Coenzyme Q10 trapping in mitochondrial complex I underlies Leber's hereditary optic neuropathy.

How does a single amino acid mutation occurring in the blinding disease, Leber's hereditary optic neuropathy (LHON), impair electron shuttling in mitochondria? We investigated changes induced by the m.3460 G>A mutation in mitochondrial protein ND1 using the tools of Molecular Dynamics and Free Energy Perturbation simulations, with the goal of determining the mechanism by which this mutation affects mitochondrial function. A recent analysis suggested that the mutation's replacement of alanine A52 with a threonine perturbs the stability of a region where binding of the electron shuttling protein, Coenzyme Q10, occurs. We found two functionally opposing changes involving the role of Coenzyme Q10. The first showed that quantum electron transfer from the terminal Fe/S complex, N2, to the Coenzyme Q10 headgroup, docked in its binding pocket, is enhanced. However, this positive adjustment is overshadowed by our finding that the mobility of Coenzyme Q10 in its oxidized and reduced states, entering and exiting its binding pocket, is disrupted by the mutation in a manner that leads to conditions promoting the generation of reactive oxygen species. An increase in reactive oxygen species caused by the LHON mutation has been proposed to be responsible for this optic neuropathy.

Directed Evolution of Protoglobin Optimizes the Enzyme Electric Field.

To unravel why computational design fails in creating viable enzymes, while directed evolution (DE) succeeds, our research delves into the laboratory evolution of protoglobin. DE has adapted this protein to efficiently catalyze carbene transfer reactions. We show that the previously proposed enhanced substrate access and binding alone cannot account for increased yields during DE. The 3D electric field in the entire active site is tracked through protein dynamics, clustered using the affinity propagation algorithm, and subjected to principal component analysis. This analysis reveals notable changes in the electric field with DE, where distinct field topologies influence transition state energetics and mechanism. A chemically meaningful field component emerges and takes the lead during DE and facilitates crossing the barrier to carbene transfer. Our findings underscore intrinsic electric field dynamic's influence on enzyme function, the ability of the field to switch mechanisms within the same protein, and the crucial role of the field in enzyme design.

Dynamic competition between a ligand and transcription factor NusA governs riboswitch-mediated transcription regulation.

Cotranscriptional RNA folding is widely assumed to influence the timely control of gene expression, but our understanding remains limited. In bacteria, the fluoride (F-)-sensing riboswitch is a transcriptional control element essential to defend against toxic F- levels. Using this model riboswitch, we find that its ligand F- and essential bacterial transcription factor NusA compete to bind the cotranscriptionally folding RNA, opposing each other's modulation of downstream pausing and termination by RNA polymerase. Single-molecule fluorescence assays probing active transcription elongation complexes discover that NusA unexpectedly binds highly reversibly, frequently interrogating the complex for emerging, cotranscriptionally folding RNA duplexes. NusA thus fine-tunes the transcription rate in dependence of the ligand-responsive higher-order structure of the riboswitch. At the high NusA concentrations found intracellularly, this dynamic modulation is expected to lead to adaptive bacterial transcription regulation with fast response times.

CS-Annotate: A Tool for Using NMR Chemical Shifts to Annotate RNA Structure.

Here, we introduce CS-Annotate, a tool that uses assigned NMR chemical shifts to annotate structural features in RNA. At its core, CS-Annotate is a deployment of a multitask deep learning model that simultaneously classifies the solvent exposure, base-stacking and -pairing status, and conformation of individual RNA residues from their chemical shift fingerprint. Here, we briefly describe how we trained and tested the classifier and demonstrate its application to a model RNA system. CS-Annotate can be accessed via the SMALTR (Structure-based MAchine Learning Tools for RNA) Science Gateway (smaltr.org).