Can molecular dynamics be used to simulate biomolecular recognition?

TL;DR

This paper examines the challenges of using molecular dynamics simulations to model biomolecular recognition, focusing on the LacI-DNA transition and its implications for understanding DNA regulation.

Contribution

It highlights the limitations of current enhanced sampling methods in simulating complex biomolecular transitions and proposes this problem as a benchmark for machine learning approaches.

Findings

Many degrees of freedom change during LacI-DNA transition

Transitions are not reversible when only a subset of degrees of freedom are biased

The problem is crucial for understanding DNA regulation

Abstract

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.