Computer Aided Drug Design

During the last decades the field of drug discovery process that direct to new ligands finding turns into the modern science employing of computational and experimental approaches. The main experimental methods are combinatorial chemistry and high-throughput screening. Computer-assisted drug design (CADD) uses computational chemistry to discover, enhance, or study drugs and related biologically active molecules. The most fundamental goal is to predict whether a given molecule will bind to a target and if so how strongly. The CADD techniques NIMML have applied to discover novel therapeutics against inflammatory bowel disease include protein homology modeling, receptor-based virtual screening, reverse docking, and molecular dynamics.

Homology Modeling

Homology modeling refers to constructing an atomic-resolution model of the target protein from its amino acid sequence according to experimental three-dimensional structure of a related homologous protein. Homology modeling relies on the identification of one or more known protein structures likely to resemble the structure of the query sequence, and on the production of an alignment that maps residues in the query sequence to residues in the template sequence (Fig. 1). It has been shown that protein structures are more conserved than protein sequences amongst homologues. The homology modeling procedure can be broken down into four sequential steps: template selection, target-template alignment, model construction, and model assessment. The homology modeling programs we mainly use are SWISS-MODEL and MODELLER. We have successfully performed homology modeling to construct a three-dimensional structure of Lanthionine synthetase component C-like protein 2 (LANCL2) using the crystal structure of LANCL1 as a template (Fig. 2).

Receptor-based Virtual Screening

The predominant technique for the identification of new lead compounds in drug discovery is the physical screening of large libraries of chemicals against a biological target (high-throughput screening), which is very costly and with mixed results. Recently, successes in predicting new ligands and their receptor-bound structures make of receptor-based virtual screening a widely used approach in drug discovery.  The basic procedure of receptor-based virtual screening is to dock compounds in large libraries into the structure of receptor targets by docking computer programs. The docking programs we mainly use are Autodock and Autodock Vina. Each compound is sampled in thousands to millions of possible configurations and scored on the basis of its complementarity to the receptor. Of the hundreds of thousands of molecules in the library, tens of top-scoring predicted ligands are subsequently tested for activity in an experimental assay. We have successfully applied receptor-based virtual screening to screen several compound libraries for discovering novel LANCL2 agonistic lead compounds (Fig. 3). The virtual libraries we have applied include NCI (National Cancer Institute) Diversity Set II, ChemBridge, ZINC natural products, and FDA-approved drugs databases.

Reverse Docking

Reverse docking is one novel technology that to dock a compound with a known biological activity into the binding sites of all the 3D structures in a given protein database. Protein ‘hits’ so identified can then serve as potential candidates for experimental validation. The potential drug target database (PDTD) is a dual function database that associates an informatics database to a structural database of known and potential drug targets. The target proteins collected in PDTD were selected from the literature, and from several online databases, such as DrugBank and Therapeutic Targets Database (TTD). Target Fishing Dock (TarFisDock) is a web-based tool for seeking potential binding proteins for a given ligand. It applies a ligand-protein reverse docking strategy to search out all possible binding proteins for a small molecule from the PDTD. TarFisDock was developed on the basis of DOCK (version 4.0) program. We have applied reverse docking to identify additional targets for LANCL2 ligands, one potential anti-inflammatory drug (Table 1). The reverse docking procedure is as follows: 1) The LANCL2 ligand structure file in sdf format was downloaded from PubChem. The LANCL2 ligand structure file was transformed to the standard mol2 format using the Chimera program. 2) TarFisDock docked ligand into the possible binding sites of proteins in the target list. Putative binding proteins are selechttp://www.nimml.org/technologies/bioinformatics-and-hpc/computer-aided-drug-design/ted by ranking the values of the interaction energy, which is composed of van der Waals and electrostatic interaction terms.

Table 1. Potential therapeutic targets of LANCL2 ligands.

Molecular Dynamics

Molecular dynamics are most often used to predict the conformation of the small molecule and to model conformational changes in the biological target that may occur when the small molecule binds to it. Semi-empirical, ab initio quantum chemistry methods, or density functional theory are often used to provide optimized parameters for the molecular dynamics calculations and also provide an estimate of the electronic properties of the drug candidate which will influence binding affinity. Molecular dynamics methods may also be used to provide semi-quantitative prediction of the binding affinity. We are applying molecular dynamics to investigate the detailed interactions between LANCL2 and its agonists (Fig. 4).

References:

1. Lu P, Bevan DR, Lewis SN, Hontecillas R, Bassaganya-Riera J (2011) Molecular modeling of lanthionine synthetase component C-like protein 2: a potential target for the discovery of novel type 2 diabetes prophylactics and therapeutics. J Mol Model 17: 543-553. [PubMed]
2. Bassaganya-Riera J, Guri AJ, Lu P, Climent M, Carbo A, et al. (2011) Abscisic acid regulates inflammation via ligand-binding domain-independent activation of peroxisome proliferator-activated receptor gamma. J Biol Chem 286: 2504-2516. [PubMed]
3. Lu P, Hontecillas R, Bevan DR, Lewis SN, Bassaganya-Riera J (2011) Computational Modeling-Based Discovery of Novel Classes of Anti-Inflammatory Drugs That Target Lanthionine Synthetase C-Like Protein 2. PLoS ONE 2012;7(4):e34643. [PubMed]
4. Lewis SN, Brannan L, Guri AJ, Lu P, Hontecillas R, et al. (2011) Dietary α-Eleostearic Acid Ameliorates Experimental Inflammatory Bowel Disease in Mice by Activating Peroxisome Proliferator-Activated Receptor-γ. PLoS ONE 6(8): e24031. doi:10.1371/journal.pone.0024031. [PubMed]
5. Lewis SN, Bassaganya-Riera J, Bevan DR (2010) Virtual Screening as a Technique for PPAR Modulator Discovery. PPAR Res 2010: 861238. [PubMed]