Artificial Intelligence Resolves Kinetic Pathways of Magnesium Binding to RNA

Abstract

Magnesium is an indispensable cofactor in countless vital processes. In order to understand its functional role, the characterization of the binding pathways to biomolecules such as RNA is crucial. Despite the importance, a molecular description is still lacking since the transition from the water-mediated outer-sphere to the direct innersphere coordination is on the millisecond time scale and therefore out of reach for conventional simulation techniques. To fill this gap, we use transition path sampling to resolve the binding pathways and to elucidate the role of the solvent in the binding process. The results reveal that the molecular void provoked by the leaving phosphate oxygen of the RNA is immediately filled by an entering water molecule. In addition, water molecules from the first and second hydration shell couple to the concerted exchange. To capture the intimate solute−solvent coupling, we perform a committor analysis as the basis for a machine learning algorithm that derives the optimal deep learning model from thousands of scanned architectures using hyperparameter tuning. The results reveal that the properly optimized deep network architecture recognizes the important solvent structures, extracts the relevant information, and predicts the commitment probability with high accuracy. Our results provide detailed insights into the solute−solvent coupling which is ubiquitous for kosmotropic ions and governs a large variety of biochemical reactions in aqueous solutions.

RNA Captures More Cations than DNA: Insights from Molecular Dynamics Simulations

“…In particular, the parameters were shown to resolve the fine differences between distinct metal cations. ,, Note that Mg 2+ was excluded from the present study. For Mg 2+ , the transition from the water-mediated outer-sphere to the inner-sphere coordination is on the micro- to millisecond time scale. , It is, therefore, tremendously challenging to obtain an equilibrated distribution for Mg 2+ with the available computational resources.…”

“…In particular, the force fields for the metal cations must be optimized to reproduce experimental solution properties , and ion binding affinities − in order to resolve the subtle differences between different cations. The second challenge for simulations is that for cation binding, the transition from a water-mediated outer-sphere to a direct inner-sphere coordination is on the micro- to millisecond time scale for metal cations with high charge density such as Mg 2+ . ,− Therefore, simulating an equilibrated distribution for highly charged ions is out of reach for conventional simulation techniques, and enhanced sampling schemes need to be applied. ,, Here, the quantitative comparison of experiments, simulations, and theoretical modeling is essential to drive the continuous improvement of atomistic models and theoretical methods. ,− In turn, simulations can contribute significantly to a deeper understanding of the interactions between cations and nucleic acids and reveal the selectivity of cation binding sites, , the sequence dependence of ion binding affinities, the influence of the handedness, or ion competition. − …”

AlphaFold, Artificial Intelligence (AI), and Allostery

“…The richness of high quality data that are being compiled in databases (e.g., refs and − ) is already strengthening studies that require protein structures, such as mapping binding sites and interactions in signaling pathways, and identification of hot spots, including latent and rare cancer driver mutations. − The most profound impact will likely be in accelerating and improving production of new medications (e.g., ref ), and in generating data that can be used toward this vital aim (e.g., refs , , , and − ). AI developments and applications may further help foretell whether the signal propagating downstream will be strong enough to reach its genomic target to activate (suppress) gene expression, and predict pathways. − Altogether, these powerful approaches and the databases that they create revamp and transform traditional and ongoing research involving the use of structures. They also embolden us to step back, rethink, and innovate our projects.…”

A consistent picture of phosphate–divalent cation binding from models with implicit and explicit electronic polarization

“…Finally, even if we don't explicitly examine the molecular details of the conversion between inner-sphere (contact) and outer-sphere (SShIP) binding modes, as has been done elsewhere, 93 we can see that even with the force fields that properly capture the binding free energy, exchange kinetics between the different ion pairs remains slow (compared to typical simulation timescales). This is evidenced, for instance, by the large barriers separating the contact and solvent-shared binding modes in Fig.…”