  There are many problems in biochemistry that are difficult to study
experimentally. Simulation methods are appealing due to direct availability of
atomic coordinates as a function of time. However, direct molecular simulations
are challenged by the size of systems and the time scales needed to describe
relevant motions. In theory, enhanced sampling algorithms can help to overcome
some of the limitations of molecular simulations. Here, we discuss a problem in
biochemistry that offers a significant challenge for enhanced sampling methods
and that could, therefore, serve as a benchmark for comparing approaches that
use machine learning to find suitable collective variables. More in particular,
we study the transitions LacI undergoes upon moving between being
non-specifically and specifically bound to DNA. It is found that many degrees
of freedom change during this transition and that the transition does not occur
reversibly in simulations if only a subset of these degrees of freedom are
biased. We also explain why this problem is so important to biologists and the
transformative impact that a simulation of it would have on the understanding
of DNA regulation.
