A Different Kind of Restraint Suitable for Molecular Dynamics Simulations

Conformational sampling of complex biomolecules is an emerging frontier in drug discovery. Indeed, advances in lab-based structural biology and related computational approaches like AlphaFold have made great strides in obtaining static protein structures. However, biology is in constant motion and many important biological processes rely on conformationally-driven events. Unrestrained molecular dynamics (MD) simulations require that the simulated time be comparable to the real time of the biological processes of interest, rendering pure MD impractical for many drug design projects, where conformationally-driven biological events can take microseconds to milliseconds or longer. An alternative approach is to accelerate the sampling of specific motions by applying restraints, guided by insights about the underlying biological process of interest. A plethora of restraints exist to limit the size of conformational search space, although each has drawbacks when simulating complex biological motions. In this work, we introduce a new kind of restraint for molecular dynamics simulations (MD) that is particularly well suited for complex conformationallydriven biological events, such as protein-ligand binding, allosteric modulations, conformational signalling, and membrane permeability. The new restraint, which relies on a barrier function (the scaled reciprocal function) is particularly beneficial to MD, where hard-wall restraints are needed with zero tolerance to restraint violation. We have implemented this restraint within a hybrid sampling framework that combines metadynamics and extended-Lagrangian adaptive biasing force (meta-eABF). We use two particular examples to demonstrate the value of this approach: (1) quantification of the approach of E3-loaded ubiquitin to a protein of interest as part of the Cullin ring ligase and (2) membrane permeability of heterobi-functional degrader molecules with a large degree of conformational flexibility. Future work will involve extension to additional systems and benchmarking of this approach compared with other methods.