The development and implementation of a range of molecular docking algorithms, based on different search methods (Taylor et al. 2002, Halperin et al. 2002) was observed in the last few years. This approach has had several recent successes in drug discovery (Sechi et al., 2005; Liu et al., 2005).

In the field of molecular modeling, docking is a method which predicts the preferred orientation of one molecule to a second when bound to each other to form a stable complex (Lengauer and Rarey, 1996). Knowledge of the preferred orientation in turn may be used to predict the strength of association or binding affinity between two molecules using for example scoring functions.

Docking is frequently used to predict the binding orientation of small molecule drug candidates to their protein targets in order to in turn predict the affinity and activity of the small molecule. Hence docking plays an important role in the rational design of drugs (Kitchen et al., 2004). Given the biological and pharmaceutical significance of molecular docking, considerable efforts have been directed towards improving the methods used to predict docking.

Evaluation of existing docking algorithms can assist in the choice of the must suitable docking programs for any particular study. Effectively, several studies estimating and comparing the accuracies of protein-ligand programs like Dock, ICM, Gold have been reported (Perola, E. et al. , 2004 ; Bursulaya, B. D. et al., 2003).

The goal of this study was to evaluate the ability of ArgusLab, a freely distributed molecular modeling package in which molecular docking is implemented, to reproduce crystallographic binding orientations and to compare its accuracy with that of a widely well established docking package, Surflex.

ArgusLab4.0 has fast become a favorite introductory molecular modeling package with academics mainly because of its user-friendly interface and intuitive calculation menus (Thompson, 2004). The ArgusDock docking engine, implemented in ArgusLab, approximates an exhaustive search method. Flexible ligand docking is possible with ArgusLab, where the ligand is described as a torsion tree and grids are constructed that overlay the binding site. Ligand’s root node (group of bonded atoms that do not have rotatable bonds) is placed on a search point in the binding site and a set of diverse and energetically favorable rotations is created. For each rotation, torsions in breadth-first order are constructed and those poses that survive the torsion search are scored. The N-lowest energy poses are retained and the final set of poses undergoes coarse minimization, re-clustering and ranking.

Surflex1.3 method is based on an incremental construction. There are three steps in performing dock : choosing how to identify the active site of the protein and constructing a docking target to which to match molecules (called a protomol), docking one or many molecules and post-processing the results.

Each of the basic tasks is controlled by a series of usersettable parameters, but the built-in defaults are reasonably robust to many different protein/ligand pairs. All input molecules must be protonated as expected at physiological pH including non-polar hydrogens. The protonation state may strongly affect docking. Docking requires a ligand, a protomol, and a protein. Surflex will fragment the molecule, search the fragments, dock the fragments, and construct the molecule in the active site of the protein. The final output is the top ten scoring conformations (Jain, A.N., 2003).

The best ranking poses predicted by the two programs Arguslab and Surflex are shown in the figure 1 and their root mean square deviation (RMSD) values from the original crystallographic pose determined. It can be observed that Surflex surpasses Arguslab. For RMSD interval < 2Å, the difference in docking accuracies between the two programs is so important but decrease significantly in RMSD interval < 3Å.

Figure 2 shows the evaluation of the docking algorithms for their sampling accuracy. The percentage of poses with RMSD within 2Å from the experimental structure was 63% for Surflex and only 33% for ArgusLab. This confirms the results reported by earlier studies, Surflex appears to be highly efficient in terms of sampling (Kellenberger, E. et al., 2004). However, under less rigorous conditions, the performance of ArgusLab is vastly improved with 62% of the top ten poses falling within 3Å of the crystallographic pose. This suggests that ArgusLab still gives some biological results and can be used in educational demonstrations.

Another kind of analysis we have carried out is the effect of a ligand parameter on docking accuracy (figure 3). It is a well-known fact that as the number of rotatable bonds of the ligand increases, the docking accuracy falls since a much larger conformational space has to be sampled. The complexes in the present study were divided into three groups, ligands with 1 to <10 rotatable bonds, ligands with 11 to < 15 rotatable bonds and those with > 15 rotatable bonds. The results confirm earlier works. Indeed for all algorithms, the docking accuracy decreases when the number of rotatable bonds increases. Also in all cases, accuracy of Surflex is approximately double that of ArgusLab. This decrease is very marked when the number of rotatable bonds exceed 15. Though, an essential remark is that docking time in ArgusLab is typically much shorter than that of Surflex.

To further evaluate these docking programs, another test we have conducted is to study the chemical nature of their protein-ligand interactions and then to check the success rate of each scoring function (figure 4). The classification is aided by using X-Score. For any given protein-ligand complex, if the contribution of the H-bond term in X-Score is 50% larger than the hydrophobic term, it is classified as the “hydrophilic” type. If the contribution of the hydrophobic term is 50% larger than the H-bond term, it is classified as the “hydrophobic” type. Otherwise, the complex is considered to have mixed hydrophilic and hydrophobic factors in the protein-ligand interaction and thus is classified as the “mixed” type. We have used X-Score for this classification process because it is the only one with open source codes, so we can analyze the hydrophobic and the hydrophilic terms conveniently. Hydrogen bond driven complexes are the best results given by Surflex (72%) and also for hydrophobicburial driven ones (63%). There is no perceptible change in the docking accuracy of ArgusLab with degree of hydrogen bonding.

Studies for determination of IC₅₀ and MIC, in specialized laboratory, are needed to confirm these in silico results.

Our results prove that the Surflex program does a rational job in docking and should assist significantly the drug discovery process. Its use for molecular docking appears to be most valuable. This study shows that the commercial package surpasses the freely available docking program in all parameters tested. The study also reveals that, in less scrupulous conditions, ArgusLab can be used for demonstration of molecular docking method to beginners in this area owing to its easiness to use graphical user interface. Moreover, some future advances can be made in this program at the expense of the docking time.