IMSc Webinar

Learning about folding and aggregation from energy landscapes

Debayan Chakraborty

Department of Chemistry, The University of Texas at Austin

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Google Meet Link: meet.google.com/wsi-phmw-ngr
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During the last few decades, the energy landscape framework has emerged both as a conceptual
and as a computational tool to understand the intimate connections between biomolecular
structure, dynamics, and ultimately function. Nonetheless, determining the global topography of the energy landscape using standard computational techniques, such as molecular dynamics
simulations, has proved challenging, because of the computational cost. In this context, a coarsegraining of the landscape in terms of stationary points, which can be located using geometry optimization techniques, can prove effective. In the first part of my talk, I will highlight how this approach can be exploited to obtain key insights into RNA folding, as well as the conformational switching between the competing morphologies of DNA duplexes, at the atomistic level.

The second half of my talk will illustrate how coarse-graining the degrees of freedom, in order to obtain a simplistic representation of the energy landscape, can also be a viable route towards understanding key biophysical phenomena. In this context, a specific problem related to the aggregation propensities of Ab40 and Ab42 peptides, will be discussed. The two peptides are intrinsically disordered in their monomeric forms, and have practically identical ensemble averaged properties, characteristic of random-coils (RCs). However, conformational clustering reveals that both peptides exhibit a sparse population of aggregation-prone (N*) structures, reminiscent of the Ab fibril state. In other words, the signatures of the fibril state are encoded as excitations on the monomer free energy landscapes. I will discuss how the nearly one order of magnitude difference between the aggregation rates of Ab40 and Ab42 can be quantitatively explained in terms of the relative populations of the N* states.