This thesis explores the use of cell theory calculations to characterise hydration thermodynamics in small molecules (cations, ions, hydrophobic molecules), proteins and protein-ligand complexes. Cell theory uses the average energies, forces and torques of a water molecule measured in its molecular frame of reference to parameterise a harmonic potential. From this harmonic potential analytical expressions for entropies and enthalpies are derived. In order to spatially resolve these thermodynamic quantities grid points are used to store the forces, torques, and energies of nearby waters which giving rise to the new grid cell theory (GCT) model. GCT allows one to monitor hydration thermodynamics at heterogeneous environments such as that of a protein surface. Through an understanding of the hydration thermodynamics around the protein and particularly around binding sites, robust protein-ligand scoring functions are created to estimate and rank protein-ligand binding affinities. GCT was then able to retrospectively rationalise the structure activity relationships made during lead optimisation of various ligand-protein systems including Hsp90, FXa, scytalone dehydratase among others. As well as this it was also used to analyse water behaviour in various protein environments with a dataset of 17 proteins. The grid cell theory implementation provides a theoretical framework which can aid the iterative design of ligands during the drug discovery and lead optimisation processes, and can provide insight into the effect of protein environment to hydration thermodynamics in general.

Virtual screening is a fast, low cost method to identify potential small molecule therapeutics from large chemical databases for the vast amount of target proteins emerging from the life sciences and bioinformatics. In this work, we applied several conventional and newly developed virtual screening approaches to identify novel non-steroidal ligands for the human and zebrafish sex hormone binding globulin (SHBG). The ‘benchmark set of steroids’ is a set of steroids with known affinities for human SHBG that has been widely used for validation in the development of different virtual screening methods. We have updated this data set by including additional steroidal SHBG ligands and by modifying the predicted binding orientations of several benchmark steroids in the SHBG binding site based on the use of an improved docking protocol and information from recent crystallographic data. The new steroid binding orientations and the expanded version of the benchmark set was then used to create new in silico models which were applied in virtual screening to identify high-affinity non-steroidal human SHBG ligands from a large chemical database. Anthropogenic compounds with the capacity to interact with the steroid-binding site of SHBG pose health risks to humans and other vertebrates including fish. We constructed a homology model of SHBG from zebrafish and applied virtual screening to identify ligands for zebrafish SHBG from a set of 80 000 existing commercial substances, many of which can be exposed to the aquatic environment. Six hits from this in silico screen were tested experimentally for zebrafish SHBG binding and three of them, hexestrol, 4-tert-octylcatechol, dihydrobenzo(a)pyren-7(8H)-one demonstrated micromolar binding affinity for the zebrafish SHBG. These findings demonstrate the feasibility of using virtual screening to identify anthropogenic compounds that may disrupt or highjack functionally important protein:ligand interactions. Studies applying this new computational toxicology method could increase the awareness of hazards posed by existing commercial chemicals at relatively low cost.

Virtual screening is a fast, low cost method to identify potential small molecule therapeutics from large chemical databases for the vast amount of target proteins emerging from the life sciences and bioinformatics. In this work, we applied several conventional and newly developed virtual screening approaches to identify novel non-steroidal ligands for the human and zebrafish sex hormone binding globulin (SHBG). The ‘benchmark set of steroids’ is a set of steroids with known affinities for human SHBG that has been widely used for validation in the development of different virtual screening methods. We have updated this data set by including additional steroidal SHBG ligands and by modifying the predicted binding orientations of several benchmark steroids in the SHBG binding site based on the use of an improved docking protocol and information from recent crystallographic data. The new steroid binding orientations and the expanded version of the benchmark set was then used to create new in silico models which were applied in virtual screening to identify high-affinity non-steroidal human SHBG ligands from a large chemical database. Anthropogenic compounds with the capacity to interact with the steroid-binding site of SHBG pose health risks to humans and other vertebrates including fish. We constructed a homology model of SHBG from zebrafish and applied virtual screening to identify ligands for zebrafish SHBG from a set of 80 000 existing commercial substances, many of which can be exposed to the aquatic environment. Six hits from this in silico screen were tested experimentally for zebrafish SHBG binding and three of them, hexestrol, 4-tert-octylcatechol, dihydrobenzo(a)pyren-7(8H)-one demonstrated micromolar binding affinity for the zebrafish SHBG. These findings demonstrate the feasibility of using virtual screening to identify anthropogenic compounds that may disrupt or highjack functionally important protein:ligand interactions. Studies applying this new computational toxicology method could increase the awareness of hazards posed by existing commercial chemicals at relatively low cost.

Virtual screening is a fast, low cost method to identify potential small molecule therapeutics from large chemical databases for the vast amount of target proteins emerging from the life sciences and bioinformatics. In this work, we applied several conventional and newly developed virtual screening approaches to identify novel non-steroidal ligands for the human and zebrafish sex hormone binding globulin (SHBG). The ‘benchmark set of steroids’ is a set of steroids with known affinities for human SHBG that has been widely used for validation in the development of different virtual screening methods. We have updated this data set by including additional steroidal SHBG ligands and by modifying the predicted binding orientations of several benchmark steroids in the SHBG binding site based on the use of an improved docking protocol and information from recent crystallographic data. The new steroid binding orientations and the expanded version of the benchmark set was then used to create new in silico models which were applied in virtual screening to identify high-affinity non-steroidal human SHBG ligands from a large chemical database. Anthropogenic compounds with the capacity to interact with the steroid-binding site of SHBG pose health risks to humans and other vertebrates including fish. We constructed a homology model of SHBG from zebrafish and applied virtual screening to identify ligands for zebrafish SHBG from a set of 80 000 existing commercial substances, many of which can be exposed to the aquatic environment. Six hits from this in silico screen were tested experimentally for zebrafish SHBG binding and three of them, hexestrol, 4-tert-octylcatechol, dihydrobenzo(a)pyren-7(8H)-one demonstrated micromolar binding affinity for the zebrafish SHBG. These findings demonstrate the feasibility of using virtual screening to identify anthropogenic compounds that may disrupt or highjack functionally important protein:ligand interactions. Studies applying this new computational toxicology method could increase the awareness of hazards posed by existing commercial chemicals at relatively low cost. / Science, Faculty of / Graduate

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In 2020, cancer impacted an estimated 1.8 million people and result in over 600,000 deaths in the United States. Some cancer treatments options are limited due to drug resistance, requiring additional drug development to improve patient survival rates. It is necessary to continuously develop new therapeutic approaches and identify novel targets, as cancer is ever-growing and adapting. Experimental research strategies have limitations when exploring how to target certain protein classes, including membrane-embedded or protein-protein bound, due to the complexity of their environments. These two domains of research are experimentally challenging to explore, and in silico research practices provide insight that would otherwise take years to study. Computer-aided drug design (CADD) routes can support the areas of drug discovery that are considered difficult to explore with experimental techniques. In this work, we provide research practices that are easily adaptable and translatable to other difficult protein targets and interfaces. First, we identified the morphological impact of a single-site mutation in the G-protein coupled receptor (GPCR), OR2T7, which had been identified as a novel prognostic marker for glioblastoma. Next, we explored the blockbuster target, Programmed Cell Death Protein 1 – (PD-1) and the agonistic vs antagonistic response that can be exploited for Non-Small Cell Lung Cancer (NSCLC) therapeutic development. Last, we explored the sphingolipid transport protein, Spns2, which has been demonstrated to be important in regulating the metastatic cancer enabling microenvironment. This work utilized molecular dynamics simulations (MDS) to explore the protein structure-function relationship for each protein of interest, allowing for the exploration of biophysical properties and protein dynamics. We identified that the D125V mutation in OR2T7 likely influences activation of the MAPK pathway by impacting G-protein binding via reducing the helical plasticity in the TM6 and TM7 regions. PD-1 was identified to have a domain near the PD-L1 binding interface that increases β-sheet stability and increases residue-residue distances with the membrane-proximal region within PD-1, thus leading to an active conformation. Lastly, Spns2 was identified to follow a rocker-switch transport model and provided preliminary insight into sphingolipid-Spns2 channel binding, interacting with residues Thr216, Arg227, and Met230, as well as highlighting the role of Arg119 in a salt-bridge network of interactions essential in substrate translocation. Collectively, this work illustrates the advantages of computational workflows in the drug discovery process and provides a framework that can be applied for additional GCPRs, transport proteins, or protein-protein interfaces to enhance and accelerate the CADD research. / Doctor of Philosophy / Cancer is an ever-evolving disease that requires continuous development of new treatment options. Experimental research strategies can be timely, expensive, or lack atomistic insight into drug development processes. Computer-aided drug design (CADD) routes provide research strategies to support areas of drug discovery that can be difficult to explore with experimental techniques. Membrane-bound proteins and protein-protein interfaces are two domains of research that are typically difficult to explore, and computational research practices provide insight that would otherwise take years to study. In this work, we provide research practices that are easily adaptable and translatable to other difficult protein targets and interfaces. First, we identified the impact of a single-site mutation in the G-protein coupled receptor (GPCR), OR2T7, which had been identified as a novel prognostic marker for glioblastoma. Next, we explored the blockbuster target, Programmed Cell Death Protein 1 – (PD-1) and active vs inactive states that can be exploited for Non-Small Cell Lung Cancer (NSCLC) therapeutic development. Last, we explored the sphingolipid transport protein, Spns2, which has been demonstrated to be important in metastatic cancer growth. This work utilized molecular dynamics simulations (MDS) to explore the protein structure-function relationship for each protein of interest, allowing for the exploration of biophysical properties and protein movement. We identified that the D125V mutation in OR2T7 likely influences activation of the MAPK pathway, which supports multiple cancer-regulation pathways, by impacting G-protein binding via reducing the structural flexibility. PD-1 was identified to have a domain near the PD-L1 binding interface that increases structural stability, thus leading to an upregulation of cancer survival pathways. Lastly, Spns2 analysis provided insight into movement involved in sphingolipid transport, provided preliminary insight into sphingolipid-Spns2 binding, as well as highlighting the role of Arg119 in a network of interactions essential in substrate translocation. Collectively, this work highlights the usefulness of computational workflows in the drug discovery process and provides a framework that can be utilized for additional GPCRs, transport proteins, or protein-protein interfaces to enhance and accelerate the CADD research.

The overall aim of the research is to develop a computational platform based on HINT paradigm for manipulating, predicting and analyzing biomacromolecular-ligand structure. A second synergistic goal is to apply the above methodology to design novel and potent anti-cancer agents. The crucial role of the microtubule in cell division has identified tubulin as an interesting target for the development of therapeutics for cancer. Pyrrole-containing molecules derived from nature have proven to be particularly useful as lead compounds for drug development. We have designed and developed a series of substituted pyrroles that inhibit growth and promote death of breast tumor cells at nM and μM concentrations in human breast tumor cell lines. In another project, stilbene analogs were designed and developed as microtubule depolymerizing agents that showed anti-leukemic activity. A molecular modeling study was carried out to accurately represent the complex structure and the binding mode of a new class of tubulin inhibitors that bind at the αβ-tubulin colchicine site. These studies coupled with HINT interaction analyses were able to describe the complex structure and the binding modes of inhibitors. Qualitative analyses of the results showed general agreement with the experimental in vitro biological activity for these derivatives. Consequently, we have been designing new analogs that can be synthesized and tested; we believe that these molecules will be highly selective against cancer cells with minimal toxicity to the host tissue. Another goal of our research is to develop computational tools for drug design. The development and implementation of a novel cavity detection algorithm is also reported and discussed. The algorithm named VICE (Vectorial Identification of Cavity Extents) utilizes HINT toolkit functions to identify and delineate a binding pocket in a protein. The program is based on geometric criteria and applies simple integer grid maps to delineate binding sites. The algorithm was extensively tested on a diverse set of proteins and detects binding pockets of different shapes and sizes. The study also implemented the computational titration algorithm to understand the complexity of ligand binding and protonation state in the active site of HIV-1 protease. The Computational titration algorithm is a powerful tool for understanding ligand binding in a complex biochemical environment and allows generating hypothesis on the best model for binding.

Computer-aided drug design is an exceptionally useful tool for screening a large number of potential drug molecules to evaluate their therapeutic potential. This technique is both effective and economical. Approximately 120 imidazole-containing molecules were computationally designed and evaluated using gas-phase and solution-phase simulations to assess their propensity for acting as a chelating agent with twenty-six biologically relevant cations. Of particular interest was their ability to chelate Zn2+ and Ca2+, which play a key role in the degradation of neurons following an ischemic stroke. The ultimate goal was to design a small molecule that could be administered before a medical procedure that featured stroke as a possible side effect. In the event that a stroke occurred, the destruction of neurons caused by release of excess Ca2+ and Zn2+ would be diminished and the patient would maintain motor and cognitive function. Promising in silico results were obtained.

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