Targeted Covalent Inhibitors Allosterically Deactivate the DEDDh Lassa Fever Virus NP Exonuclease from Alternative Distal Sites

Trajectory Statistical Learning of the Potential Mean of Force and Diffusion Coefficient from Molecular Dynamics Simulations

Central to studying the conformational changes of a complex protein is understanding the dynamics and energetics involved. Phenomenologically, structural dynamics can be formulated using an overdamped Langevin model along an observable, e.g., the distance between two residues in the protein. The Langevin model is specified by the deterministic force (the potential of mean force, PMF) and stochastic force (characterized by the diffusion coefficient, D). It is therefore of great interest to be able to extract both PMF and D from an observable time series but under the same computational framework. Here, we approach this challenge in molecular dynamics (MD) simulations by treating it as a missing-data Bayesian estimation problem. An important distinction in our methodology is that the entire MD trajectory, as opposed to the individual data elements, is used as the statistical variable in Bayesian imputation. This idea is implemented through an eigen-decomposition procedure for a time-symmetrized Fokker-Planck equation, followed by maximizing the likelihood for parameter estimation. The mathematical expressions for the functional derivatives used in learning PMF and D also provide new physical insights for the manner by which the information on both the deterministic and stochastic forces is encoded in the dynamics data. An all-atom MD simulation of a nontrivial biomolecule case is used to illustrate the application of this approach. We show that, interestingly, the results of trajectory statistical learning can motivate new order parameters for an improved description of the kinetic bottlenecks in conformational changes. Complementing purely data-driven or black-box methods, this work underscores the advantages of physics-based machine learning in gaining chemical insights from quantitative parameter estimation.

Nuclease Activity of the Junín Virus Nucleoprotein C-Terminal Domain

The mammarenavirus Junín (JUNV) is the causative agent of Argentine hemorrhagic fever, a severe disease of public health concern. The most abundant viral protein is the nucleoprotein (NP), a multifunctional, two-domain protein with the primary role as structural component of the viral nucleocapsids, used as template for viral polymerase RNA synthesis activities. Here, we report that the C-terminal domain (CTD) of the attenuated Candid#1 strain of the JUNV NP can be purified as a stable soluble form with a secondary structure in line with known NP structures from other mammarenaviruses. We show that the JUNV NP CTD interacts with the viral matrix protein Z in vitro, and that the full-length NP and Z interact with each other in cellulo, suggesting that the NP CTD is responsible for this interaction. This domain comprises an arrangement of four acidic residues and a histidine residue conserved in the active site of exoribonucleases belonging to the DEDDh family. We show that the JUNV NP CTD displays metal-ion-dependent nuclease activity against DNA and single- and double-stranded RNA, and that this activity is impaired by the mutation of a catalytic residue within the DEDDh motif. These results further support this activity, not previously observed in the JUNV NP, which could impact the mechanism of the cellular immune response modulation of this important pathogen.

A dsRNA-binding mutant reveals only a minor role of exonuclease activity in interferon antagonism by the arenavirus nucleoprotein

The arenavirus nucleoprotein (NP) plays an important role in the virus' ability to block interferon (IFN) production, and its exonuclease function appears to contribute to this activity. However, efforts to analyze this contribution are complicated by the functional overlap between the exonuclease active site and a neighboring region involved in IKKε-binding and subsequent inhibition of IRF3 activation, which also plays an important role in IFN production. To circumvent this issue, we mutated a residue located away from the active site that is involved in binding of the dsRNA substrate being targeted for exonuclease digestion, i.e. H426A. We found that expression of Tacaribe virus (TCRV) NP containing this RNA-binding H426A mutation was still able to efficiently block IFN-β promoter activity in response to Sendai virus infection, despite being strongly impaired in its exonuclease activity. This was in contrast to a conventional exonuclease active site mutant (E388A), which was impaired with respect to both exonuclease activity and IFN antagonism. Importantly, growth of a recombinant virus encoding the RNA-binding mutation (rTCRV-H426A) was similar to wild-type in IFN-deficient cells, unlike the active site mutant (rTCRV-E388A), which was already markedly impaired in these cells. Further, in IFN-competent cells, the TCRV-H426A RNA-binding mutant showed more robust growth and delayed IFN-β mRNA upregulation compared to the TCRV-E388A active site mutant. Taken together, this novel mutational approach, which allows us to now dissect the different contributions of the NP exonuclease activity and IKKε-binding/IRF3 inhibition to IFN antagonism, clearly suggests that conventional exonuclease mutants targeting the active site overestimate the contribution of the exonuclease function, and that rather other IFN antagonistic functions of NP play the dominant role in IFN-antagonism.