RNA folding pathways from all-atom simulations with a variationally improved history-dependent bias

Optimizing On-the-Fly Probability Enhanced Sampling for Complex RNA Systems: Sampling Free Energy Surfaces of an H-Type Pseudoknot

All-atom molecular dynamics (MD) simulations offer crucial insights into biomolecular dynamics, but inherent time scale constraints often limit their effectiveness. Advanced sampling techniques help overcome these limitations, enabling predictions of deeply rugged folding free energy surfaces (FES) of RNA at atomistic resolution. The Multithermal-Multiumbrella On-the-Fly Probability Enhanced Sampling (MM-OPES) method, which combines temperature and collective variables (CVs) to accelerate sampling, has shown promise and cost-effectiveness. However, the applications have so far been limited to simpler RNA systems, such as stem-loops. In this study, we optimized the MM-OPES method to explore the FES of an H-type RNA pseudoknot, a more complex fundamental RNA folding unit. Through systematic exploration of CV combinations and temperature ranges, we identified an optimal strategy for both sampling and analysis. Our findings demonstrate that treating the native-like contacts in two stems as independent CVs and using a temperature range of 300-480 K provides the most effective sampling, while projections onto native Watson-Crick-type hydrogen bond CVs yield the best resolution FES prediction. Additionally, our sampling scheme also revealed various folding/unfolding pathways. This study provides practical insights and detailed decision-making strategies for adopting the MM-OPES method, facilitating its application to complex RNA structures at atomistic resolution.

A discard-and-restart MD algorithm for the sampling of protein intermediate states

We introduce a discard-and-restart molecular dynamics (MD) algorithm tailored for the sampling of realistic protein intermediate states. It aids computational structure-based drug discovery by reducing the simulation times to compute a "quick sketch" of folding pathways by up to 2000×. The algorithm iteratively performs short MD simulations and measures their proximity to a target state via a collective variable loss, which can be defined in a flexible fashion, locally or globally. Using the loss, if the trajectory proceeds toward the target, the MD simulation continues. Otherwise, it is discarded, and a new MD simulation is restarted, with new initial velocities randomly drawn from a Maxwell-Boltzmann distribution. The discard-and-restart algorithm demonstrates efficacy and atomistic accuracy in capturing the folding pathways in several contexts: 1) fast-folding small protein domains, 2) the folding intermediate of the prion protein PrP, and 3) the spontaneous partial unfolding of α-tubulin, a crucial event for microtubule severing. During each iteration of the algorithm, we can perform AI-based analysis of the transitory conformations to find potential binding pockets, which could represent druggable sites. Overall, our algorithm enables systematic and computationally efficient exploration of conformational landscapes, enhancing the design of ligands targeting dynamic protein states.

Influence of ion and hydration atmospheres on RNA structure and dynamics: insights from advanced theoretical and computational methods

RNA, a highly charged biopolymer composed of negatively charged phosphate groups, defies electrostatic repulsion to adopt well-defined, compact structures. Hence, the presence of positively charged metal ions is crucial not only for RNA's charge neutralization, but they also coherently decorate the ion atmosphere of RNA to stabilize its compact fold. This feature article elucidates various modes of close RNA-ion interactions, with a special emphasis on Mg2+ as an outer-sphere and inner-sphere ion. Through examples, we highlight how inner-sphere chelated Mg2+ stabilizes RNA pseudoknots, while outer-sphere ions can also exert long-range electrostatic interactions, inducing groove narrowing, coaxial helical stacking, and RNA ring formation. In addition to investigating the RNA's ion environment, we note that the RNA's hydration environment is relatively underexplored. Our study delves into its profound interplay with the structural dynamics of RNA, employing state-of-the-art atomistic simulation techniques. Through examples, we illustrate how specific ions and water molecules are associated with RNA functions, leveraging atomistic simulations to identify preferential ion binding and hydration sites. However, understanding their impact(s) on the RNA structure remains challenging due to the involvement of large length and long time scales associated with RNA's dynamic nature. Nevertheless, our contributions and recent advances in coarse-grained simulation techniques offer insights into large-scale structural changes dynamically linked to the RNA ion atmosphere. In this connection, we also review how different cutting-edge computational simulation methods provide a microscopic lens into the influence of ions and hydration on RNA structure and dynamics, elucidating distinct ion atmospheric components and specific hydration layers and their individual and collective impacts.