Claire Shoemake

ABSTRACTTherapeutic areas for Protein Kinase inhibitors include cancer. A study has indicated that isojacareubin, a plant-derived natural product, inhibits Protein Kinase C. This study aimed to design in silico protein kinase inhibitors using isojacareubin as a molecular template. Virtual screening and de novo design were carried out during the study. A total of three hundred hits were produced from virtual screening using the best binding conformer of isojacareubin. Two hundred molecules were generated de novo from each seed structure of isojacareubin. Lipinski Rules compliant molecules were chosen from each study, and thus, were orally bioavailable. The binding affinities (pKd) of the Lipinski Rules compliant molecules produced ranged from 7.16 to 10.00. The molecules require further validation through in silco molecular dynamics and confirmed through in vitro assays. Keywords: Protein Kinase C; Isojacareubin; Bisindolylmaleimide inhibitor; Conformational Analysis; Virtual Screening; De novo.

K-Ras serves as an important component of signalling pathways involved in cell cycle control. Proper functioning K-Ras is regulated by phosphodiesterase δ (PDEδ). Deltarasin binds to this prenyl-binding protein thus inhibiting its interaction with K-Ras and hence disrupting Ras signalling. The objective of this study is to use Deltarasin as a template for further iteration of the design of novel drugs with potential clinical use in the management of malignancies. Deltarasin was constructed using SYBYL-X® V1.2, followed by analysis of the critical interactions with the amino acids lining the Ligand Binding Pocket (LBP). Seeds were modelled based on the Deltarasin scaffold and Virtual Screening (VS) was used to identify ‘hits’, using the same molecule as a template. SYBYL-X®, X-SCORE®, LigBuilder®, Visual Molecular Dynamics (VMD), Accelrys® Draw, Accelrys® Discovery Studio v3.5, Protein Data Bank and ZINCPharmer® were all used to generate results. The main outcome measures of this research project are to discover and optimise in silico high binding affinity of PDEδ inhibitory drug molecules, as well as molecule display, Ligand Binding Affinity (LBA) and Ligand Binding Energy (LBE) calculations, seed generation and ultimately de novo design. Based on reviewed SAR studies, nine seeds were generated using SYBYL-X® V1.2. The POCKET and GROW algorithm of LigBuilder® V1.2 were used to generate in silico molecules for each seed. Surflex-docking in SYBYL-X® V1.2 resulted in five molecules with a total docking score of six or greater. De novo molecules created and optimized, present viable leads for high-throughput screening, leading to identification of novel PDEδ inhibitors for use as anti-cancer agents.

This project utilised abiraterone as a lead molecule for further iterative design of novel anti-prostate cancer drugs which modulate the CYP17A1 receptor. The protein data bank crystallographic deposition describing the bound co-ordinates of abiraterone and the CYP17A1 enzyme was selected. Abiraterone and the CYP17A1 were examined using structure activity relationship studies; Sybyl®-X was used to generate the apo-receptor and abiraterone extract. For in silico ligand based drug design, ViCi® Hamburg screened for molecules similar to abiraterone. A protomol for CYP17A1 was generated, usingSybyl®-X, in order to probe areas of instability, within the active site region. Both abiraterone extract and the apo-receptor were later imported into X-SCORE® to calculate the ligand binding affinity and the ligand binding energy (kcal mol-1). A total of three seeds was generated using Sybyl®-X, from which de novo molecules were generated, using LigBuilder®. Novel structures divided into various families were generated having different pharmacophores and filtered in accordance to Lipinski’s rule of five. The protomol and the keysite volumes were then compared using UCSF® Chimera.1000 molecules were generated using in silico based drug design, of which 756 were Lipinski rule compliant; 99 molecules exhibited a total score of 6 or higher, when docked into the protomol. 727 de novo molecules were generated; 465 were found to be Lipinski rule compliant and hence further used in the study for pharmacophoric evaluation. Some of the de novo molecules exhibit a pKd higher than the baseline value of 7.04, for abiraterone molecule.

Alzheimer's disease affects cognitive function through formation of ß- secretase mediated extracellular cerebral protein plaques and intracellular neurofibrillary tangles, thus its antagonism could mitigate disease progression. This project aims to identify newly obtained and optimized molecules which decrease the formation of ß -amyloid plaques through inhibition of the ß- secretase enzyme. Protein databank (PDB) depositions describing the bound coordinates of 6 lead structures complexed with ß- secretase were identified (PDB ID- 2VKM, 4B05, 4IVS, 3U6A, 3IGB, 2Q11) as leads for in silico ligand based and de novo design of novel antagonist molecules. For the first part of this study, ligands extracted from the protein were used as templates for screening ViCi Hamburg’s database. Protomols were generated for each of the ligands using the Surflex Dock suite in SYBYL-X. The molecules received through ViCi were then used as ligand sources. For the second part of the study the ligand binding affinity (LBA) of each small molecule for its cognate receptor was calculated in X-Score for baseline affinity establishment. 2D topology maps highlighting the important interactions between ligand and receptor were generated using Poseview, and noncritical moieties were computationally removed in the process of creating seed structures (n=3, 2, 3,2,2,2 respectively) on to which novel moieties were computationally introduced using the GROW module of LigBuilder. Protomol and Keysite volumes were then compared using UCSF Chimera. 1636 novel structures were generated with 253 structures being Lipinski Rule compliant. The highest ranking molecules from each pharmacophoric family were identified for optimization and in vitro validation.