Prebiotic chemical reactivity in solution with quantum accuracy and microsecond sampling using neural network potentials.

While RNA appears as a good candidate for the first autocatalytic systems preceding the emergence of modern life, the synthesis of RNA oligonucleotides without enzymes remains challenging. Because the uncatalyzed reaction is extremely slow, experimental studies bring limited and indirect information on the reaction mechanism, the nature of which remains debated. Here, we develop neural network potentials (NNPs) to study the phosphoester bond formation in water. While NNPs are becoming routinely applied to nonreactive systems or simple reactions, we demonstrate how they can systematically be trained to explore the reaction phase space for complex reactions involving several proton transfers and exchanges of heavy atoms. We then propagate at moderate computational cost hundreds of nanoseconds of a variety of enhanced sampling simulations with quantum accuracy in explicit solvent conditions. The thermodynamically preferred reaction pathway is a concerted, dissociative mechanism, with the transient formation of a metaphosphate transition state and direct participation of water solvent molecules that facilitate the exchange of protons through the nonbridging phosphate oxygens. Associative-dissociative pathways, characterized by a much tighter pentacoordinated phosphate, are higher in free energy. Our simulations also suggest that diprotonated phosphate, whose reactivity is never directly assessed in the experiments, is significantly less reactive than the monoprotonated species, suggesting that it is probably never the reactive species in normal pH conditions. These observations rationalize unexplained experimental results and the temperature dependence of the reaction rate, and they pave the way for the design of more efficient abiotic catalysts and activating groups.

Molecular Mechanisms of Phosphoester Bond Formation in Water Using Tight-Binding Ab Initio Molecular Dynamics.

Phosphate groups are ubiquitous in biomolecules and are usually incorporated through phosphoester bonds between alcohol groups and orthophosphate. The formation of this bond is exceptionally difficult, with associated barriers of 30-45 kcal/mol in the absence of catalysts. In abiotic conditions, polymerizing nucleic acids without enzymes remains very challenging and is still a partly unsolved problem that severely questions the RNA World hypothesis for the origins of life. Offering a solution to this problem would involve a detailed knowledge of the reaction energetics and mechanisms, yet these remain not fully understood at a molecular level, especially because of the very slow reaction rates that represent a significant challenge for the experiments. The number of involved reaction coordinates and the possible role of the solvent in assisting the reaction are challenging for computational studies. Here, we use extensive ab initio molecular dynamics simulations using semiempirical tight-binding methods and enhanced sampling to address these issues. We first show that the choice of the tight-binding method is greatly limited by the instability of the water liquid phase for most DFTB generations and parameter sets that are widely available. We then focus on a model reaction involving methanol and orthophosphate, for which the two protonation states (mono- and dianionic) that are dominant around neutral pH are considered. We compare different proton coordinates that enable (or not) the participation of solvent water molecules. Our simulations suggest that in all cases, a dissociative associative mechanism, with an intermediate metaphosphate, is favored. The main difference between the two phosphate species is that reaction with the monoanion is assisted by the substrate, while that with the dianion involves solvent water molecules. Our results are in agreement with early experimental measurements, but the reaction barriers are underestimated in our framework. We believe that our approach provides an interesting perspective on how to sample the reaction phase space efficiently, but it calls for future studies using more accurate descriptions of chemical reactivity.

Simulation of FUS Protein Condensates with an Adapted Coarse-Grained Model.

Disordered proteins and nucleic acids can condense into droplets that resemble the membraneless organelles observed in living cells. MD simulations offer a unique tool to characterize the molecular interactions governing the formation of these biomolecular condensates, their physicochemical properties, and the factors controlling their composition and size. However, biopolymer condensation depends sensitively on the balance between different energetic and entropic contributions. Here, we develop a general strategy to fine-tune the potential energy function for molecular dynamics simulations of biopolymer phase separation. We rebalance protein-protein interactions against solvation and entropic contributions to match the excess free energy of transferring proteins between dilute solution and condensate. We illustrate this formalism by simulating liquid droplet formation of the FUS low-complexity domain (LCD) with a rebalanced MARTINI model. By scaling the strength of the nonbonded interactions in the coarse-grained MARTINI potential energy function, we map out a phase diagram in the plane of protein concentration and interaction strength. Above a critical scaling factor of αc ≈ 0.6, FUS-LCD condensation is observed, where α = 1 and 0 correspond to full and repulsive interactions in the MARTINI model. For a scaling factor α = 0.65, we recover experimental densities of the dilute and dense phases, and thus the excess protein transfer free energy into the droplet and the saturation concentration where FUS-LCD condenses. In the region of phase separation, we simulate FUS-LCD droplets of four different sizes in stable equilibrium with the dilute phase and slabs of condensed FUS-LCD for tens of microseconds, and over one millisecond in aggregate. We determine surface tensions in the range of 0.01-0.4 mN/m from the fluctuations of the droplet shape and from the capillary-wave-like broadening of the interface between the two phases. From the dynamics of the protein end-to-end distance, we estimate shear viscosities from 0.001 to 0.02 Pa s for the FUS-LCD droplets with scaling factors α in the range of 0.625-0.75, where we observe liquid droplets. Significant hydration of the interior of the droplets keeps the proteins mobile and the droplets fluid.