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The successful recent application of machine learning methods to scientific problems includes the learning of flexible and accurate atomic-level force-fields for materials and biomolecules from quantum chemical data. In parallel, the machine learning of force-fields at coarser resolutions is rapidly gaining relevance as an efficient way to represent the higherbody interactions needed in coarse-grained force-fields to compensate for the omitted degrees of freedom. Coarsegrained models are important for the study of systems at time and length scales exceeding those of atomistic simulations. However, the development of transferable coarse-grained models via machine learning still presents significant challenges. Here, we discuss recent developments in this field and current efforts to address the remaining challenges. 

Site-directed spin labeling (SDSL) of large RNAs for electron paramagnetic resonance (EPR) spectroscopy has remained challenging to date. We here demonstrate an efficient and generally applicable posttranscriptional SDSL method for large RNAs using an expanded genetic alphabet containing the NaM-TPT3 unnatural base pair (UBP). An alkyne-modified TPT3 ribonucleotide triphosphate (rTPT3 CO TP) is synthesized and site-specifically incorporated into large RNAs by in vitro transcription, which allows attachment of the azide-containing nitroxide through click chemistry. We validate this strategy by SDSL of a 419-nucleotide ribonuclease P (RNase P) RNA from Bacillus stearothermophilus under non-denaturing conditions. The effects of site-directed UBP incorporation and subsequent spin labeling on the global structure and function of RNase P are marginal as evaluated by Circular Dichroism spectroscopy, Small Angle X-ray Scattering, Sedimentation Velocity Analytical Ultracentrifugation and enzymatic assay. Continuous-Wave EPR analyses reveal that the labeling reaction is efficient and specific, and Pulsed Electron–Electron Double Resonance measurements yield an inter-spin distance distribution that agrees with the crystal structure. The labeling strategy as presented overcomes the size constraint of RNA labeling, opening new avenues of spin labeling and EPR spectroscopy for investigating the structure and dynamics of large RNAs.