Mobility experience with a research focus
PhD sandwich
Pharmaceutical and Pharmacological Sciences
Topic: New computational methodologies in inspecting ligand-receptor recognition pathways as a valuable strategy to speed up the identification of drug candidates.
State of the art: One of the most challenging issues for the future of drug discovery is the capability to understand the receptor-ligand recognition pathway with the aim to facilitate the development of drug candidates with more favorable pharmacodynamic profiles. Unfortunately, the recognition process between a ligand and its receptor is an infrequent event to describe at the molecular level, and even with the recent GPU-based computing resources, it is necessary to carry out classical molecular dynamics (MD) experiments in a long microsecond time scale. To overcome this limiting factor, we have implemented an alternative MD approach, named supervised molecular dynamics (SuMD), that enables us to follow the receptor-ligand approaching process within a time scale reduced by up to 3 orders of magnitude compared to classical MD. SuMD enables the investigation of ligand-receptor binding events independently from the starting position, chemical structure of the ligand, and also from its receptor binding affinity.
Moreover, the prediction of ligand efficacy has long been linked to thermodynamic properties such as the equilibrium dissociation constant, which considers both the association and the dissociation rates of a defined protein-ligand complex. In the last 15 years, there has been a paradigm shift, with an increased interest in the determination of kinetic properties such as the drug-target residence time since they better correlate with ligand efficacy compared to other parameters. Recently, we presented thermal titration molecular dynamics (TTMD), an alternative computational method that combines a series of molecular dynamics simulations performed at progressively increasing temperatures with a scoring function based on protein-ligand interaction fingerprints for the qualitative estimation of protein-ligand-binding stability. TTMD was successfully able to distinguish between high-affinity compounds (low nanomolar range) and low-affinity ones (micromolar), proving to be a useful screening tool for the prioritization of compounds in a drug discovery campaign.
Objectives: Theme A – Application of SuMD and TTMD technologies in different hot therapeutic areas such as oncology, inflammation, and immunology. Theme B – Extending and improving the development of SuMD methods, in particular implanting a novel approach to predict the free energy of binding and the corresponding binding kinetic parameters.
Background in Chemistry or Medicinal Chemistry with good knowledge of informatics (Linux environments). Skills in programming (i.e. Phyton) are a plus.
PhD sandwich: 6-12