Nucleotide addition and cleavage by RNA polymerase II: Coordination of two catalytic reactions using a single active site.

RNA polymerase II (Pol II) incorporates complementary ribonucleotides into the growing RNA chain one at a time via the nucleotide addition cycle. The nucleotide addition cycle, however, is prone to misincorporation of noncomplementary nucleotides. Thus, to ensure transcriptional fidelity, Pol II backtracks and then cleaves the misincorporated nucleotides. These two reverse reactions, nucleotide addition and cleavage, are catalyzed in the same active site of Pol II, which is different from DNA polymerases or other endonucleases. Recently, substantial progress has been made to understand how Pol II effectively performs its dual role in the same active site. Our review highlights these recent studies and provides an overall model of the catalytic mechanisms of Pol II. In particular, RNA extension follows the two-metal-ion mechanism, and several Pol II residues play important roles to facilitate the catalysis. In sharp contrast, the cleavage reaction is independent of any Pol II residues. Interestingly, Pol II relies on its residues to recognize the misincorporated nucleotides during the backtracking process, prior to cleavage. In this way, Pol II efficiently compartmentalizes its two distinct catalytic functions using the same active site. Lastly, we also discuss a new perspective on the potential third Mg2+ in the nucleotide addition and intrinsic cleavage reactions.

A comprehensive mechanism for 5-carboxylcytosine-induced transcriptional pausing revealed by Markov state models.

RNA polymerase II (Pol II) surveils the genome, pausing as it encounters DNA lesions and base modifications and initiating signals for DNA repair among other important regulatory events. Recent work suggests that Pol II pauses at 5-carboxycytosine (5caC), an epigenetic modification of cytosine, because of a specific hydrogen bond between the carboxyl group of 5caC and a specific residue in fork loop 3 of Pol II. This hydrogen bond compromises productive NTP binding and slows down elongation. Apart from this specific interaction, the carboxyl group of 5caC can potentially interact with numerous charged residues in the cleft of Pol II. However, it is not clear how other interactions between Pol II and 5caC contribute to pausing. In this study, we use Markov state models (a type of kinetic network models) built from extensive molecular dynamics simulations to comprehensively study the impact of 5caC on Pol II translocation. We describe two translocation intermediates with specific interactions that prevent the template base from loading into the Pol II active site. In addition to the previously observed state with 5caC constrained by fork loop 3, we discovered a new intermediate state with a hydrogen bond between 5caC and fork loop 2. Surprisingly, we find that 5caC may curb translocation by suppressing kinking of the helix bordering the active site (the bridge helix) because its high flexibility is critical to translocation. Our work provides new insights into how epigenetic modifications of genomic DNA can modulate Pol II translocation, inducing pauses in transcription.

Incorporation efficiency and inhibition mechanism of 2'-substituted nucleotide analogs against SARS-CoV-2 RNA-dependent RNA polymerase.

The ongoing pandemic caused by SARS-CoV-2 emphasizes the need for effective therapeutics. Inhibition of SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) by nucleotide analogs provides a promising antiviral strategy. One common group of RdRp inhibitors, 2'-modified nucleotides, are reported to exhibit different behaviors in the SARS-CoV-2 RdRp transcription assay. Three of these analogs, 2'-O-methyl UTP, Sofosbuvir, and 2'-methyl CTP, act as effective inhibitors in previous biochemical experiments, while Gemcitabine and ara-UTP show no inhibitory activity. To understand the impact of the 2'-modification on their inhibitory effects, we conducted extensive molecular dynamics simulations and relative binding free energy calculations using the free energy perturbation method on SARS-CoV-2 replication-transcription complex (RTC) with these five nucleotide analogs. Our results reveal that the five nucleotide analogs display comparable binding affinities to SARS-CoV-2 RdRp and they can all be added to the nascent RNA chain. Moreover, we examine how the incorporation of these nucleotide triphosphate (NTP) analogs will impact the addition of the next nucleotide. Our results indicate that 2'-O-methyl UTP can weaken the binding of the subsequent NTP and consequently lead to partial chain termination. Additionally, Sofosbuvir and 2'-methyl CTP can cause immediate termination due to the strong steric hindrance introduced by their bulky 2'-methyl groups. In contrast, nucleotide analogs with smaller substitutions, such as the fluorine atoms and the ara-hydroxyl group in Gemcitabine and ara-UTP, have a marginal impact on the polymerization process. Our findings are consistent with experimental observations, and more importantly, shed light on the detailed molecular mechanism of SARS-CoV-2 RdRp inhibition by 2'-substituted nucleotide analogs, and may facilitate the rational design of antiviral agents to inhibit SARS-CoV-2 RdRp.

Submillisecond Atomistic Molecular Dynamics Simulations Reveal Hydrogen Bond-Driven Diffusion of a Guest Peptide in Protein-RNA Condensate.

Liquid-liquid phase separation mediated by proteins and/or nucleic acids is believed to underlie the formation of many distinct condensed phases, or membraneless organelles, within living cells. These condensates have been proposed to orchestrate a variety of important processes. Despite recent advances, the interactions that regulate the dynamics of molecules within a condensate remain poorly understood. We performed accumulated 564.7 µs all-atom molecular dynamics (MD) simulations (system size ∼200k atoms) of model condensates formed by a scaffold RNA oligomer and a scaffold peptide rich in arginine (Arg). These model condensates contained one of three possible guest peptides: the scaffold peptide itself or a variant in which six Arg residues were replaced by lysine (Lys) or asymmetric dimethyl arginine (ADMA). We found that the Arg-rich peptide can form the largest number of hydrogen bonds and bind the strongest to the scaffold RNA in the condensate, relative to the Lys- and ADMA-rich peptides. Our MD simulations also showed that the Arg-rich peptide diffused more slowly in the condensate relative to the other two guest peptides, which is consistent with a recent fluorescence microscopy study. There was no significant increase in the number of cation-π interactions between the Arg-rich peptide and the scaffold RNA compared to the Lys-rich and ADMA-rich peptides. Our results indicate that hydrogen bonds between the peptides and the RNA backbone, rather than cation-π interactions, play a major role in regulating peptide diffusion in the condensate.

The Ion-Dipole Correction of the 3DRISM Solvation Model to Accurately Compute Water Distributions around Negatively Charged Biomolecules.

The 3D reference interaction site model (3DRISM) provides an efficient grid-based solvation model to compute the structural and thermodynamic properties of biomolecules in aqueous solutions. However, it remains challenging for existing 3DRISM methods to correctly predict water distributions around negatively charged solute molecules. In this paper, we first show that this challenge is mainly due to the orientation of water molecules in the first solvation shell of the negatively charged solute molecules. To properly consider this orientational preference, position-dependent two-body intramolecular correlations of solvent need to be included in the 3DRISM theory, but direct evaluations of these position-dependent two-body intramolecular correlations remain numerically intractable. To address this challenge, we introduce the Ion-Dipole Correction (IDC) to the 3DRISM theory, in which we incorporate the orientation preference of water molecules via an additional solute-solvent interaction term (i.e., the ion-dipole interaction) while keeping the formulism of the 3DRISM equation unchanged. We prove that this newly introduced IDC term is equivalent to an effective direct correlation function which can effectively consider the orientation effect that arises from position dependent two-body correlations. We first quantitatively validate our 3DRISM-IDC theory combined with the PSE3 closure on Cl-, [ClO]- (a two-site anion), and [NO2]- (a three-site anion). For all three anions, we show that our 3DRISM-IDC theory significantly outperforms the 3DRISM theory in accurately predicting the solvation structures in comparison to MD simulations, including RDFs and 3D water distributions. Furthermore, we have also demonstrated that the 3DRISM-IDC can improve the accuracy of hydration free-energy calculation for Cl-. We further demonstrate that our 3DRISM-IDC theory yields significant improvements over the 3DRISM theory when applied to compute the solvation structures for various negatively charged solute molecules, including adenosine triphosphate (ATP), a short peptide containing 19 residues, a DNA hairpin containing 24 nucleotides, and a riboswitch RNA molecule with 77 nucleotides. We expect that our 3DRISM-IDC-PSE3 solvation model holds great promise to be widely applied to study solvation properties for nucleic acids and other biomolecules containing negatively charged functional groups.

Temperature-dependent kinetic pathways of heterogeneous ice nucleation competing between classical and non-classical nucleation.

Ice nucleation on the surface plays a vital role in diverse areas, ranging from physics and cryobiology to atmospheric science. Compared to ice nucleation in the bulk, the water-surface interactions present in heterogeneous ice nucleation complicate the nucleation process, making heterogeneous ice nucleation less comprehended, especially the relationship between the kinetics and the structures of the critical ice nucleus. Here we combine Markov State Models and transition path theory to elucidate the ensemble pathways of heterogeneous ice nucleation. Our Markov State Models reveal that the classical one-step and non-classical two-step nucleation pathways can surprisingly co-exist with comparable fluxes at T = 230 K. Interestingly, we find that the disordered mixing of rhombic and hexagonal ice leads to a favorable configurational entropy that stabilizes the critical nucleus, facilitating the non-classical pathway. In contrast, the favorable energetics promotes the formation of hexagonal ice, resulting in the classical pathway. Furthermore, we discover that, at elevated temperatures, the nucleation process prefers to proceed via the classical pathway, as opposed to the non-classical pathway, since the potential energy contributions override the configurational entropy compensation. This study provides insights into the mechanisms of heterogeneous ice nucleation and sheds light on the rational designs to control crystallization processes.

Markov State Models to Study the Functional Dynamics of Proteins in the Wake of Machine Learning.

Markov state models (MSMs) based on molecular dynamics (MD) simulations are routinely employed to study protein folding, however, their application to functional conformational changes of biomolecules is still limited. In the past few years, the field of computational chemistry has experienced a surge of advancements stemming from machine learning algorithms, and MSMs have not been left out. Unlike global processes, such as protein folding, the application of MSMs to functional conformational changes is challenging because they mostly consist of localized structural transitions. Therefore, it is critical to properly select a subset of structural features that can describe the slowest dynamics of these functional conformational changes. To address this challenge, we recommend several automatic feature selection methods such as Spectral-OASIS. To identify states in MSMs, the chosen features can be subject to dimensionality reduction methods such as TICA or deep learning based VAMPNets to project MD conformations onto a few collective variables for subsequent clustering. Another challenge for the application of MSMs to the study of functional conformational changes is the ability to comprehend their biophysical mechanisms, as MSMs built for these processes often require a large number of states. We recommend the recently developed quasi-MSMs (qMSMs) to address this issue. Compared to MSMs, qMSMs encode the non-Markovian dynamics via the generalized master equation and can significantly reduce the number of states. As a result, qMSMs can be built with a handful of states to facilitate the interpretation of functional conformational changes. In the wake of machine learning, we believe that the rapid advancement in the MSM methodology will lead to their wider application in studying functional conformational changes of biomolecules.