Incorporating experimental data into long timescale simulations of macromolecules

Recent breakthroughs in experimental technologies and in high-performance computing have enabled unprecedented measurements and simulations of complex biophysical systems such as macromolecules. However, experiments provide only a partial view of macromolecular processes and are limited in their temporal and spatial resolution. On the other hand, atomistic simulations are still not able to sample the conformation space of large complexes, thus leaving significant gaps in our ability to study molecular processes at a biophysically relevant scale. We present our efforts to bridge these gaps, by using experimental data as a starting point in a computational modeling approach. We use models at different resolutions and "anchor" them to experimental measurements, to provide quantitatively accurate representations of systems of interest, and address open biophysical questions.