Comparison of the DNA Association Kinetics of the Lacy Repressor Tetramet, Its Dimeric Mutant LacI(Adi), and the Native Dimeric Gal Repressor

The rates of association of the tetrameric Lacy repressor (LacI), dimeric LacI(adi) (a deletion mutant of LacI), and the native dimeric Gal repressor (GalR) to DNA restriction fragments containing a single specific site were investigated using a quench-flow DNase I \u27foot-printing\u27 technique. The dimeric proteins, LacI(adi) and GalR, and tetrameric LacI possess one and two DNA binding sites, respectively. The nanomolar protein concentrations used in these studies ensured that the state of oligomerization of each protein was predominantly either dimeric or tetrameric, respectively. The bimolecular association rate constants (k(a)) determined for the LacI tetramer exceed those of the dimeric proteins. The values of k(a) obtained for LacI, LacI(adi), and GalR display different dependences on [KCl]. For LacI(adi) and GalR, they diminish as [KCl] increases from 25 mM to 200 mM, approaching rates predicted for three-dimensional diffusion. In contrast, the k(a) values determined for the tetrameric LacI remain constant up to 300 mM [KCl], the highest salt concentration that could be investigated by quench- flow footprinting. The enhanced rate of association of the tetramer relative to the dimeric proteins can be modeled by enhanced \u27sliding\u27 (Berg, O. G., Winter, R. B., and von Hippel, P. H. (1981) Biochemistry 20, 6929-6948) of the LacI tetramer relative to the LaeI(adi) dimer or a combination of enhanced sliding and the superimposition of \u27direct transfer\u27 mediated by the bidentate DNA interactions of the tetramer

Understanding the Role of Three-Dimensional Topology in Determining the Folding Intermediates of Group I Introns

Many RNA molecules exert their biological function only after folding to unique three-dimensional structures. For long, noncoding RNA molecules, the complexity of finding the native topology can be a major impediment to correct folding to the biologically active structure. An RNA molecule may fold to a near-native structure but not be able to continue to the correct structure due to a topological barrier such as crossed strands or incorrectly stacked helices. Achieving the native conformation thus requires unfolding and refolding, resulting in a long-lived intermediate. We investigate the role of topology in the folding of two phylogenetically related catalytic group I introns, the Twort and Azoarcus group I ribozymes. The kinetic models describing the Mg2+-mediated folding of these ribozymes were previously determined by time-resolved hydroxyl (⋅OH) radical footprinting. Two intermediates formed by parallel intermediates were resolved for each RNA. These data and analytical ultracentrifugation compaction analyses are used herein to constrain coarse-grained models of these folding intermediates as we investigate the role of nonnative topology in dictating the lifetime of the intermediates. Starting from an ensemble of unfolded conformations, we folded the RNA molecules by progressively adding native constraints to subdomains of the RNA defined by the ⋅OH time-progress curves to simulate folding through the different kinetic pathways. We find that nonnative topologies (arrangement of helices) occur frequently in the folding simulations despite using only native constraints to drive the reaction, and that the initial conformation, rather than the folding pathway, is the major determinant of whether the RNA adopts nonnative topology during folding. From these analyses we conclude that biases in the initial conformation likely determine the relative flux through parallel RNA folding pathways

Understanding the Role of Three-Dimensional Topology in Determining the Folding Intermediates of Group I Introns

Many RNA molecules exert their biological function only after folding to unique three-dimensional structures. For long, noncoding RNA molecules, the complexity of finding the native topology can be a major impediment to correct folding to the biologically active structure. An RNA molecule may fold to a near-native structure but not be able to continue to the correct structure due to a topological barrier such as crossed strands or incorrectly stacked helices. Achieving the native conformation thus requires unfolding and refolding, resulting in a long-lived intermediate. We investigate the role of topology in the folding of two phylogenetically related catalytic group I introns, the Twort and Azoarcus group I ribozymes. The kinetic models describing the Mg2+-mediated folding of these ribozymes were previously determined by time-resolved hydroxyl (⋅OH) radical footprinting. Two intermediates formed by parallel intermediates were resolved for each RNA. These data and analytical ultracentrifugation compaction analyses are used herein to constrain coarse-grained models of these folding intermediates as we investigate the role of nonnative topology in dictating the lifetime of the intermediates. Starting from an ensemble of unfolded conformations, we folded the RNA molecules by progressively adding native constraints to subdomains of the RNA defined by the ⋅OH time-progress curves to simulate folding through the different kinetic pathways. We find that nonnative topologies (arrangement of helices) occur frequently in the folding simulations despite using only native constraints to drive the reaction, and that the initial conformation, rather than the folding pathway, is the major determinant of whether the RNA adopts nonnative topology during folding. From these analyses we conclude that biases in the initial conformation likely determine the relative flux through parallel RNA folding pathways

ACE: A fast, skillful learned global atmospheric model for climate prediction

Existing ML-based atmospheric models are not suitable for climate prediction, which requires long-term stability and physical consistency. We present ACE (AI2 Climate Emulator), a 200M-parameter, autoregressive machine learning emulator of an existing comprehensive 100-km resolution global atmospheric model. The formulation of ACE allows evaluation of physical laws such as the conservation of mass and moisture. The emulator is stable for 100 years, nearly conserves column moisture without explicit constraints and faithfully reproduces the reference model's climate, outperforming a challenging baseline on over 90% of tracked variables. ACE requires nearly 100x less wall clock time and is 100x more energy efficient than the reference model using typically available resources. Without fine-tuning, ACE can stably generalize to a previously unseen historical sea surface temperature dataset.Comment: Accepted at Tackling Climate Change with Machine Learning: workshop at NeurIPS 202