This thesis describes the development and refinement of a number of techniques for molecular docking and ligand database screening, as well as the application of these techniques to predict the structures of several protein-ligand complexes and to discover novel ligands of an important receptor protein. Global energy optimisation by Monte-Carlo minimisation in internal co-ordinates was used to predict bound conformations of eight protein-ligand complexes. Experimental X-ray crystallography structures became available after the predictions were made. Comparison with the X-ray structures showed that the docking procedure placed 30 to 70% of the ligand molecule correctly within 1.5A from the native structure. The discrimination potential for identification of high-affinity ligands was derived and optimised using a large set of available protein-ligand complex structures. A fast boundary-element solvation electrostatic calculation algorithm was implemented to evaluate the solvation component of the discrimination potential. An accelerated docking procedure utilising pre-calculated grid potentials was developed and tested. For 23 receptors and 63 ligands extracted from X-ray structures, the docking and discrimination protocol was capable of correct identification of the majority of native receptor-ligand couples. 51 complexes with known structures were predicted. 35 predictions were within 3A from the native structure, giving correct overall positioning of the ligand, and 26 were within 2A, reproducing a detailed picture of the receptor-ligand interaction. Docking and ligand discrimination potential evaluation was applied to screen the database of more than 150000 commercially available compounds for binding to the fibroblast growth factor receptor tyrosine kinase, the protein implicated in several pathological cell growth aberrations. As expected, a number of compounds selected by the screening protocol turned out to be known inhibitors of the tyrosine kinases. 49 putative novel ligands identified by the screening protocol were experimentally tested and five compounds have shown inhibition of phosphorylation activity of the kinase. These compounds can be used as leads for further drug development.

Drug discovery has witnessed an increase in the application of in silico methods to complement existing in vitro and in vivo experiments, in an attempt to 'fail fast' and reduce the high attrition rates of clinical phases. Computer algorithms have been successfully employed for many tasks including biological target selection, hit identification, lead optimization, binding affinity determination, ADME and toxicity prediction, side-effect prediction, drug repurposing, and, in general, to direct experimental work. This thesis describes a multifaceted approach to virtual screening, to computationally identify small-molecule inhibitors against a biological target of interest. Conformer generation is a critical step in all virtual screening methods that make use of atomic 3D data. We therefore analysed the ability of computational tools to reproduce high quality, experimentally resolved conformations of organic small-molecules. We selected the best performing method (RDKit), and developed a protocol that generates a non-redundant conformer ensemble which tends to contain low-energy structures close to those experimentally observed. We then outline the steps we took to build a multi-million, small-molecule database (including molecule standardization and efficient exact, substructure and similarity searching capabilities), for use in our virtual screening experiments. We generated conformers and descriptors for the molecules in the database. We tagged a subset of the database as `drug-like' and clustered this to provide a reduced, diverse set of molecules for use in more computationally-intensive virtual screening protocols. We next describe a novel virtual screening method we developed, called Ligity, that makes use of known protein-ligand holo structures as queries to search the small-molecule database for putative actives. Ligity has been validated against targets from the DUD-E dataset, and has shown, on average, better performance than other 3D methods. We also show that performance improved when we fused the results from multiple input structures. This bodes well for Ligity's future use, especially when considering that protein structure databases such as the Protein Data Bank are growing exponentially every year. Lastly, we describe the fruitful application of structure-based and ligand-based virtual screening methods to Plasmodium falciparum Subtilisin-like Protease 1 (PfSUB1), an important drug target in the human stages of the life-cycle of the malaria parasite. Our ligand-based virtual screening study resulted in the discovery of novel PfSUB1 inhibitors. Further lead optimization of these compounds, to improve binding affinity in the nanomolar range, may promote them as drug candidates. In this thesis we postulate that the accuracy of computational tools in drug discovery may be enhanced to take advantage of the exponential increase of experimental data and the availability of cheaper computational power such as cloud computing.

Staphyloccocus aureus is an opportunisitic pathogen that causes soft skin and tissue infections (SSTI) such as endocarditis, osteomyelitis and meningitis. In recent years, the re-emergence of antibiotic-resistant S. aureus such as MRSA presents a formidable challenge for infection management worldwide. Amidst this global epidemic of antimicrobial resistance, several research efforts have turned their focus towards exploiting the cell-wall biosynthesis pathway for novel anti-bacterial targets. Recently, the lipoteichoic acid (LTA) biosynthesis pathway has emerged as a potential anti-bacterial target. LTA is an anionic polymer found on the cell envelope of Gram-positive bacteria. It comprises of repeating units of glycerol-phosphate (GroP) and is important for bacterial cell physiology and virulence. For example, it is critically involved in regulating ion homeostasis, cell division, host colonization and immune system invasion. Several reports showed that bacteria lacking LTA are unable to grow. At the same time, they suffer from severe cell division defects and also exhibit aberrant cell morphologies. The key protein involved in the LTA biosynthesis pathway is the Lipoteichoic acid synthase (LtaS). LtaS is located on the cell membrane of Gram-positive bacteria and can be divided into two parts: a transmembrane domain and an extra-cellular domain responsible for its enzymatic activity (annotated eLtaS). Given that LtaS is important for bacterial survival and there are no known eLtaS homologues in eukaryotic cells, this protein is an attractive antibacterial target. In 2013, a small molecule eLtaS inhibitor (termed 1771) was discovered. Although 1771 was able to deplete LTA production, the binding mechanism of 1771 to eLtaS remains unknown. Additionally, 1771 could only prolong the survival of infected mice temporarily because of its in vivo instability. Therefore, the need for finding more potent and metabolically stable inhibitors of eLtaS still remains. Computational-aided drug design (CADD) is a cost-effective and useful approach that has been widely integrated into the drug discovery process. The protein eLtaS lends itself to be a good target for CADD since its crystal structure and a known inhibitor (with limited structure-activity data) is available. In this work, I have targeted eLtaS using CADD methodology followed by prospective validation using various biophysical, biochemical and microbiological assays. My project can broadly be sub-divided into three phases: (a) identification of small molecule binding “hot spots”, (b) optimization of existing inhibitor and (c) discovery of new hits. Through a systematic use of different computational approaches, I modelled a plausible 1771-bound eLtaS complex and used the structural insights to generate new inhibitors against eLtaS. To this end, I discovered EN-19, which is a more potent inhibitor of eLtaS. Additionally, by targeting transient cryptic pockets predicted by Molecular Dynamic simulations, I have discovered a new inhibitor chemotype that seems to exhibit a different mode of action against eLtaS. Taken together, my work presents a computational platform for future drug design against eLtaS and reinforces the notion that targeting eLtaS is a viable strategy to combat Gram-positive infections.