A general machine learning scheme for integrating time-series data from single-molecule experiments and molecular dynamics simulations is proposed and successfully demonstrated for the folding dynamics of the WW domain.
Propagation, speed and shapes of genetic waves of expression during development can be modeled by a simple interplay between two transcriptional modules (dynamic/static), which explains robustness and precision of patterning.
A combination of molecular dynamics simulations and X-ray diffraction data has been used to construct more realistic models of proteins and to provide new insights into their interactions with other proteins and biomolecules.
Structure, dynamics, and mutation of a gamete fusion protein and comparisons to viral homologues suggest that after trimerization the domain bearing the membrane-inserting fusion loops can pivot with respect to the trimer 3-fold axis.
Sub-second pontine waves functionally interact with hippocampal population activity in a state-dependent manner across sleep states, while brainstem ensemble dynamics exhibit slow, long-lasting state-predictive activity.