What is an energy landscape?
The potential energy landscape is a high-dimensional object  that can be derived from the Schrödinger equation within the Born-Oppenheimer approximation.  The information contained in it  allow us to compute structural, thermodynamic and kinetic properties of a molecular system.

In simpler terms, we might consider the energy landscape as a high-dimensional mountain range. The valleys are where we find stable structures. The lower down the valley is, the more stable the associated structures. To get from one valley to another, molecules follow the lowest energy path, i.e. the lowest pass heights.

Why is this relevant to understanding proteins and nucleic acids?
Proteins and nucleic acids fold into (fairly) well-defined three-dimensional structures based on their sequence.  Achieving the correct fold is important, as these biomolecules require it to fulfill their function. The topography of the energy landscape is important for the reliable and fast folding and re-folding.


What topographies are observed in these energy landscapes?
We observe a range of topographies. For a single available fold, we observe a single funnel energy landscape, i.e. a single big valley.
The more interesting case is when we see multiple possible structures. If these are well-defined, the energy landscapes are showing multiple funnels.


What am I interested in?
As there are multiple, competing structures in the case of multifunnel energy landscapes, small changes allow to alter the observed functions subtly. Such changes can be achieved for example by mutations of the sequence or chemical alterations, such as methylation. I work on understanding these changes better, and aim to use them in designing functional biomolecules.


How are energy landscapes explored?
Any method studying biomolecules probes the energy landscape, though usually implicitly. I use the computational energy landscape framework, pioneered and developed by Prof. David Wales and his group (for more information, see his website). The framework uses geometry-optimisation to locate local minima and transition states. The method is complementary to other simulation techniques, and facilitates active collaboration with experiment.

For more details on the methodology, check out the Reviews & Perspective section on the Publications page. For more details on my current work, see the projects page.