**4. Discovery of new drugs using virtual screening**

Once the protein target is identified, the next step is finding an effective and non-toxic inhibitor that can be administrated in human patients. However, the process for identifying compounds, as in the case for drug targets, requires expensive equipment for testing millions of chemicals with a final result of few *hits* that can advance in further drug development stages. This process is unpractical and time consuming. A computational strategy has been used to improve the drug search by using computational chemistry to select those compounds that are likely to work in the experimental setting, this computational technique is called *virtual screening*. The aim of this technique is to identify novel small molecules that could bind the target of interest. This is carried out by *docking* compound libraries over the protein structure of the target, using optimization algorithms that search for the best conformation of the target and the ligand, which by definition has the lowest conformational energy (McInnes 2007). The libraries are usually compounds available from chemical vendors or predicted in some cases.

The procedure for virtual screening involves the selection of the target as a first step. The target can be chosen by the results from RNAi screenings or from computational approaches as the ones previously described. The target must have a 3D structure with an acceptable resolution, which is critical for the accuracy of the predictions. The structures determined by experimental methods can be easily accessed through the Protein Data Bank (PDB; http://www.pdb.org/). This database contains protein structures from different organisms determined by X-ray crystallography or NMR methods. In addition, predicted structures by homology-modeling methods can be found in the ModBase database (http://modbase.compbio.ucsf.edu/) (Pieper., et al. 2009). In contrast, the resources for compounds or *ligands* are restricted due to patent protection. However an interesting resource that contains millions of ligands ready to use for virtual screening experiments is the ZINC database (http://zinc.docking.org/) (Irwin & Shoichet 2005). This database includes commercial ligands and it is also free for academic use. This combination of resources of protein structures and ligand databases facilitates enormously the development of virtual screening projects which can be very productive for finding new drugs, especially for neglected diseases such as leishmaniasis.

The tools employed for virtual screening are also very diverse. One very popular tool is AutoDock (Goodsell., et al. 1996), which was developed by the Scripps Institute and used in several virtual screening projects, demonstrating good performance and accuracy. However, a recent study (Chang., et al. 2010) compared the accuracy and reproducibility of AutoDock

ergosterol). Interestingly, proteins belonging to this process in *Leishmania* were also detected

Double knockouts were also simulated to identify lethal combination of genes. Out of 152,520 double deletions, 19,341 were lethal. From this group, 19,285 double deletions were trivial lethal, which means that at least one of the genes involved was lethal in a single deletion. There were 56 non-trivial lethal double deletions that could be interesting to test experimentally. The main participation of these double knockouts was in the lipid and carbohydrate metabolism with 57.6% of the genes in these groups. One explanation for the large number of double deletions that were not essential is the high degree of redundancy in the network. These results show the utility of a different methodology that uses

Once the protein target is identified, the next step is finding an effective and non-toxic inhibitor that can be administrated in human patients. However, the process for identifying compounds, as in the case for drug targets, requires expensive equipment for testing millions of chemicals with a final result of few *hits* that can advance in further drug development stages. This process is unpractical and time consuming. A computational strategy has been used to improve the drug search by using computational chemistry to select those compounds that are likely to work in the experimental setting, this computational technique is called *virtual screening*. The aim of this technique is to identify novel small molecules that could bind the target of interest. This is carried out by *docking* compound libraries over the protein structure of the target, using optimization algorithms that search for the best conformation of the target and the ligand, which by definition has the lowest conformational energy (McInnes 2007). The libraries are usually compounds

The procedure for virtual screening involves the selection of the target as a first step. The target can be chosen by the results from RNAi screenings or from computational approaches as the ones previously described. The target must have a 3D structure with an acceptable resolution, which is critical for the accuracy of the predictions. The structures determined by experimental methods can be easily accessed through the Protein Data Bank (PDB; http://www.pdb.org/). This database contains protein structures from different organisms determined by X-ray crystallography or NMR methods. In addition, predicted structures by homology-modeling methods can be found in the ModBase database (http://modbase.compbio.ucsf.edu/) (Pieper., et al. 2009). In contrast, the resources for compounds or *ligands* are restricted due to patent protection. However an interesting resource that contains millions of ligands ready to use for virtual screening experiments is the ZINC database (http://zinc.docking.org/) (Irwin & Shoichet 2005). This database includes commercial ligands and it is also free for academic use. This combination of resources of protein structures and ligand databases facilitates enormously the development of virtual screening projects which can be very productive for finding new drugs, especially

The tools employed for virtual screening are also very diverse. One very popular tool is AutoDock (Goodsell., et al. 1996), which was developed by the Scripps Institute and used in several virtual screening projects, demonstrating good performance and accuracy. However, a recent study (Chang., et al. 2010) compared the accuracy and reproducibility of AutoDock

by homology and interactome analysis showing consistency between methods.

mathematical modeling for the detection of essential genes in metabolism.

**4. Discovery of new drugs using virtual screening** 

available from chemical vendors or predicted in some cases.

for neglected diseases such as leishmaniasis.

against a recently developed tool, also freely available, called AutoDock Vina (Trott & Olson 2010). The experiments consisted of screening ligands with known activity against the HIV protease and *decoys* or non-binders. Autodock Vina performed very well in terms of speed, being ~10 times faster, and more accurate in ranking larger molecules compared to AutoDock. We are currently using AutoDock Vina in a virtual screening project called "Drug Search for Leishmaniasis" in association with IBM-World Community Grid (http://www.worldcommunitygrid.org/research/dsfl/overview.do) to speed up the process of finding new active compounds.

As an example of the application of this strategy in *Leishmania,* a recent study demonstrated the utility of virtual screening to identify potential MAPK inhibitors. The target, MAPK was first modeled by using *homology modeling* techniques. Essentially, the technique predicts the 3D structure of a particular protein by finding sequence homology to a model protein with experimentally determined structure. This model was refined by molecular dynamics. Structural features, such as ATP binding pocket, phosphorylation lip, and common docking site were identified. Virtual screening was carried out using this target with several compounds from the class of ATP inhibitors. Interestingly, the docking analysis suggested that the indirubin class of molecules could act as putative inhibitors of *Leishmania* MAPK. By testing this result experimentally, the authors found reasonably good correlation between *in vitro* activity and calculated binding energy for indirubin class of inhibitors obtained in the virtual screening study. These molecules make strong hydrogen bonding interactions with Lys43, Arg57, Asp155, Glu94, and Ile96 amino acid residues of the *Leishmania* MAPK model. These residues belong to the catalytic domain and inhibition of the catalytic domain leads to impaired kinase activity (Awale., et al. 2010). This is a clear example of the synergy between computational and experimental methods to accelerate drug discovery.
