Project Details
Description
P-glycoprotein (P-gp) is a membrane protein responsible for the transport of molecules through biological membranes. P-gp is widely distributed throughout the body and plays a pivotal role in limiting cellular uptake of xenobiotics such as drugs. It is thus of fundamental importance to understand the exact molecular mechanisms of the biological function of P-gp, and especially how to selectively block the function of this protein in order to prevent unwanted drug efflux. However, due to difficulties in obtaining experimental structures of membrane proteins, and especially such proteins in complex with xenobiotics, our current understanding of the exact mechanism of e.g., drug efflux by Pgp is largely unknown and thus hampers the development of novel drugs related to e.g., cancer therapy. In this research project, we will combine experimental procedures with state-of-the-art simulation and data science approaches to obtain a molecular basis understanding of the biological mechanisms of P-gp.
In a first step, we will quantify the binding modes of known P-gp inhibitors, and through molecular simulations, we will reveal the working mechanisms of such known binders. In a second step, we will use this knowledge as feed for the development of a combined machine-learning and protein structure-based approach in order to develop rational design strategies concerning the design of small molecules that selectively binds to the nucleotide binding domain of P-gp and thereby block the biological function of P-gp. The outcome of this research project will thus in addition to provide fundamental knowledge concerning the functioning of transport proteins – and how to selectively block the function of such proteins - also provide concrete rational design principles for small molecules blocking the function of P-gp thereby solving a major problem related to drug formulation and delivery in e.g. cancer therapy.
In a first step, we will quantify the binding modes of known P-gp inhibitors, and through molecular simulations, we will reveal the working mechanisms of such known binders. In a second step, we will use this knowledge as feed for the development of a combined machine-learning and protein structure-based approach in order to develop rational design strategies concerning the design of small molecules that selectively binds to the nucleotide binding domain of P-gp and thereby block the biological function of P-gp. The outcome of this research project will thus in addition to provide fundamental knowledge concerning the functioning of transport proteins – and how to selectively block the function of such proteins - also provide concrete rational design principles for small molecules blocking the function of P-gp thereby solving a major problem related to drug formulation and delivery in e.g. cancer therapy.
Status | Finished |
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Effective start/end date | 01/09/2021 → 31/08/2024 |