Structural basis for ribosome protein S1 interaction with RNA in trans-translation of Mycobacterium tuberculosis

Mycobacterium tuberculosis ribosomal protein S1 (RpsA) and variants with truncated C-terminal end show absence of interaction with pyrazinoic acid

Pyrazinamide (PZA) is an antibiotic used in first- and second-line tuberculosis treatment regimens. Approximately 50% of multidrug-resistant tuberculosis and over 90% of extensively drug-resistant tuberculosis strains are also PZA resistant. Despite the key role played by PZA, its mechanisms of action are not yet fully understood. It has been postulated that pyrazinoic acid (POA), the hydrolyzed product of PZA, could inhibit trans-translation by binding to Ribosomal protein S1 (RpsA) and competing with tmRNA, the natural cofactor of RpsA. Subsequent data, however, indicate that these early findings resulted from experimental artifact. Hence, in this study we assess the capacity of POA to compete with tmRNA for RpsA. We evaluated RpsA wild type (WT), RpsA ∆A438, and RpsA ∆A438 variants with truncations towards the carboxy terminal end. Interactions were measured using Nuclear Magnetic Resonance spectroscopy (NMR), Isothermal Titration Calorimetry (ITC), Microscale Thermophoresis (MST), and Electrophoretic Mobility Shift Assay (EMSA). We found no measurable binding between POA and RpsA (WT or variants). This suggests that RpsA may not be involved in the mechanism of action of PZA in Mycobacterium tuberculosis, as previously thought. Interactions observed between tmRNA and RpsA WT, RpsA ∆A438, and each of the truncated variants of RpsA ∆A438, are reported.

Assessing the performance of MM/PBSA and MM/GBSA methods. 8. Predicting binding free energies and poses of protein-RNA complexes

Molecular docking provides a computationally efficient way to predict the atomic structural details of protein-RNA interactions (PRI), but accurate prediction of the three-dimensional structures and binding affinities for PRI is still notoriously difficult, partly due to the unreliability of the existing scoring functions for PRI. MM/PBSA and MM/GBSA are more theoretically rigorous than most scoring functions for protein-RNA docking, but their prediction performance for protein-RNA systems remains unclear. Here, we systemically evaluated the capability of MM/PBSA and MM/GBSA to predict the binding affinities and recognize the near-native binding structures for protein-RNA systems with different solvent models and interior dielectric constants (ε_in). For predicting the binding affinities, the predictions given by MM/GBSA based on the minimized structures in explicit solvent and the GB^GBn1 model with ε_in = 2 yielded the highest correlation with the experimental data. Moreover, the MM/GBSA calculations based on the minimized structures in implicit solvent and the GB^GBn1 model distinguished the near-native binding structures within the top 10 decoys for 117 out of the 148 protein-RNA systems (79.1%). This performance is better than all docking scoring functions studied here. Therefore, the MM/GBSA rescoring is an efficient way to improve the prediction capability of scoring functions for protein-RNA systems.