Tracing Allostery in the Spliceosome Ski2-like RNA Helicase Brr2.

RNA ATPases/helicases remodel substrate RNA-protein complexes in distinct ways. The different RNA ATPases/helicases, taking part in the spliceosome complex, reshape the RNA/RNA-protein contacts to enable premature-mRNA splicing. Among them, the bad response to refrigeration 2 (Brr2) helicase promotes U4/U6 small nuclear (sn)RNA unwinding via ATP-driven translocation of the U4 snRNA strand, thus playing a pivotal role during the activation, catalytic, and disassembly phases of splicing. The plastic Brr2 architecture consists of an enzymatically active N-terminal cassette (N-cassette) and a structurally similar but inactive C-terminal cassette (C-cassette). The C-cassette, along with other allosteric effectors and regulators, tightly and timely controls Brr2's function via an elusive mechanism. Here, microsecond-long molecular dynamics simulations, dynamical network theory, and community network analysis are combined to elucidate how allosteric effectors/regulators modulate the Brr2 function. We unexpectedly reveal that U4 snRNA itself acts as an allosteric regulator, amplifying the cross-talk of distal Brr2 domains and triggering a conformational reorganization of the protein. Our findings offer fundamental understanding into Brr2's mechanism of action and broaden our knowledge on the sophisticated regulatory mechanisms by which spliceosome ATPases/helicases control gene expression. This includes their allosteric regulation exerted by client RNA strands, a mechanism that may be broadly applicable to other RNA-dependent ATPases/helicases.

Are Neural Network Potentials Trained on Liquid States Transferable to Crystal Nucleation? A Test on Ice Nucleation in the mW Water Model.

Neural network potentials (NNPs) are increasingly being used to study processes that happen on long time scales. A typical example is crystal nucleation, which rate is controlled by the occurrence of a rare fluctuation, i.e., the appearance of the critical nucleus. Because the properties of this nucleus are far from those of the bulk crystal, it is yet unclear whether NN potentials trained on equilibrium liquid states can accurately describe nucleation processes. So far, nucleation studies on NNPs have been limited to ab initio models whose nucleation properties are unknown, preventing an accurate comparison. Here we train a NN potential on the mW model of water─a classical three-body potential whose nucleation time scale is accessible in standard simulations. We show that a NNP trained only on a small number of liquid state points can reproduce with great accuracy the nucleation rates and free energy barriers of the original model, computed from both spontaneous and biased trajectories, strongly supporting the use of NNPs to study nucleation events.

A neural network potential with self-trained atomic fingerprints: A test with the mW water potential.

We present a neural network (NN) potential based on a new set of atomic fingerprints built upon two- and three-body contributions that probe distances and local orientational order, respectively. Compared with the existing NN potentials, the atomic fingerprints depend on a small set of tunable parameters that are trained together with the NN weights. In addition to simplifying the selection of the atomic fingerprints, this strategy can also considerably increase the overall accuracy of the network representation. To tackle the simultaneous training of the atomic fingerprint parameters and NN weights, we adopt an annealing protocol that progressively cycles the learning rate, significantly improving the accuracy of the NN potential. We test the performance of the network potential against the mW model of water, which is a classical three-body potential that well captures the anomalies of the liquid phase. Trained on just three state points, the NN potential is able to reproduce the mW model in a very wide range of densities and temperatures, from negative pressures to several GPa, capturing the transition from an open random tetrahedral network to a dense interpenetrated network. The NN potential also reproduces very well properties for which it was not explicitly trained, such as dynamical properties and the structure of the stable crystalline phases of mW.