Data-Driven Insights into Ligands, Proteins, and Genetic Mutations.

Abstract

Structure-based drug design (SBDD) is an essential component of many drug discovery programs. In SBDD, a large number of potential molecules are virtually screened against the known three-dimensional protein structure. Proper creation of ligand libraries and selection of target binding sites are critical for SBDD. This thesis focuses on knowledge-based approaches to improve SBDD in two aspects, the construction of the ligand libraries and analysis of the target binding sites used. First, this thesis presents ChemTreeMap, a visualization tool to explore structurally diverse molecules and mine for correlation between chemical structure and biological data. The visualization tool is applicable to a wide range of questions involving small molecule/drug binding and exploration and construction of ligand libraries. Experimental data and molecular properties can be interactively visualized with graph properties. With the help of this powerful tool, this thesis reports the findings on discriminating physicochemical properties between allosteric and orthosteric competitive molecules. It is observed that allosteric ligands are more hydrophobic, aromatic, and rigid. The result is useful to guide building new chemical libraries biased towards allosteric regulators. Thirdly, the selection of target binding sites of drug candidates needs to take account for possible interruption due to mutations which occur from non-synonymous single nucleotide polymorphisms (nsSNPs). Disease nsSNPs occur more frequently in a protein core or binding site, rather than the rest of the protein surface. The result can be used to imply the probability and consequence of nsSNP on new target binding sites.