residues (Halgren et al., 2004), and ensemble docking (Lorber and Shoichet, 1998). For example, INVDOCK allows the amino acid residues of the receptor binding sites to rotate with the entry of the ligand, thereby simulating the ligand induced-fit conformational changes of receptors (Chen and Zhi, 2001). idTarget uses the docking of a query molecule into an ensemble of different receptor crystal structures after clustering (Wang et al., 2012) and thus simulates semi-flexible receptor docking by possible binding of the molecule with the distinct locations of the active pocket residues of the receptor in its different structures.

The docking score between a query molecule and receptors is an evaluation criterion for ranking its potential targets in reverse screening. Docking energy is a major method of scoring docking poses and normally refers to the interaction energy between the ligand and protein but may also include the energy of the ligand or the energies of both the ligand and the protein (or a part of the protein such as the binding pocket). For example, INVDOCK evaluates the docking structure by calculating the interaction energy between the ligand and receptor (Chen and Zhi, 2001), whereas idTarget scores the docking pose by calculating the energy of the ligand, the protein binding pocket and the interaction between them (Wang et al., 2012). According to the principle that the most stable structure has the lowest energy, a more negative docking energy results in stronger binding between the ligand and protein. The docking energy is calculated based on energy functions, which are mainly divided into three types: molecular mechanics energy functions, empirical energy functions, and semi-empirical energy functions. The molecular mechanics energy functions are more comprehensive and are rigorously defined by the sum of terms with clear physical meaning, including bond stretching, angle bending, torsion angles, van der Waals forces, electrostatic interactions, desolvation, or hydrophobic interactions, conformational entropy, and potentially others (Huang and Zou, 2010; Wang et al., 2011). In reality, the molecular mechanics energy functions used in the docking programs may include only some of these terms. For example, TarFisDock uses energy functions including only van der Waals and electrostatic interaction terms (Li et al., 2006). Empirical energy functions comprise weighted energy terms whose coefficients are obtained by reproducing the binding affinities of a benchmark data set of protein-ligand complexes (Gilson et al., 1997; Gilson and Zhou, 2007). For example, INVDOCK uses an empirical energy function based on simple contact terms, including hydrogen bond and non-bond terms, to calculate the ligand-protein interactive energy as the binding affinity (Chen and Zhi, 2001). Semi-empirical energy functions combine some molecular mechanics energy terms with empirical weights and/or empirical functional forms and have been widely used in computational docking methods (Raha and Merz, 2005). For example, idTarget follows the AutoDock 4 robust scoring functions (Huey et al., 2007) and employs a semi-empirical free energy function that includes hydrogen bonding, electrostatics, desolvation, and torsional entropy, whose weighting coefficients are derived from regression analysis of the experimental binding affinity information (Wang et al., 2011). In addition, reverse docking allows visual assessment of the docking poses by analyzing the number of hydrogen bonds, the presence or absence of critical hydrogen bonds and pi-pi conjugation, etc., as in traditional virtual screening, to further assist target evaluation for a more accurate prediction.

Reverse docking considers key elements of both shape screening and pharmacophore modeling. It determines whether or not the size of a query molecule can fit inside the binding pocket of a protein target by docking and scores the interaction of the key pharmacophore groups in the molecule and the targets to perform target evaluation. Thus, reverse docking could be the most comprehensive of the three methods in principle. However, similar to traditional molecular docking, it also has the following shortcomings: incompleteness of the search space, inaccuracy of the scoring function, and extensive calculation (Lee et al., 2016). Relative to traditional docking, reverse docking has the additional problem that the sizes of the active pockets of proteins defined by co-crystallized ligands are inconsistent. Even if the docking pockets can be defined as being a universally equal size, the residue density of different protein binding pockets may vary, resulting in differences in the calculation ranges for the binding interaction energies. Therefore, reverse docking suffers from a rationality problem, as it is unable to normalize binding energies for the correct sorting of potential targets. Nevertheless, reverse docking can serve as an effective method to complement shape and pharmacophore screening when the protein structures are available.