Our group uses a range of computational tools to investigate structure-function relationships in complex biological systems. The overarching aim of this research is to understand the chemistry that drives biological function. To this end, we use and develop computational tools
(e.g. quantum mechanics (QM), molecular mechanics (MM), hybrid QM/MM, small-molecule docking, molecular dynamics (MD), machine-learning) to simulate the underlying physics and chemistry of the system of interest.
Understanding mechanisms of catalysis in metalloenzymes. These enzymes rely on transition metal centers to perform sophisticated biocatalysis, such as the capture and reduction of small-molecule substrates. Understanding the chemistry driving these processes is the first step to re-engineering new catalysts. Working together with biologists who provide us with structural data from X-ray crystallography and cryo-EM, we model these complex enzymes using QM/MM methodologies. This hybrid computational approach allows us to characterize the electronic state that is captured in specific protein conformations, within the protein scaffold. We compute the vibrational frequencies of ligands that are bound to the catalytic center and compare these with experimentally measured vibrational frequencies. Learning from Nature’s elegance in catalysis has significant potential in the field of clean energy.
Understanding lysosomal proteins involved in sterol homeostasis. Failure of these proteins to perform their function can have severe physiological consequences, in some cases leading even to organism death. In humans, several lysosomal storage disorders have been identified, and many of these are fatal, killing the patients in early age, frequently before adulthood. Our research has focused on the Niemann Pick Type C (NPC) disease, characterized by a disrupted transport of cholesterol and lipids out of cellular compartments. Due to accumulation of cholesterol in the liver, brain and lung, the NPC disease leads to progressive cognitive deterioration and in 50% of the diagnosed cases, causes death in childhood. Molecular modeling and state-of-the-art simulation techniques enable us to obtain atomic details of the complex machinery of sterol-protein interactions. This information is critical for developing therapeutic strategies.
The research highlighted below represents key contributions to our field, focusing on advancements in molecular biology and biochemistry. Each publication provides in-depth analysis and novel findings, contributing to a broader understanding of fundamental biological processes.
Find out more about our project team members below.
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Nadia completed her BSc at Stanford University in 1999 with a degree in Chemistry and then moved to Berlin to complete her Masters (2001) and PhD (2005) in Theoretical Chemistry at the Free University Berlin
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PhD candidate at University College Dublin studying lysosomal Niemann–Pick C proteins using an integrated strategy of molecular dynamics simulations, experimental structural biology, and machine learning. My research bridges computational and laboratory methods to reveal protein mechanisms at atomic resolution.
My name is Ramanathan Rajesh, and I am a second-year PhD student from India. My research focuses on using multiscale modeling approaches to investigate the mechanisms by which the nitrogenase enzyme reduces small molecules. In particular, I am interested in understanding how carbon-based substrates are processed by nitrogenase.
Undergraduate Research in Artificial Intelligence and Computational Chemistry.
Undergraduate Research in QM Computational Chemistry
Undergraduate Research in Computational Chemistry