Sample-based Models of Protein Structural Transitions

Modeling structural transitions of a protein at equilibrium is central to understanding function modulation but challenging due to the disparate spatio-temporal scales involved. Of particular interest are sampling-based methods that embed sampled structures in discrete, graph-based models of dynamics to answer path queries. These methods have to balance between further exploiting low-energy regions and exploring unpopulated, possibly high-energy regions needed for a transition. We recently presented a strategy that leverages experimentally-known structures to improve sampling. Here we demonstrate how such structures can further be leveraged to improve both exploitation and exploration and obtain paths of very high granularity. We show that such improvement is key to accurate sample-based modeling of structural transitions. We further demonstrate that ranking methods by the best transition cost obtained can be deceptive, as denser sampling, which follows a rugged landscape more faithfully, may result in higher costs. The work presented here improves understanding of the current capabilities and limitations of sampling-based methods. Proposing strategies to address some of these limitations in this paper is a first step towards sampling-based methods becoming reliable tools for modeling protein structural transitions.