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Background
Transient non-covalent interactions are critical for biological processes. The sequencing of a variety of genomes and the development of proteomics techniques have enabled scientists to study these interactions on the widest scales [1]. Advances in X-ray crystallography, nuclear magnetic resonance spectroscopy, and other experimental structure techniques provide the ability to study these interactions at an atomic level of detail [2]. One important application of these advances is the design of small molecules that interact with cellular processes to modify biological activity and treat disease.

The drug discovery process typically requires between 10-15 years from early discovery until FDA approval [3]. Computational tools - such as virtual screening, homology modeling and cheminformatics - are applied both to facilitate various stages of research and to create models that explain experimental data [4-6]. Molecular docking, which can broadly be defined as the prediction of the orientation of two molecules with respect to one another, is a computational technique that has been successfully used in both of these capacities [7]. In drug design applications, one molecule is typically a protein or nucleic acid drug target - the receptor - and the other is a small molecule that will be examined as a putative drug - the ligand. Docking is used to identify novel ligands that interact with a biomolecular target and to predict the geometric position (binding mode) of ligands with respect to the target of interest.

DOCK [8] is one example of a family of molecular docking packages available, which includes Glide, FlexX, and GOLD [9-11]. Our motivation in developing DOCK is to provide a modular docking package that permits the easy development of new scoring functions, search algorithms, and analysis tools. Thus, each functional unit of the DOCK algorithm was implemented as a self-contained and portable module that interacts with the user through a well-defined interface. The object-oriented language C++ was chosen to allow each component of the DOCK algorithm to be implemented as a class, which encapsulates both the data structures and functions [12]. DOCK 5 incorporates several new routines, including parallelization of the algorithm through an external library, modification of the ligand structural class to enable greater user control over sampling, and clustering of the final results by root mean square deviation. In Dock 6 (released in July 2006), additional scoring functions are implemented including one that communicates smoothly with the Amber [13] molecular dynamics package. Going hand in hand with the development of DOCK itself is the development of tools to facilitate the preparation of DOCK input and examination of DOCK output. Collaboration with the RBVI has produced the Dock Prep extension to Chimera for the former need and the ViewDock extension for the latter.

Dock Prep
Researchers preparing an experimentally-derived receptor structure for input to DOCK must typically perform several tasks. These tasks include:

• Delete solvent molecules
• Delete alternate locations of atoms
• Add hydrogens
• Change selenomethionine (MSE) to methionine (MET)
• Properly type atoms for receptor and potential cofactors
• Assign partial charges to atoms in receptor and potential cofactors
• Save the prepared receptor file in Mol2 format

The last task, in particular, has constrained DOCK users to using the commercial program Sybyl, which in turn has severely constrained the use of DOCK in the academic community. The motivation for developing Dock Prep was to streamline the tasks outlined above into a tool that would be freely available to academic researchers. The last four tasks (MSE→MET, atom typing, charge assignment, Mol2 format) required new functionality to be added to Chimera, including the use of Antechamber [14] for charge calculation for nonstandard residues and small molecules and conversion of Chimera atom types to Sybyl atom types. Although the first three tasks listed above were already possible to perform in Chimera, their integration into a single tool (along with the four new tasks) made the entire receptor preparation process considerably simpler. In addition, Dock Prep uses Chimera's recently-developed capability to place hydrogens using hydrogen-bond guidance, which has been demonstrated to improve docking success rates [8].

A major planned improvement to Dock Prep is the addition of missing receptor side chains, either by offering choices from a side-chain rotamer library or by building out the side chain and then minimizing it. Another planned addition to Chimera that will be a major help to DOCK users is the development of a tool to create the active site description for DOCK. This tool will take points from a user-selected part of a molecular surface of the active site, as well as the normal vectors to those points, and send them as input to the "sphgen" component of DOCK, which in turn creates a set of spheres describing the negative image of the active site. Chimera will be able to display these spheres and then allow the user to select the best set for the receptor being studied, and then write the result as an input file for DOCK.

ViewDock
ViewDock has continued to be improved with the addition of ligand filtering based on the number of hydrogen bonds formed with the receptor, and with histogram depiction of the component DOCK scores of ligands - also allowing filtering using selections from one or more histograms. Although development of features for ViewDock has not been as active recently as with Dock Prep, this is still an important area of collaborative development with the RBVI.

References:

1. Kopec, K.K., Bozyczko-Coyne, D., and Williams, M., Biochem. Pharmacol., 69 (2005) 1133-1139.
2. Congreve, M., Murray, C.W., and Blundell, T.L., Drug Discovery Today, 10 (2005) 895-907.
3. Kraljevic, S., Stambrook, P.J., and Pavelic, K., EMBO Rep, 5 (2004) 837-42.
4. Schnecke, V. and Bostrom, J., Drug Discovery Today, 11 (2006) 43-50.
5. Hillisch, A., Pineda, L.F., and Hilgenfeld, R., Drug Discovery Today, 9 (2004) 659-669.
6. Posner, B.A., Curr. Opin. Drug Discovery Dev., 8 (2005) 487-494.
7. Alvarez, J.C., Curr Opin Chem Biol, 8 (2004) 365-70.
8. Moustakas, D.T., Lang, P.T., Pegg, S., Pettersen, E., Kuntz, I.D., Broojimans, N., and Rizzo, R.C., J. Comp.-Aided Mol. Design, submitted
9. Verdonk, M.L., Cole, J.C., Hartshorn, M.J., Murray, C.W., and Taylor, R.D., Proteins, 52 (2003) 609-623.
10. Friesner, R.A., Banks, J.L., Murphy, R.B., Halgren, T.A., Klicic, J.J., Mainz, D.T., Repasky, M.P., Knoll, E.H., Shelley, M., Perry, J.K., Shaw, D.E., Francis, P., and Shenkin, P.S., J. Med. Chem., 47 (2004) 1739- 1749.
11. Halgren, T.A., Murphy, R.B., Friesner, R.A., Beard, H.S., Frye, L.L., Pollard, W.T., and Banks, J.L., J. Med. Chem., 47 (2004) 1750-1759.
12. Lischner, R. C++ in a nutshell. 1st ed, Sebastopol, CA.: O'Reilly Media, Inc., 2003.
13. Case, D.A., Cheatham III, T.E., Darden, T., Gohlke, H., Luo, R., Merz Jr., K.M., Onufriev, A., Simmerling, C., Wang, B., and Woods, R., J. Comp. Chem., 26 (2005) 1668-1688.
14. Wang, J., Wang, W., Kollman, P.A., Case, D.A., J. Mol. Graph. Model., 25 (2006) 247-260.

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