Venoms To Drugs: Characterizing The Molecular Interactions Between Venom Peptides And Ion Channels With A View To Rational Drug Design
Funder
National Health and Medical Research Council
Funding Amount
$316,449.00
Summary
The conventional approach to drug development is reaching a state of crisis as it is producing fewer new drugs at increasing cost. A promising alternative is to harness the rich and diverse chemistry of venom peptides. This project aims to understand the mechanism by which venom peptides achieve their pharmacological activity. This knowledge is essential for venom-based drug design for treating diseases ranging from nervous systems disorders, stroke, chronic pain and psychiatric illnesses.
Understanding Multidrug Resistance In Cancer: Identification Of The Substrate And Inhibitor Binding Sites In P-glycoprotein
Funder
National Health and Medical Research Council
Funding Amount
$284,343.00
Summary
Cancers expressing the multidrug transporter P-glycoprotein (P-gp) are resistant to chemotherapy. The clinical impact of P-gp is so large that the National Cancer Institute (USA) “profiles” all anticancer drugs for transport by P-gp, primarily because the mechanism of drug binding and transport by P-gp is unknown. The aim of this proposal is to understand the molecular details of how drugs bind to and interact with P-gp, a major step in our understanding of P-gp mediated chemotherapy resistance.
Developing Drugs To Prevent Prostate Cancer Spread.
Funder
National Health and Medical Research Council
Funding Amount
$99,248.00
Summary
Current therapies for prostate cancer lose their efficacy as the cancer advances. Moreover, despite the spread of cancer being the major cause of prostate cancer mortality, there is no therapy available which selectively targets this process, thus new agents are needed. By using computer modelling to predict molecules that bind to the cell surface protein CD151 and testing these in biological assays, we aim to discover molecules that reduce cell migration of prostate cancer and that can be devel ....Current therapies for prostate cancer lose their efficacy as the cancer advances. Moreover, despite the spread of cancer being the major cause of prostate cancer mortality, there is no therapy available which selectively targets this process, thus new agents are needed. By using computer modelling to predict molecules that bind to the cell surface protein CD151 and testing these in biological assays, we aim to discover molecules that reduce cell migration of prostate cancer and that can be developed into anti-migration drugs.Read moreRead less
Structural Biology Of Bacterial Lipid II-glycopeptide Antibiotic Interactions
Funder
National Health and Medical Research Council
Funding Amount
$605,190.00
Summary
Drug resistant bacteria are threatening our ability to successfully treat serious life-endangering infections, with many common antibiotics no longer effective. We will study in atomic detail how one class of antibiotics interacts with bacteria in order to design new members of this group that can overcome resistance.
Theoretical modelling and design of safe covalent anti-cancer drugs. Covalent drugs are a new class of drugs with outstanding potential in cancer therapy. Detailed computer modelling studies will be performed to determine how these drugs interact with an important target in cancer therapy, the epithelial growth factor receptor, and thereby aid the development of new cancer treatments.
Enhanced force fields for computational drug design and materials research. This project aims to improve the atomic interaction functions used to calculate the structural, dynamic and thermodynamic properties of molecules that alter net charge or structure in different environments. Predicting the stability of alternative protonation and tautomeric states for molecules bound to therapeutic targets is a major challenge in computational drug design. It is key to identifying the therapeutically act ....Enhanced force fields for computational drug design and materials research. This project aims to improve the atomic interaction functions used to calculate the structural, dynamic and thermodynamic properties of molecules that alter net charge or structure in different environments. Predicting the stability of alternative protonation and tautomeric states for molecules bound to therapeutic targets is a major challenge in computational drug design. It is key to identifying the therapeutically active chemical species as well as understanding drug transport and off-target effects. The work will expand the utility of modelling software used by over 13,000 researchers worldwide. In addition, the improved interaction functions will also help in the understanding of a wide range of other materials at an atomic level.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE140100550
Funder
Australian Research Council
Funding Amount
$358,248.00
Summary
Quantum refinement of DNA X-ray structures. DNA carries the genetic map of life and refinement of its x-ray structures is a key tool to understand its functions. Standard refinement, however, relies strongly on empirical geometry constraints, and it is known that these can induce unphysical features. Quantum mechanical (QM) methods have now evolved to a level that offers an intriguing way out of this dilemma. In this project, state-of-the-art QM methods will be applied to DNA x-ray structures, a ....Quantum refinement of DNA X-ray structures. DNA carries the genetic map of life and refinement of its x-ray structures is a key tool to understand its functions. Standard refinement, however, relies strongly on empirical geometry constraints, and it is known that these can induce unphysical features. Quantum mechanical (QM) methods have now evolved to a level that offers an intriguing way out of this dilemma. In this project, state-of-the-art QM methods will be applied to DNA x-ray structures, and a unique quantum refinement scheme will be developed. Such a scheme will provide crystallographers with a new tool to determine DNA structures with greater accuracy and it will offer benefits to many areas of the life sciences that depend on such accurate structures.Read moreRead less
Computational enzymology: exploring the free energy landscape of enzymatic catalysis. Most biochemical reactions depend on enzyme catalysis and understanding how enzymes work at the molecular level remains a central question. This project will develop a suite of computational models to study the mechanisms of enzyme-catalysed reactions and such knowledge holds promise for technological benefits in the form of new drugs and novel catalysts.
Force Fields for Structure Refinement and Computational Drug Design. The ability to model molecular systems at an atomic level, as used in protein structure refinement or computational drug design, is critically dependent on the accuracy with which inter-atomic interactions are represented. Highly optimised and well-validated interaction parameters are available for common biomolecules, such as amino acids, sugars and lipids, but not for co-factors, substrates and potential drug molecules, or ot ....Force Fields for Structure Refinement and Computational Drug Design. The ability to model molecular systems at an atomic level, as used in protein structure refinement or computational drug design, is critically dependent on the accuracy with which inter-atomic interactions are represented. Highly optimised and well-validated interaction parameters are available for common biomolecules, such as amino acids, sugars and lipids, but not for co-factors, substrates and potential drug molecules, or other molecules of interest such as polymers and dendrimers. The aim of this project is to develop and validate geometric and interaction parameters (force fields) for complex organic molecules and use these to facilitate bio-molecular structure refinement and computational drug design.Read moreRead less
Improving empirical force fields: a big-data approach. This project aims to improve the ability to represent the thermodynamic properties of molecules of biological, pharmaceutical or materials interest by developing force fields capable of describing a diverse range of molecules both consistently and with high fidelity. The project aims to exploit a rapidly expanding, in-house database of parameterized molecular structures to develop highly optimised, well-validated parameters that are both con ....Improving empirical force fields: a big-data approach. This project aims to improve the ability to represent the thermodynamic properties of molecules of biological, pharmaceutical or materials interest by developing force fields capable of describing a diverse range of molecules both consistently and with high fidelity. The project aims to exploit a rapidly expanding, in-house database of parameterized molecular structures to develop highly optimised, well-validated parameters that are both consistent and transferable, enabling molecules of any size or complexity to be parameterised with a fidelity currently only possible for simple organics. This will provide significant benefits, such as helping to improve the accuracy and reliability of ligand: protein complexes determined experimentally, a limiting factor in computational drug design.Read moreRead less