Multiscale Simulations to Unveil the Role of Divalent Cations in Ribozyme Catalysis – MUSIRICAT

The goal of this project is to develop a multiscale simulation strategy enabling the study of how the reactivity of charged biomolecules is affected by slow environment fluctuations, including ion reorganization. In particular, we will apply it towards understanding cation and sequence-dependent effects on ribozyme catalysis.
Ribozymes are RNA molecules that exhibit a catalytic activity analogous to that of enzymes. They have attracted much interest because they are involved in key biological processes and are potential therapeutic tools. Understanding the origin of their catalytic activity at the molecular level is therefore a major challenge of biochemistry, and a prerequisite to control, modify, or tune their activity. Yet, crucial aspects of their function remain unclear, notably the role played by divalent cations such as Mg2+—associated in vivo with their activity. Many ribozymes thus display a strongly ion-dependent activity, which also subtly depends on distant tertiary interactions. Understanding the origin of this ion specificity is both an experimental and a computational challenge, because ribozymes are highly charged, their interaction with divalent cations is difficult to probe experimentally and to capture in simulations, and they are much more flexible than typical enzymes. From a computational modeling point of view, the challenge originates from the need to account simultaneously for the ribozyme flexibility, its reactivity, and its interactions with divalent cations, poorly described by standard methods.
To achieve this goal,we propose to combine advanced explicitly and implicitly polarizable ion force fields with extensive conformational sampling of the active site and ion binding modes, to identify typical active site conformations. We will then use state-of-the-art path sampling QM(DFTB3)/MM-MD techniques to dynamically explore the reaction pathway in different environments and determine the possibly ion-dependent mechanism. In addition, machine-learned corrections to DFTB3 will be used in a new ML/MM embedding scheme to achieve high accuracy at a modest computing cost. Finally, once the mechanism(s) and key states identified, a semi-classical (EVB) description will be parameterized. This computationally efficient model coupled with enhanced sampling techniques will finally allow the exploration of the ns-?s time scales, that are out of reach for traditional QM/MM-MD approaches. Such time scales are typical for ion movements and conformational fluctuations, and are key to accurately capture the impact of divalent cations on the ribozyme conformation.
This project will use as a real-world model system the RzB Hammerhead ribozyme, whose well-characterized activity highly depends not only on the nature of the cations in the active site but also on the ribozyme sequence. With a large body of experimental data already available for comparaison, it is the ideal system to test and develop our simulation strategy, as it presents all of the computational challenges outlined above. Our approach will make it possible to identify how the reaction mechanism and active site conformational fluctuations are affected by a change of cation in the active site, and to quantitatively link them to changes in the ribozyme activity. We will thus unveil the molecular origin of divalent cations’ specificity in ribozyme catalysis, and its sequence dependence.
Beyond this specific system and biochemical question, the methodologies and tools developed in this project should prove useful to a broad biosimulation community in studies of various biochemical systems involving the interaction of divalent cations with biomolecules—e.g. phospholipids in membranes—or where reactivity is modulated by environmental fluctuations, for instance in the field of enzyme design.