Biomolecular Dynamics

Biological macromolecules like proteins and nucleic acids are not static objects but their structures fluctuate dynamically.

The dynamics of biological molecules is often crucial for biological function.

Dynamics may range from internal thermal fluctuations to conformational transitions between kinetically distinct states and dynamically varying molecular associations

We capture dynamics via computer simulations and generative AI models.

Key topics that we are currently interested in:

  • Structural ensembles for highly dynamic and intrinsically disordered peptides and regions
  • Transient molecular interactions during assembly and aggregation
  • Generation of conformational ensembles consistent with experimental data, especially from NMR or cryoEM.
  • Rapid generation of functional states and kinetic networks from a single input structure

Cellular Crowding

cellular crowding

Phase Separation

phase separation is interesting

Multiscale Modeling

multi-scale modeling

Machine Learning

Rapid advances with machine learning approaches present new opportunities in biomolecular science. 

Artificial intelligence has significant potential to augment if not replace traditional computational methods for the generation and analysis of biomolecular structure and dynamics. 

We are especially interested in the following topics:

  • Rapid generation of dynamic structural ensembles for proteins, nucleic acids, and other biological molecules