Docking

Protein−ligand docking is a powerful tool to study and provide a proper understanding of Protein−ligand interactions [142]. Docking is regularly used in different stages of drug design strategies, such as to facilitate design of potentially active leads [143]. Detection of the best ligand poses and proper ranking of several ligands’ relative docking propensity are of great importance. Molecular docking, in practice, has two essential requirements: structural data, for candidate ligands and the protein target of interest and a procedure to estimate protein−ligand interaction poses and strengths [144, 145]. The RSCB Protein Data Bank (PDB) repository is the main source of protein target structures for docking studies [146, 147]. The number of structures deposited in the PDB repository has been rapidly increasing for many years. Currently there are >62,000 PDB entries of protein−ligand complexes, of which >60,000 were solved by X-ray and >1700 by NMR methods (other techniques were used to solve the remaining structures) [148].

The candidate ligands in docking procedures are generally small molecules. There was a rapid increase in the number of available synthesized chemical libraries after the development of combinatorial chemistry [149], which increased demand for the development of fast and cheap ways to test interactions with protein targets. The increasing numbers of PDB entries and of chemical database entries, coupled to the strong desire to be able to predict binding modes and binding affinities of ligands, has led to a wide acceptance of the routine use of docking methods as a crucial step in virtual screening [150]. Various molecular docking algorithms are available to predict protein−ligand poses and to rank them based on scoring functions implemented in each specific docking approach [151, 152]. Practically, docking software applications require protein−ligand sampling algorithms in order to be able to generate acceptable ligand poses.

30

Ligand sampling algorithms, for ligand pose generation and placement in the active site, are of three types: shape matching [153, 154], systematic search [155], and stochastic algorithms [156].

Ligand conformational sampling is an essential step that generates a ligand multi- conformer database to be used in ligand sampling. Conformational search is sometimes performed as a separate step before docking [157] or can be implemented as an integrated part of the docking algorithm [158]. Protein sampling refers to the allowed degree of binding site flexibility. Docking algorithms may consider the protein as a rigid body [159], as a soft body [160, 161], to have flexible side chains [162], or to have certain flexible domains [163-165]. Alternatively, protein flexibility can be represented by using multiple conformers or ensembles of rigid protein structures [166]. Various classes of scoring functions are used to estimate the binding affinities of ligand poses [167]. Scoring functions can be classified as force field-based [168, 169], empirical [170, 171], knowledge-based [172, 173], clustering and entropy-based [174-176], or consensus scoring methods [177-179]. Active site water molecules can be considered another aspect of docking target flexibility [180, 181]. Incorporation of active site water molecules in the docking procedure is challenging. Each water molecule needs to be analyzed to check if it is an integral part of the protein or just an artifact of the crystallization procedure.

There are several publications regarding the utilization of docking in kinase research. McInnes et al. [182] were able to design a selective CDK4 inhibitor over CDK2 using literature data, crystal structures, homology models and docking experiments. The authors proposed that the acidic residue Glu144 in CDK4 is a key amino acid residue for ligand binding and selectivity over CDK2, which has Gln131 in the same position (Figure 1.12). Trying to understand ligand binding to MEK1 is another example of the utilization of docking experiments [183]. Ligand

31

docking into MEK1 (PDB ID: 1S9J [184] (Figure 1.12)) was performed using Glide [155]. The results explained how the docked compounds fit into the binding pocket with the hydrophobic ring occupying the hydrophobic pocket formed by Leu118, Ile126, Val127, Phe129, Ile141, Met143, Phe209, and Val211.

Figure 1.12. CDK4 (PDB ID: 2W9Z [185]) has an acidic residue compared to the neutral

residue in CDK2 (PDB ID: 1AQ1 [94]) in the same position, which may have a role in the ligand binding selectivity (left). The binding pocket of MEK1 (right, PDB ID: 1SJ9) showing the ring of ligand ring occupying the hydrophobic pocket (shown in cyan surface)

Molecular docking studies were performed to identify new chemotype inhibitors for EphA3 and non-phosphorylated Abelson tyrosine kinase (Abl1) [186]. MD simulations of the complex of the catalytic domain of a tyrosine kinase receptor, ephrin type-A receptor 3 (EphA3), and a manually docked type II inhibitor, were performed to get a set of DFG-out structures. This was followed by selection of a single snapshot based on the docking result of reported type II inhibitors. The molecular database was then filtered using a pharmacophore model, followed by high-throughput docking and ranking of the docked poses based on van der Waals efficiency. A series of 5-(piperazine-1-yl)isoquinoline derivatives was identified as a new class for EphA3 and non-phosphorylated Abelson tyrosine kinase (Abl1).

32

Molecular docking combined with a machine learning algorithm was used to predict potential kinase inhibitors in Leishmania spp. [187] The authors used ChEMBL, therapeutic target database (TTD) and DrugBank as sources for kinase inhibitors and targets. Support vector machine in conjunction with feature selection techniques were used, and the enzymes were used as the training set. A set of druggable kinases was identified in five sequenced Leishmania species. After target selection, and homology modeling of target Leishmania kinases, the compounds were docked into the models using AutoDock 4, AutoDockVina [188] and DOCK [189].

Another example of docking-based virtual screening is the discovery of Rho-kinase inhibitors [190]. Rho kinases (ROCK1 and ROCK2) belong to the serine/threonine (Ser/Thr) protein kinase family that exert an essential role in the organization of actin skeleton. Because of these activities Rho kinases are considered attractive targets for cancer, renal disease, hypertension, ischemia, and stroke. Molecular docking was performed as a virtual screening tool to screen molecular databases. Small molecule inhibitors of ROCK1 were identified and submitted to biological testing and in vitro assays. The protein–ligand interaction pattern was characterized by using MD simulations and free energy decomposition analysis.